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Ice induction in DSC experiments with Snomax®
Accepted Manuscript Title: Ice induction in DSC experiments with Snomax® Authors: Hugo Desnos, Anne Baudot, Magda Teixeira, G´erard Louis, Loris Commin, Samuel Buff, Pierre Bruy`ere PII: DOI: Reference:
S0040-6031(18)30593-8 https://doi.org/10.1016/j.tca.2018.07.022 TCA 78055
To appear in:
Thermochimica Acta
Received date: Revised date: Accepted date:
15-11-2017 27-7-2018 29-7-2018
Please cite this article as: Desnos H, Baudot A, Teixeira M, Louis G, Commin L, Buff S, Bruy`ere P, Ice induction in DSC experiments with Snomax®, Thermochimica Acta (2018), https://doi.org/10.1016/j.tca.2018.07.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Ice induction in DSC experiments with Snomax® Hugo Desnosa,b,*, Anne Baudotb,c, Magda Teixeiraa, Gérard Louisb,c, Loris Commina, Samuel Buffa, Pierre Bruyèrea a: Université de Lyon, VetAgro Sup, UPSP ICE (2011-03-101) & CRB-ANIM (ANR11-INBS-0003), Marcy L’Etoile, France b: Université Sorbonne Paris-Cité, Université Paris Descartes, Paris, France c: INSERM U1148, Paris, France *: [email protected]
HighlightsSnomax allows to control the nucleation temperature during DSC experiment. Several nucleation temperature groups are achievable. A key factor is the presence probabilities of the Pseudomonas. syringae INA classes.
Solutions’ thermodynamic characteristics are not affected at Snomax concentration below 103 mg L-1. Key-Words: DSC; Ice Nucleation; INA; Snomax®; Crystallization; Self-triggered
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Abstract
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The Snomax concentration and the sample volume determine this factor.
For differential scanning calorimetry (DSC) analysis, controlling the degree of supercooling at which crystallization begins may be required for several studies, experiments, and applications. In this paper, the use of Snomax, an ice nucleating agent (INA), was evaluated to create ice at a desirable
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temperature range in a DSC aluminum sample pan. The effect of Snomax on the nucleation
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temperature (Tn) was studied in pure water. Best practices and methods are described in terms of the Tn dependence on three experimental parameters: (i) the Snomax concentration that controls the Tn
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value for different groups according to the three classes of the Pseudomonas syringae protein
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aggregates (from which Snomax originates); (ii) the sample volume that affects the presence probabilities of the different INA subpopulations in the solution and that could also favor their
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deterioration; and (iii) the cooling rate that does not seem to further affect the Tn value. There is noevidence of time dependence of the nucleation process promoted by Snomax. The presence of artifacts or disturbances introduced by the addition of Snomax into the solution was evaluated. No
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major disturbances of the thermodynamic characteristics of these solutions were observed with the addition of Snomax below 103 mg L-1 concentration. This underscores the possible use of Snomax for
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controlling ice nucleation during DSC experiments.
1. Introduction
Differential scanning calorimetry (DSC) studies enable thermodynamic characterization of aqueous
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solutions by the analysis of the phase transitions of water molecules. For example, in many applications or theoretical developments, it is important to be able to follow the appearance of ice in aqueous solutions[1–6]. This technology provides essential information on the solution properties (such as glass transition temperature, equilibrium temperature, and maximal amount of ice) and on the phase change process (such as kinetics, amplitude, and specific heat changes). Nevertheless, the crystallization processes do not begin at the equilibrium temperature between the liquid solution and the crystalline phases. The system can exist in a supercooling state below the phase
equilibrium temperature. The initiation of the appearance of ice, which is called the nucleation process, must occur to break the metastable state and to start crystallization. After nucleation, the crystallization process occurs as a result of a dendritic ice front growth[7–9]. The speed, mode, and driving force of this process are defined by several parameters, including the extent of the supercooling state[10–14]. Therefore, the temperature of the first appearance of ice crystals in the solution, known as the nucleation temperature (Tn), is an important factor for some experiments. In DSC experiments especially, precise, repeatable, and self-triggered control of the appearance of ice may be required.
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For example, in cell cryopreservation, nucleation control is imperative during most slow-freezing protocols[15–17]. Consequently, control of this step may be required for experiments with procedural
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developments or efficiency evaluation of cryopreservation solutions.
Nucleation is a local phenomenon during which some molecules arrange themselves into a molecular conformation close to the crystalline phase (an ice-like structure)[18–21]. This germ of molecules, organized in a minimizing Gibbs free energy conformation (in comparison to the surrounding
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molecules)[22], catalyzes the transfer to the crystalline phase. The study of the nucleation
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phenomenon for pure water, is an expanding branch of science whose theoretical explanations are still late to predict all observable phenomena. Two types of nucleation exist: homogeneous and
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heterogeneous. The classical nucleation theory[23,24] assumes that it is possible for a certain quantity
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of water molecules to be pre-organized in a stochastic manner into an ice-like structure and thus, be able to initiate the nucleation phenomenon. According to this theory, in the absence of
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defects/heterogeneities in the spatial water molecule distribution, the quantity of water molecules cannot exceed the number of molecules present within a water cluster. This kind of germ, stemming from the molecules of the solution, corresponds to homogeneous nucleation. The homogeneous
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temperature of a pure water solution is assumed to occur in the range of –35 to –38 °C, depending on the cooling rate and volume[25]. This type of supercooling magnitude can only be achieved in special
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cases that are difficult to obtain as they imply an absence of external perturbation. Consequently, the nucleation of water (even pure water) often starts from the heterogeneous nucleation phenomenon. This type of nucleation involves a spatial heterogeneous distribution of water molecules, which favors the appearance of an ice germ larger than water cluster size. These heterogeneities can have different
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origins and can be introduced by impurities or organizational disturbances within the system[18]. Not all impurities favor ice nucleation at high temperatures, but there are some compounds whose structural or surface properties are particularly prone to nucleation. Such compounds are called ice nucleating agents (INA). The best INA is an ice crystal itself, which can theoretically initiate crystallization from the equilibrium temperature between the crystalline and the liquid phase (0 °C for pure water). In addition, some INAs can initiate the ice nucleation process from a supercooling state
of only a few degrees[21,26,27]. There are several types of INAs, and the two major types are bacterial and mineral INAs. Some microorganisms, such as Pseudomonas syringae, synthesize proteins on extracellular aggregates, which can initiate the ice nucleation process inside aqueous solutions[26,28]. The nucleation ability of these kinds of extracellular aggregates has been widely studied[26,29–34]. In nature, these bacteria are implicated as the source of frost at relatively high temperatures[35–37]. A freeze-dried powder containing P. syringae cells and sterilized by gamma irradiation is currently marketed by the York
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Snow® Company[38]. This product, Snomax, is used and sold to winter sports resorts to make artificial snow. It is thus a potential INA. Other potential INAs have also been proposed (AgI, SIO2, ATD,
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IceStart®)[19,20,27,39–41].
The nucleating effect of P. syringae on ice appearance has been previously described[32], and the effect of the Snomax concentration on the Tn value was first reported by Maki (1974)[31] and then by other teams[42–47], including studies with DSC[27,48–51]. Snomax is a particularly efficient INA.
for
example,
in
cryobiology
studies[15,57–60]
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solutions[34,44,52–56],
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Hence, Snomax or P. syringae is widely used to control ice crystal appearance in aqueous with
optical
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experiments[5,6,61–64] or in DSC experiments[4,6,65–72]. However, all these studies were conducted under different experimental conditions, thus preventing comparisons among them. In particular, the
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studies were not conducted on bulk samples but often on emulsions. The present study aimed to investigate a tool that controls ice nucleation to optimize DSC experiments
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with bulk samples. We evaluated the Snomax INA activity in DSC experiments to induce ice nucleation at a selected temperature range to define how to use the tool relevantly. In this study, we focused on
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the use of the Snomax compound inside a DSC aluminum pan with bulk samples of pure water. Two aspects were evaluated. We first studied the impact on Tn of namely, the Snomax concentration
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(SmC), the sample volume (SV), and the cooling rate (CR). As Snomax is an INA that interacts with surrounding water molecules and because it replaces some of the water molecules present in a solution, the system evolves from a pure compound description to a mixture. Consequently, perturbations of the thermodynamic responses could be linked to the addition of Snomax. Thus, as
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Snomax is supposed to be used inside solutions during the thermodynamic characterization of these solutions, we evaluated the effects of the addition of Snomax on the solution’s thermodynamic properties.
2. Materials and Methods 2.1. Materials
DSC (Diamond, Perkin-Elmer; Pyris 11.1.1) with Cryofill (liquid nitrogen cooling system) was used to obtain thermal, kinetic, and calorimetric information in particular on ice crystallization and melting in aqueous mixtures during cooling and warming. The atmospheric pressure was not controlled during the experiment (estimated around 1000±50 hPa according to [73]). Sample was loaded into a sealable standard aluminum sample pan (Perkin Elmer 0219-0062 designed for volatile samples) at room temperature. The evolution of pressure inside the sample pan as a function of temperature is unknown and uncontrolled.
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Calibration: The temperature range of the DSC was carefully calibrated at +2.5 °C min-1 with the structural conformation transition (c/c) temperature of cyclohexane (–87.06 °C)[74] and the onset of
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the melting transition temperature of pure bi-osmosed water (0.00 °C)[75]. Heat flow was calibrated, at +2.5 °C min-1 (for a normal data range of 320 mW) with the heat of c/c transition of cyclohexane
(79.58 J g-1)[74] and the heat of the melting of pure bi-osmosed water (333.446 J g-1)[75]. The empty oven baseline of the DSC, which was systematically subtracted from the sample thermograms, was
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regularly recorded with care. The validity of the calibration was verified regularly. DSC instrument
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energy (previously verified in our instrument[76,77]).
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uncertainties specified by Perkin Elmer are: u(p) = ±0.11 °C for temperature and ur(p) = 0.11 J g-1 for
Sample Table
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Chemical Name H2O Snomax®
Source
Laboratory bi-osmosis purification, Purelab Prima, Elga
York Snow Company, Snowmakers AG, Steffisburg, Switzerland
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Sample preparation: Solutions were freshly made in mass per volume percentage (% m v-1) and preserved for less than 2 weeks at +4°C. Before testing, the solutions were warmed to room temperature for 30 minutes. The weighing of one or two grains of the Snomax powder was conducted
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with a high sensibility scale balance (XS105 DualRange; Metler Toledo; resolution: 10-5 g). The highest concentration solution was made first; all other concentrations were then obtained with a 10-time
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dilution pipetting (100 µL of the solution with Snomax in 900 µL of pure bi-osmosed water). The solutions were mixed through a series of capsizing (about 20) before pipetting. For DSC experiments, 1 to 15 µL of each medium was loaded into a sealable standard aluminum sample pan at room temperature. These sample pans were previously cleaned using a protocol provided by Perkin Elmer
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(with xylene and acetone). Sample weighing was conducted using the high sensibility scale balance. To ensure the sealed pans’ integrity, weights were measured at the end of the experiments and compared with the weights obtained before the DSC measurements. Each medium was tested three times, and for each test, a new sample pan was prepared. 2.2. Methods
Analysis of solutions: The properties of several solutions were determined using thermograms and Pyris software tools. Tn: The nucleation temperature (Tn) of a solution is the temperature at which the phase change from liquid to solid is initiated. For heterogeneous nucleation, this temperature is not a thermodynamic property and is only linked to the most efficient ice-germ inducer in the solution. In a DSC record, this temperature is revealed by the onset of the crystallization peak. Tonset: For a pure compound during warming at ambient pressure, one temperature exists at which the
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crystalline phase is no longer the minimizing Gibbs free energy conformation. From this temperature,
the thermal agitation can break some bonds between molecules, and the crystalline phase starts to melt. This temperature is the specific melting temperature of the compound at ambient pressure. It is
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a thermodynamic property, revealed in DSC experiments by the beginning/onset of the melting peaks
(Tonset). To calculate this temperature, two tangent lines are drawn from the beginning of melting peaks.
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Tm: During a mixture’s melting process, the maximum melting temperature (Tm) gives valuable information about the properties of the solution. At this temperature, we consider that the last ice
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crystal disappears from the solution. Thus, during cooling, it is assumed that a mixture liquid solution
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evolves into the supercooling state below the Tm. During a DSC analysis, this value is raised to the top
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of the melting peak.
ΔHc and ΔHm: To precisely measure the amount of ice that has crystallized during cooling or that has melted during thawing, the areas of peaks obtained on DSC thermograms during phase transitions are
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evaluated. The peak area is correlated with the amount of energy exchanged during the thermal event in solution (exothermic or endothermic). This allows calculation of enthalpy (ΔH) of the corresponding
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phase transition as it corresponds to the amount of energy, per gram of sample, which is released or absorbed during the phase transition. Consequently, in an aqueous solution, this area is correlated
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with the amount of water involved in the phase transition. ΔH can be evaluated during the crystallization process (crystallization enthalpy: ΔHc) or the melting process (melting enthalpy: ΔHm). The areas were calculated using a sigmoid curve- baseline.
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Protocols: Two types of protocols were used during the experiments. Protocol 1: From +10 °C to –40 °C at “CR” °C min-1 From –40 °C to +20 °C at +10 °C min-1 Protocol 2: From +10 °C to -150 °C at –100 °C min-1 From –150 °C to +10 °C at +2.5 °C min-1 From +10 °C to –150 °C at –2.5 °C min-1 From –150 °C to +20 °C at +20 °C min-1
Snomax control of ice nucleation: Protocol 1 rapidly allows the characterization of Tn for three experimental parameters: SmC, SV, and CR.
Effect of the SmC on the Tn: 11 SmC (from 10-6 to
104 mg L-1) were tested with a CR of 2.5 °C min-1 and an SV of 5 µL.
Effect of the SV on the Tn: 5 SV (1, 3, 5, 10, and 15 µL) were tested with a CR of 2.5 °C min-1 and 4 SmC (10-2, 1, 10, and 103 mg L-1).
Effect of the CR on the Tn: 7 CR (0.5, 1.0, 2.5, 5.0, 10, 20, and 40 °C min-1) were tested with an SV of 10 µL and 4 SmC (10-2, 1, 10, and 103 mg L-1).
Snomax impact on solution properties: All the results obtained with protocol 1 during the study of the SmC impact on Tn were used to study Tm, Tonset, ΔHc, and ΔHm. Complementary studies were conducted
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with protocol 2 previously described by our team to characterize cryopreservation solutions[76,77]. This time-consuming protocol was used to compare ΔHc and ΔHm more precisely, with a slow cooling or warming rate (CR and WR equal to 2.5 °C min-1) for four SmC (10-2, 1, 10, 103 mg L-1) and SV of 3 µL
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or 10 µL. To obtain the latent heat of solidification of supercooled water (Lf) at the measured phase
change temperature (we considered this temperature to be Tn), we interpolated Boutron’s[2] data. He calculated the temperature dependence of Lf from Angell’s[78] measurements of the specific heat capacity (cp) of supercooled water at different temperatures and the corresponding values of ice given
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in HandBook[79].
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3. Results & Discussion
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To evaluate ice nucleation with Snomax during DSC experiments, we studied two aspects: the Snomax
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nucleation activity and the Snomax effect on solutions’ properties. Figure 1, which shows thermograms
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with crystallization and melting processes, gives an overview of characterization of this compound.
Figure 1: SmC influence on DSC crystallization and melting peaks during cooling and thawing, respectively, after dilution in pure water (encapsulated in a DSC sample pan). Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102;
103; 104 mg L-1. CR: –2.5 °C min-1. WR: +10 °C min-1. The term “Endo Up” for heat flow representation implies that a positive exchange (during an endothermic reaction) is represented at the top and a negative exchange at the bottom (exothermic). All freezing thermograms were aligned from –1.5 to +0.5 °C and then stacked on the same line (–2 mW). All thawing thermograms were aligned from –30 to –10 °C and then stacked on the same line (+2 mW). Some thermograms were omitted for easier reading.
3.1. Snomax control of ice nucleation Nucleation in pure water is a stochastic process during a DSC experiment and is influenced by heterogeneities induced by experimental conditions. These heterogeneities can be created by
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impurities, edges, or perturbations naturally present in a solution within the DSC sample pan. In
consequence, in a sample pan during cooling at –2.5 °C min-1, 5 µL of pure water sample solution
usually crystallizes at a temperature range between –24 and –17 °C. Crystallization occurs from the
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heterogeneous nucleation within that temperature range. 3.1.1. Snomax concentration influence on Tn value
The DSC characterization presented in Figures 1 and 2 demonstrates a large range of crystallization
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peak distribution on the temperature scale. Peak shapes and sizes are not identical. According to SmC, four distinct groups of Tn can be defined. Between –24 and –17 °C, there is a group (the Tn-group 4)
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composed of pure water and solutions with very low SmC (less than or equal to 10-4 mg L-1). At –13.6 °C,
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there is a single Tn data with a high dilution of Snomax (10-3 mg L-1). Between –8.7 and –2.0 °C, there
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are three groups composed of all the solutions with the other SmC (from 10-3 mg L-1): one between – 8.7 and –7.0 °C with SmC of 10-3 to 10-1 mg L-1 (the Tn-group 3); one between –6.0 and –5.0 °C with SmC
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of 10-1 mg L-1 (the Tn-group 2); and one between –4.0 and –2.0 °C with SmC of 1 to 104 mg L-1 (the Tngroup 1). The logarithmic scale in Figure 2 emphasizes the influence of the SmC on the Tn for a pure water solution. From average values, two horizontal asymptotes can be observed: the first one for SmC
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less than or equal to 10-4 mg L-1, with noise variations of 1 or 2 °C around –20 °C; and the second one for SmC greater than or equal to 1 mg L-1, around –3 °C. For the other SmC, only a small proportion of
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Snomax (10-3 mg L-1) induces an increase in the Tn as compared to pure water. A larger amount of Snomax added to water reduces slightly more the possibility of achieving a large supercooling. As Snomax contains INAs, this result is partly a direct consequence of the stochastic nature of nucleation. The increase in the concentration of impurities increases the chance to favor the nucleation and thus
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increases Tn. The heterogeneous nucleation temperature (Tn) is determined by the presence of INA particles in the solution, which will cause the ice germ to become critical at the highest temperature. According to the CNT, the abilities of an INA to trigger nucleation do not depend on the presence of other INAs in solution[80]. When the INA germ becomes critical, the nucleation phenomenon is triggered, and the crystallization starts from this point[81].
To date, three INA subpopulations from P. syringae have been reported and are linked to three specific INA activities and three specific presence probabilities[21,26,29,30,33,82,83]. Each subpopulation corresponds to different sizes of protein aggregates consisting of the repetition of a 150 kDA protein[26,30,32,84,85]. In an aggregate, nucleating proteins act cooperatively to nucleate supercooled water. According to the classical nucleation theory, the largest INA surface will preorganize the largest amount of water molecules on an ice-like structure (in its surroundings) and will be the most efficient germ in the solution[86]. Thus, the larger an aggregate of an ice nucleating
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protein, the more it triggers nucleation at high temperature[26,33]. Two different subpopulation
classifications have been proposed. Yankofsky et al.[82] proposed one classification of the subpopulations, named as type I, II, and III, with ice-nucleating temperature in the range of –4 to –2 °C,
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–7 to –5 °C, and –10 to –7 °C, respectively. Turner et al.[83] proposed another subpopulation
classification as follows: class A, active above –4.8 °C; class B, between –5.7 and –4.8 °C; and class C, below –7.6 °C. INA protein type I, or class A, from P. syringae, is considered to be one of the most
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A
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active INA known after an ice crystal[32,87].
Figure 2: SmC influence on Tn during cooling. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1 (logarithmic scale representation). CR: –2.5 °C min-1. Circles represent average (n = 3) data. At a given concentration, each cross represents one of the 3 data. The bold blue line represents the average temperature for pure water samples and the dotted lines show its standard deviation.
As Snomax is derived from P. syringae, these different subpopulations could influence the ability of Snomax to nucleate ice. Because the nucleation event starts from the most efficient ice germ inducer in the solution, the temperature at which an aqueous solution with Snomax will nucleate depends on the presence of INA subpopulations of P. syringae, which depends on the Snomax quantity. The presence probability of the third INA subpopulation is higher than that of the second, which is itself higher than that of the first. Assuming a link between the different Tn-groups and the different INA classes, it is possible to explain the Tn step-variation with SmC. In consequence, we can assume that Tn
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depends on the presence probability of the different INA subpopulations, which is linked to the SmC.
Increasing the SmC increases the chance to have a most efficient INA in solution and increases the T n value. The presence of the different subpopulations with their own presence probabilities could
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consequently cause the step variation of Tn with SmC.
Snomax has been recently proposed as a biological ice nucleant test substance[43] to compare ice nucleation measurement techniques. In the present study and other studies[45,88], the reproducibility
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of the ice nucleation activity of the Snomax was proven for specific experimental conditions. We can
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then preliminary assume the stability of the presence probabilities of each INA subpopulation.
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In our experimental conditions, as mentioned in Table 1, four distinct Tn-groups were achieved using different SmCs. In Tn-group 4, the Tn values obtained for the low SmC are equivalent to those obtained
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without Snomax. We therefore conclude that there is a maximum Snomax dilution below which, no influence on the nucleation effect can be noticed. We can thus expect that there is only a minor chance
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of active INA traces of P. syringae. The probability of creating a germ, whose activity is superior to that of a bi-osmosed pure water solution encapsulated in a DSC sample pan is negligible. In Tn-group 3, we
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assume that the nucleation is triggered by the third INA subpopulation (III or C). In Tn-group 2, we assume that the nucleation is triggered by the second INA subpopulation (II or B). This group is small and contains only two values of Tn. Finally, nucleation in the last Tn-group (named Tn-group 1) is
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supposedly triggered by the first INA subpopulation (I or A). The repartition of all these Tn-groups can be observed in Figures 1 and 2. For the concentration of 10-3 mg L-1, there are two kinds of Tn values: an isolated value localized at –
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13.6 °C and two values around –7.5 °C in Tn-group 3. This isolated value is far from the temperature range of the third INA subpopulation. Three hypotheses could explain this value. First, a study[26] proposed that the ice nucleation activity of a single INA protein could trigger ice nucleation between –13 and –12°C, which is close to our value. Second, residual materials or impurities in the Snomax powder (other than protein compounds from the active INA protein) or dust could induce this nucleation. These residual materials or impurities could be more efficient as INA than those usually
present on the DSC sample pan. Third, the stochastic aspect of the nucleation classically involved in sample pan, for a pure water sample does not exclude the occurrence of this type of Tn value. For the concentration of 10-2 mg L-1, all values were obtained in Tn-group 3, which indicates that the probability of obtaining the first or second INA subpopulation is low as opposed to that of obtaining the third INA subpopulation, which is high. For the concentration of 10-1 mg L-1, two Tn values were obtained in Tn-group 2, and the other was part
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of Tn-group 3. Consequently, the probability of obtaining the second INA subpopulation is quite high but not exclusive in our experimental conditions, and the probability of obtaining the first INA
subpopulation seems negligible. Tn-group 2 corresponds to a quite small interval of temperature, and
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the Tn occurrence from this group is low. As Snomax is a commercial product, this result could be due to its production method. The bacterial strains of P. syringae selected and cultivated for the production of Snomax may have been designed to achieve a high probability of the presence of the first INA subpopulation. Consequently, the probabilities of the presence of the first and the second INA
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subpopulation could be similar. In addition, the range of SmC for which the probability of the presence
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of the second INA subpopulation is large, and the probability of the presence of the first INA
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subpopulation is low, is probably small. The evaluation of this range could be the objective of a specific study, especially by following the nucleation rate of a solution containing these INAs, as described by
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Vali [81]. It was shown that this kind of studies can provide information on the INA properties and can prove the presence and efficiency of different INA subpopulations[43]. However, we were not able to
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achieve this kind of results with our experimental device, and it would be difficult to make comparisons with the present data.
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Finally, we considered that the probability of obtaining the first INA subpopulation in the solution is quite high from 1 mg L-1. By increasing the SmC, the Tn values do not increase significantly. Thus, as
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reported earlier[31], a concentration limit exists above which it is no longer possible to change the Tn. The best INAs are already present in the solution, and it is no longer possible to obtain better INA.
Tn-groups
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Group 1 (–4.0 to –2.0 °C) Group 2 (–6.0 to –5.0 °C) Group 3 (–8.7 to –7.0 °C) Isolated value (–13.6 °C) Group 4 (–24 to –17 °C)
Snomax Concentration (SmC) More than 1 mg L-1 10-1 mg L-1 -3
Between 10 and 10 1 mg L-1
-
Yankofsky Classification [82]
Turner Classification [83]
Type I (above –4 °C) Type II (–7 to –5 °C) Type III (–10 to –7 °C)
Class A (above –4.4 °C) Class B (–5.7 to –4.8 °C) Class C (below –7.6 °C)
10-3 mg L-1 Less than 10-4 mg L-1 and without Snomax
Protein Aggregate Repetitions [26,34,84] From 50 to 100
Presence Probabilities from Literature [26,34,43,84]
Presence Probabilities from our Experiments
Extremely rare
Rare
Undefined
Low probability
Rare
2 to a few
High probability
Highly probability
1 or dusts
None
Could happen
None 0
Always possible
Table 1: Tn-groups distribution obtained for the different studied SmCs during the DSC experiment. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. Last column: difference in the presence probabilities estimated from the analysis of the influence of SmC on Tn (Figure 2).
Consequently, according to the influence of the SmC on the Tn, the control of the nucleation temperature is conceivable. This control is conditional upon the selection of the most suitable experimental conditions and an SmC in which the proper INA subpopulation is highly probable compared to all other efficient INAs. Thus, nucleation will be triggered at a temperature equivalent to
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the nucleating range of this desired INA subpopulation. Although some authors[89,90] explain that Tn studies need to be conducted with repetition of freezing tests for more than 200 times to be accurate, the main goal of this work was to highlight the
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parameters allowing the control of Tn in DSC experiments, and the SmC parameter was one of them.
To conclude, the Tn values are affected by the presence probability of the different INA subpopulations. However, the results we just discussed have been obtained under well-defined experimental
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conditions (–2.5 °C min-1 and 5 µL). Our knowledge of the possibility of controlling the Tn value had to be completed by studies on the influence of different parameters that can modify the presence
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3.1.2. Sample volume influence on Tn value
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probability, such as the SV.
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The effect of the droplet size of water solution with Snomax, on the T n value, was studied by Wex et al.[43]. They observed a dependence of the solution freezing ratio with the droplet size. In our
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experiments, bulk sample volume could also modify the Tn values. Indeed, the SV can be a critical parameter on Tn, as revealed by Figure 3, for different SmCs. Here, we noted the presence of three of the different Tn-groups described above (Tn-groups 1, 3, and 4). For the
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pure water sample, a variation of the Tn value with SV is observable between –24 and –14 °C. Nevertheless, the small number of repetitions, in comparison to the stochastic aspect of this event,
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prevents us from further analyzing these results. Concerning the concentration of 10-2 mg L-1, the absence of a Tn variation (stagnation between –7.3 and –8.0 °C) is a sign of a still-sufficient presence probability of the third INA subpopulation (while the
A
presence probabilities of the first and second INA subpopulations are negligible). The influence of the SV on Tn is not remarkable here.
Figure
3:
SV
influence on Tn during cooling for different
SmCs.
Protocol 1. n = 3. SV: 1; 3; 5; 10; 15 µL. SmC: 0; 0.01;
1;
10;
1000 mg L-1. CR: –
represents value.
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2.5 °C min-1. Cross each Circles lines
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symbol
represent average with
standard
deviations.
For large SV (beyond 5 µL), there is no variation of Tn. An increase in SV by a factor of 2 or 3 (5
N
U
For the concentration of 1, 10, and 103 mg L-1, there are two situations:
A
to 10 µL or 5 to 15 µL) does not appear to affect Tn. Assuming a link between the different Tngroups and the different INA classes, these results seem logical for these values of SmC. In fact,
M
when using an SV of 5 µL, the experimental conditions are identical to those used during the evaluation of the influence of SmC on Tn. During this evaluation, the SmC led to nucleation
ED
processes triggered by the first INA subpopulation. Consequently, by increasing the SV, the presence probability of the first INA subpopulation will increase, and the nucleation will still
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be triggered by this subpopulation. The Tn value will consequently remain in Tn-group 1. In contrast, for small SV (less than 5 µL), the SV seems to influence Tn. In fact, a decrease in Tn was observed. This decrease is more pronounced for the less concentrated solution, 1 mg L-1,
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than for 10 and 103 mg L-1 concentrated solutions. For 1 mg L-1, the decrease seems to be caused by a change in the INA subpopulation, which triggers nucleation. For an SV of 3 µL, one Tn seems to be triggered by the first INA subpopulation, while the other two seem to be
A
triggered by the third INA subpopulation. Consequently, a jump was observed between Tngroup 1 and Tn-group 3. This observation reinforces the possibility that the presence probabilities of the first and second INA subpopulations are closer to each other in the Snomax composition. For the same SmC and an SV of 1 µL, one Tn seems to be triggered by the second INA subpopulation, one by the third INA subpopulation, and the last T n is positioned between these two subpopulations. The separation between the different INA subpopulations is therefore probably not always clear. Consequently, the decrease in the solution of the
presence probabilities of high activity INAs influences the result. A combination of SmC and SV should correspond to a presence probability of an INA subpopulation and consequently to a Tn. For 10 and 103 mg L-1, several Tn values are located between –5 and –4 °C and are consequently between Tn-groups 1 and 2. Most of the other values are still in Tn-group 1, but around its smallest value (–4 °C). Consequently, it seems that a slight decrease in the INA action of the first INA subpopulation is observed, and a presence probability comparison does not explain all the observed
thus be removed, or deteriorated, or their activities could be reduced.
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differences. Thus, other explanations have to be proposed. The INAs of the first subpopulation could
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Authors[40,45] have already reported the affinity of INAs for the liquid-air interfaces when INAs are in solution. The hydrophobic nature of the protein aggregates leads to this effect. When the size of the aggregate is large, this effect is accentuated. At the interface, we can assume a decrease in the INA nucleation strength[45] or an increase in its fragility. Some studies[40,91] argue that the presence of
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the INA near the liquid-air interface did not induce a reduction of the INA activity, but these studies
N
did not use a large INA (more than 1000 kDa) for confirmation. Furthermore, our sample preparatory
A
mode requires the use of a micro-graduated pipette. When passing through the micropipette cone, some of the best INAs may be damaged or removed from the solution. As the surface to volume ratio
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of the solution in contact with the cone increases, the damage could be more intense. For example, the ratio for 1 µL would be higher than that for 5 µL. This difference could explain the Tn variation with
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the SV, which is unexpected even for a small SV at the high SmC (10 and 103 mg L-1). Finally, with the concentration of 10-2 mg L-1, no effect of the SV on Tn is noticeable. These findings
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indicate that the third INA subpopulation could be insensitive to the interface selection or the distance to the interface. The smallest protein aggregates of the third INA subpopulation and the largest
1
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amount of its presence probability might explain this observation (it may be possible that at 10-2 mg L, the number of INA of the third INA subpopulation is so large that the effects related to the interfaces
could be negligible).
3.1.3. Experimental caution regarding influence on Tn value
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On the basis of the results presented above, using Snomax as a nucleating agent requires attention to the preparation of the experimental solutions. In particular, at each pipetting or solution bath change, a selection or deterioration of particularly fragile INA may occur. We wondered if the preparation mode did not remove the most active INA from the solution. To reduce the damaging effect of small micropipette cones, a transfer was made with large cones between each water dilution by a factor of 10 for the different SmCs. In a preliminary study, the use of vortex and small cones resulted in a decrease in the INA activity (measured on the most active INA in Snomax). The solution mixing seemed
to be a significant cause of protein aggregate deterioration and should be done carefully. Consequently, the vortex was eliminated in the study and mixing was performed by a series of capsizing. Storage of solutions with Snomax is another critical parameter. As already mentioned by some authors[30,45], we noted a rapid decrease in the INA activity during storage, even at +4 °C. The best INA subpopulation disappeared during the storage period, perhaps because of coagulation and/or settling of the protein aggregates or protein denaturation in water[45]. Another explanation could be
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the disappearance (by “melting”) of the semi-permanent ice germs hooked to INA. Indeed, it was demonstrated by Pandey et al. that a certain amount of water could be organized in an ice germ form
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around an INA, even at a temperature warmer than the critical one[22]. This amount increases when the temperature decreases[22]. These semi-permanent germs could not remain stable over time at the storage temperature. This phenomenon could be due to Brownian motion disturbance and could involve a decrease with time of the INA ability. Based on our findings and those of previous
U
studies[29,30,45,83], the most efficient INA protein aggregates (type I or class A) seem to be more
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vulnerable to alteration than the smaller aggregates (type III or class C, containing a lower protein aggregate amount). We observed differences between grain ability of Snomax powder as reported by
A
Polen[45]. Consequently, we recommend to use freshly made solutions and to prefer, if it is needed,
process can involve massive damage.
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the shortest storage at 4 °C. Storage at a temperature below 0 °C is inadvisable because the freezing
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3.1.4. Cooling rate influence on Tn value
An INA can pre-organize the surrounding water molecules in an ice-like conformation. The amount of
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pre-organized water molecules is linked to the active surface area of the INA. The association of the INA and the organized water molecules constitutes the nucleating germ. At a specific temperature, when the minimization of Gibbs free energy (linked to the phase change of the concerned water
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molecules) is bigger than the consumption of interfacial surface energy, the ice germ reaches its critical size, and the nucleation process occurs. In the absence of INAs, the homogeneous ice nucleation has been defined to be a stochastic process whose probability increases with time[25]. For the
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heterogeneous ice nucleation description, the process could be temperature dependent or both temperature and time dependent[92]. A certain amount of water molecules is already organized even at a high temperature around an INA. The quantity of molecules increases with the decrease in the temperature[22]. Consequently, the formation of an ice germ triggered by an INA is possibly affected by the time given for water molecules to organize around the INA and thus by the CR amplitude. The CR could consequently affect the temperature at which the germ size becomes critical (Tn).
To analyze the CR effect on the Tn value and verify whether we could neglect the time dependence, we conducted experiments with different CRs, for different SmCs (Figure 4). Tn values obtained for all the solutions are approximately affected equally for the various CRs applied. A decrease in Tn was revealed by an increase in the applied CR, whereas data uncertainties were not affected by this parameter. Between –0.5 and – 40 °C min-1, the evolution of the mean Tn values obtained for the
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studied concentration seem to be about –2.26; –2.10; –2.94; – 1.95; and –2.04 °C, respectively,
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for SmC of 0; 10-2; 1; 10; and 103 mg L-1. Figure 4: CR influence on Tn during
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cooling for different SmCs. Protocol 1. n = 3. CR: 0,5; 1,0; 2,5; 5,0; 10; 20;
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40 °C min-1. SV: 10 µL. SmC: 0; 0.01; 1;
represent
average
with
A
10; 1000 mg L-1. Circles symbol lines standard
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deviations.
These results show a CR dependence on the nucleation process promoted by Snomax. Nevertheless,
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on a DSC, there is a thermal lag led by the time of heat transfer between the occurrence of a phenomenon in a sample pan and its detection by the device[6,72,93–95]. This effect results from the
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delay of heat transfer between the sample and the sensor. The thermal lag is considered to be constant for a given scan rate and is taken into account when the temperature is calibrated at this rate. In
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consequence, the measured temperatures are accurately evaluated when the effects are recorded in the same experimental conditions as those used to record the calibration standards. However, because a change in the scan rate implies a modification of the transit delay of the heat between the sample and the sensor, the thermal lag changes with the rate. In consequence, the values recorded at the
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highest CR are affected and understated. Thus, because variations in temperature are slight and constant for a large range of CR and because we assume a thermal lag for those values, we can suppose that if the building of the germ is time dependent, this phenomenon is not as important. In the range of the CRs we studied, the nucleation process should not be influenced by a localized organizational delay. Budke and Koop[47] have concluded that it is possible to neglect the time dependence of the nucleation process promoted by
Snomax in water. Thus, for our use of the Snomax compound during DSC tests, we will not consider time dependence on its nucleating ability. 3.2. Impact of Snomax on the properties of solutions Before using a compound in an experimental procedure, it is necessary to study the modifications this compound might promote. For this purpose, we conducted experiments to reveal the eventual effect of Snomax on several characteristics of water. As described above, Snomax is a useful tool to induce
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ice at a desired temperature during cooling. Nevertheless, we can question the existence of a thermodynamic impact resulting from the addition of Snomax, related for example to a cryoscopic effect.
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A cryoscopic effect resulting from the addition of a product to a solution is related to a decrease in the
sample water activity and the water chemical potential in solution[96,97]. In thermodynamics, the difference in the chemical potential between the water molecules in solution and the water molecules
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in ice determines the equilibrium temperature value between the two phases. Thus, a cryoscopic effect corresponds to a decrease in the thermodynamic equilibrium temperature. Otherwise, it is well
N
established that a decrease in the water activity of a solution imply a decrease in the homogeneous
A
nucleation temperature of this solution[20]. In other words, if the water activity decreases, the critical
M
ice germ size will increase for a corresponding temperature[19]. The heterogeneous nucleation temperature triggered by an INA will, in the same way, be affected and reduced. When the “phase change equilibrium” temperature value decreases, it affects and decreases the extent of the
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supercooling state and thus, the resulting driving force to the crystallization process. Snomax is composed of INA proteins, cell membrane fragments, and other cellular dust; thus, it could
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influence the solution properties. All these compounds can interact with water. In particular, the large size of the INA protein aggregates can organize a large volume of the surrounding water. Besides, some
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ions or salts present in the cellular dust can also interact with water. 3.2.1. Crystallization and melting kinetics The peak size of DSC thermograms can be linked to the kinetics of the sample phase change. In fact, the horizontal peak shape gives information on the duration and temperature spread of the thermal
A
phenomenon. The vertical peak shape gives information, at each temperature or time, on the quantity of energy exchanged between the sample and the DSC oven. A comparison of the different peak sizes and shapes allows an evaluation of the influence of Snomax on crystallization and melting kinetics. As revealed by Figure 1, the crystallization peaks are distributed in a large range across the temperature scale. Peak shapes and sizes are not identical. For large supercooling, peaks are thin and high. However, for small supercooling, peaks are thick and short. Peak size seems to increase quasi-
linearly with the decrease in the Tn. Furthermore, as described before, crystallization kinetics depend on the temperature at which nucleation occurs and could be evaluated from DSC peak shapes and sizes. Consequently, it seems that the addition of Snomax affects the Tn value, which modifies the crystallization kinetics. In contrast, all the melting peaks are superimposed in a quasi-uniform way and with a similar peak shape and size. The reproducibility of the shape, size, and position on the temperature scale of all these peaks demonstrates that the Snomax does not affect the melting process.
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3.2.2. Equilibrium temperature Tm and Tonset
For a pure compound, the melting process starts at the thermodynamic equilibrium temperature
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between the crystalline and the liquid phase. Aqueous solutions mixtures do not have one transition
temperature because of the liquid phase concentration modification correlated with the ice crystallization ratio. Indeed, when ice crystallizes in the solution, a certain amount of water molecules is extracted from the solution. If we consider the crystallization process to be restricted only to water
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molecules, the amount of water molecules extracted from the solution is then correlated with the
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crystallization ratio. Because water is the major organic solvent, this extraction of water molecules increases the other product concentrations, and the evolution of the concentration modifies the
A
chemical potential (and water activity) of the resulting liquid. This implies a reduction in the phase
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equilibrium temperature. Thus, the tendency of water molecules to integrate an ice crystal is dependent on the freezing ratio. In consequence, there is not only one transition temperature but a
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range of melting temperatures, the values of which are correlated with the freezing ratio. For a pure water sample, the melting process starts at 0 °C, and the corresponding temperature is measured at the beginning of the melting pic (Tonset). In our highly diluted solutions, it would be incorrect to analyze
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only Tonset value. We therefore studied the evolution of Tonset and the end of melting temperature Tm
A
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because they can demonstrate a cryoscopic effect in aqueous solutions.
Figure 5: SmC influence on ice melting temperature, Tm and Tonset, during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. Green columns represent average Tm values with standard deviation. Blue columns represent average Tonset values with standard deviation.
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No particular influence of the SmC
was observed on Tm. The mean values remain close to +3.5 °C (Figure 5). This
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confirms that the melting process is
not affected by the presence of Snomax. Overlapping melting peaks, presented in Figure 6, seem to confirm these findings. Almost all
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Tonset values are close to +0.3 °C. A small decrease in the obtained values was revealed for the two
N
highest SmC. For 103 and 104 mg L-1, Tonset was obtained at 0.0 and –0.3 °C, respectively. Thus, no cryoscopic effect was detected on Tm, but we wondered whether this effect exists on Tonset for these
A
two values unless this decrease is explained by a pre-melting effect.
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3.2.3. Pre-melting phenomenon extent
A pre-melting phenomenon is a melting process that occurs before the phase equilibrium temperature.
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This effect is correlated to the amount of the ice surface that is in contact or interacts with solid materials. Wilson et al.[98] discussed several possible explanations of the pre-melting phenomenon in aqueous solutions during DSC analysis, such as long-range and short-range hydration forces. For a pure
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water sample, this effect is often negligible and difficult to detect. On the contrary, the Snomax induces an increase in the surface contact between water molecules and Snomax materials, which can magnify
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the pre-melting phenomenon.
Selections extracted from thermograms are presented in Figure 6. This enlargement illustrates the beginnings of the melting processes of the different samples, after alignment and stacking of thermograms. The finding demonstrates that an apparent superimposition of peak shapes with
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melting starts before 0 °C for all data. This pre-melting effect seems to be more obvious than that which seems to demonstrate the corresponding Tonset reported in Figure 5. The difference is a result of the determination method of Tonset with two tangent lines. Nevertheless, we notice a clear difference at the start of the melting process for the solutions containing 104 and 103 mg L-1 (and a few also for 102 mg L-1). The Pyris software functionality: “%Area” allows the calculation of the pre-melting phenomenon amount under 0 °C. It is expressed as the surface area percentage of the entire peak with
temperature. We obtained a surface area percentage under 0 °C as follows: less than 2.5% for 104 mg L1
, less than 0.6% for 103 mg L-1, and less than 0.25% for 102 mg L-1. We also observed a decrease in the
baseline leap (corresponding to a change of the sample “mass-specific heat capacity”) for the highest SmC. From our measurements, the pre-melting effect is not negligible for a large amount of Snomax. Thus, at a concentration of 103 mg L-1, we will consider the Snomax influence on the pre-melting process as insignificant. Figure 6: SmC influence on DSC thermogram melting peaks during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 1010-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. All thermograms were aligned from –30 to –20 °C and then
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4;
stacked on the same line. Only one of the thermograms, obtained for each solution is shown for easier reading.
The
Tonset
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calculation
method (with two tangent lines) is
M
A
N
U
designed to ignore the noise and the low
pre-melting
process
that
always occurs in DSC experiments. In
consequence,
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the reduction of Tonset values obtained for 103 and 104 mg L-1, in contrast to the other values, cannot be explained only by the pre-melting phenomenon. In fact, the major endothermic form of the leading
PT
edge of the peak seems to shift toward lower temperatures for these two values as compared to the other values. This argues for the existence of a perceptible cryoscopic effect for these two
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concentrations.
3.2.4. Phase change enthalpy calculation During a phase change, the system evolves into another molecule conformation, which minimizes the Gibbs free energy of the system. This operation consumes or releases thermal energy. Energy is
A
consumed during melting and released during crystallization. The amount of energy exchanged is named the “transition latent heat” (Lf) and corresponds to a phase change enthalpy (ΔH). This amount of energy is correlated to the amount of water molecules (mass) involved in the transition. In consequence, bonded water molecules isolated from the bulk solution can become unfreezable and will thus not participate in the phase change enthalpies of crystallization and melting (ΔHc and ΔHm)[99,100].
Figure 7: SmC influence on enthalpy variations measured
during
crystallization (ΔHc) and melting (ΔHm) processes, calculated from the peak area. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 104;
10-3; 10-2; 10-1; 1; 10;
+10
°C
min-1.
WR:
°C
min-1.
Blue
columns
represent
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2.5
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102; 103; 104 mg L-1. CR: –
average ΔHm values with standard deviation. Red columns
represent
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average ΔHc values with
N
standard deviation.
ΔHc and ΔHm obtained for different SmC are presented in Figure 7. No correlation between the ΔH m
A
obtained and the SmC is apparent. All data are close to 335 J g-1, with one exception for the solution
M
of 104 mg L-1 concentration, which exhibits a small decrease in this value (331 J g-1). Indeed, the amount of energy released during the endothermic melting process is proportional to the amount of water
ED
molecules concerned with the phase change. This amount of “concerned molecules per gram of solution” is reduced when the SmC increases. In fact, for a sample of 5 mg of the 104 mg L-1 solution for example, there are fewer water molecules than those in 5 mg of pure water. This reduction is
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estimated to be around 1% of the water mass proportion in the sample (since 104 mg of Snomax in 1 L of water corresponds to the same mass of Snomax in 106 mg of water; thus the sample water mass
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proportion is 106 / (106 + 104) = 0.99). In consequence, the corresponding ΔHm should be reduced to 333.4 x 0.99 = 330.1 J g-1. For lower SmC, the influence of that effect can be neglected. In contrast, for crystallization, when Tn decreases, a small decrease in the ΔHc value is observed (from
A
306 to 298 J g-1). Interpretations are not straightforward for several reasons: crystallization peaks areas at 0, 10-6, 10-5, and 10-4 mg L-1 were not correctly computed because of the following reasons: exceedance of the DSC data range; a difference between the cooling and the warming rates applied to record peaks increases difficulties in comparison; and Lf varies with the temperature. To allow more accurate comparisons, we conducted other experiments with protocol 2. We chose a larger SV (10 µL) to increase result resolution, with an exception for pure water on cooling for which an SV less than 4 µL is required to prevent the data range heat-flow exceedance (that truncates the
peak). Thus, for pure water, 3 µL SV thermograms (n = 3) were analyzed for the crystallization process calculation and 10 µL SV thermograms (n = 3) for the melting process calculation. The same cooling and warming rate was configured (± 2.5 °C min-1) to enable to conduct comparison analysis. Results are given in Figure 8. During warming, the absence of variation on the calculated ΔHm (≈ 332 J g1
) also argues for an absence of impact of the Snomax on the melting phenomenon process. During
cooling, based on the evolution of the latent heat with temperature[78], we compared measured values to the expected values calculated at the corresponding temperatures. To realize this calculation,
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we used the interpolation of Boutron’s data[2].
We did not obtain a good correlation between our experimental ΔHc and the corresponding Lf
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calculated at the same Tn temperature. This may be because our experimental values were obtained
from a DSC peak area determination, while calculated Lf originates from a theoretical link[78] between the cp values of both phases and Lf. Nevertheless, during calibration, this effect is considered. Thus, the
Figure 8: SmC influence on enthalpy variations measured during crystallization (ΔHc) and melting
(ΔHm)
revealed
by
processes peak
as area
calculation. Protocol 2. n = 3. SmC: 0; 10-2; 1; 10; 103 mg L-1. CR: –2.5 °C min-1. WR: +2.5 °C min-1. SV: 10 µL. For pure water: SV: 3 µL on cooling and 10 µL on warming. This exception is due to the exceedance of the DSC data range heat-flow during crystallization of more
than
supercooling.
4
µL Blue
for
large
columns
represent average ΔHm values with standard deviation. Green columns represent average ΔHc
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A
N
U
absence of correlation needs to be explained by other reasons.
values with standard deviation. Grey columns represent calculated Lf = f(T) data, calculated from interpolation of Boutron’s data[2].
The irreversibility of the crystallization process has already been advanced to explain differences between measured ΔHc and the corresponding Lf[101]. Contrary to a reversible transition, the experimental crystallization latent heat can be reduced because of defects present between ice crystals. The number of defects is influenced by the amount of supercooling when crystallization
occurs[101,102]. These defects imply that a certain amount of water will not participate in the phase change. Furthermore, the presence of defects implies a cost of energy that will not be released by the crystallization process. The presence of these defects also implies an increase in the sample entropy, involving an increase in the cp of ice[101], and indeed a decrease in ΔHc. In consequence, the larger the amount of supercooling is, the more ΔHc will be reduced. On the other hand, during warming, defects could be annihilated because of the reorganization of water molecules at a higher temperature (without any DSC detection before melting peaks). Thus, the corresponding ΔHm will not be affected
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by the temperature at which crystallization occurred (Tn).
Moreover, for large supercooling, the rapidity of the freezing process exceeds the power
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compensation provided by the DSC. This involves slight warming of the sample (particularly observable for Tn-group 4 in Figure 1). In this case, the cooling speed that occurs in the sample does not correspond
to the setting. Because the cooling speed needs to be known to analyze the peak area, the ΔHc could be inaccurate. Besides, the transition temperature could occur at a temperature warmer than T n,
U
whereas Tn is used as a reference to compare measured exchanged enthalpies and Lf. This effect could
N
also lead to a reduction in crystal defects. As a result, and as reported for Bertolini et al. values[103] in Cantrell’s discussion[101], the corresponding measured ΔHc could be higher than the corresponding Lf
A
calculated at Tn.
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Adding Snomax to water creates heterogeneities that increase system entropy. If the cp value of both liquid and ice are affected, all ΔH values would be affected. But no ΔHm variation is observed, even for
ED
large SmC. We therefore assume that this effect can be ignored. Our measurements were performed during ramped temperature variation; thus, the crystallization
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process does not occur at one temperature. This temperature variation causes a bias in the comparison of ΔHc with Lf, because a ΔHc value is obtained from the area calculation in a temperature range, while
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an Lf value is calculated by cp measurement at a specific temperature. During the heat flow calibration, this effect was considered to correlate the peak area measured for a reference standard material to its tabulated value. This calibration was realized on warming, with kinetics specific to the melting process. However, this study focused on crystallization during cooling, which includes variable
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crystallization kinetics. These two factors may impair a precise comparison of the measured value with standard values. To conclude, as opposed to ΔHm, there is no obvious explanation of the variation in ΔHc obtained with the different studied samples. Deveriddy et al.[65] assumed that the effect of P. syringae on the heat release during a DSC experiment is around ±2% for experiments conducted in cryobiological samples. Hu et al.[55] also published data, but we cannot use that data for comparison because their
experimental conditions are different from ours. In our study, we brought to light a few arguments that could explain the observable differences between ΔHc and ΔHm. In practice, we will consider negligible the effect of Snomax on the crystallization of water in solution for concentrations below 103 mg L-1.
4. Conclusion This study indicates that the addition of Snomax to aqueous solution allows the introduction of
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different materials that have specific abilities to induce the heterogeneous nucleation process at a
defined temperature. Three INA subpopulations were described in the Snomax powder (which
originates from P. syringae). Each subpopulation reaches a singular presence probability in the solution
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according to the experimental conditions (concentration and sample volume). We assumed that these INAs subpopulations explained partly our results. With this knowledge, it is conceivable to trigger
crystallization at the desired short range of temperature. To do this, it is required to define the proper
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conditions in which the desired INA subpopulation is highly probable, while all the other more efficient INAs are not. In our experimental conditions (5 µL), we demonstrated that if the presence probability
N
of the first INA subpopulation is high enough, which is reached for an SmC greater or equal to 1 mg L1
A
, the ice nucleation will occur in the range of –4 to –2 °C; if the presence probability of the second INA
subpopulation is large enough, and the presence probability of the first INA subpopulation is low,
M
which is reached for an SmC around 10-1 mg L-1, the ice nucleation will occur in the temperature range of –6 to –5 °C; finally, if the presence probability of the third INA subpopulation is high, and the
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presence probabilities of the first and second INA subpopulations are low, which is reached for an SmC between 10-3 mg L-1 and 10-2 mg L-1, the ice nucleation will occur at the temperature range between –
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10 to –7 °C. As expected, the SV influences the presence probabilities of the different INA subpopulations in solution. Nevertheless, small volumes (less than 5 µL in our experiments) seem also
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to favor the deterioration of Snomax INA, especially for large protein aggregates. Consequently, care must be taken to reduce the INA protein aggregate deterioration (sample preparation, shaking, small SV, storage, etc.). Finally, with the use of this kind of INA, no proof was found about time dependence (through the influence of CR) on Tn control.
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To validate the use of this nucleating trigger, it was required to verify whether artifacts or disturbances were induced by the Snomax in solution. Thermodynamic characteristics (Tm and Tonset) of water solutions with Snomax were compared, and no major disturbances were observed below 103 mg L-1 concentration. No variation was observed for the energy released by the melting of ice (ΔHm), which excludes a possible increase in unfreezable water quantity in solution. Our results seem to indicate that the Snomax presence triggers only negligible modifications of the thermodynamic response of the
solutions and these modifications are only measurable for SmC higher than 103 mg L-1. Thus, Snomax effects on the thermodynamic properties seem to be negligible for lower SmC. This paper paves the way for a large Snomax use in DSC experiments for several applications, especially for cryobiological purposes. Indeed, this compound can help to standardize ice nucleation in studies linked to water crystallization. Conflicts of interest: None. Acknowledgments: The authors thank Cindy Morris for providing the Snomax powder and Kevin Laouer for his help during
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his internship.
Funding: This work was supported by a grant from the Veterinary Campus of Lyon (VetAgro Sup, Lyon, France) and the
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infrastructure project CRB-ANIM (ANR11-INBS-0003).References [1] R.J. Williams, H.T. Meryman, A calorimetric method for measuring ice in frozen solutions, Cryobiology. 1 (1965) 317–323.
P. Boutron, More accurate determination of the quantity of ice crystallized at low cooling rates in the glycerol and 1,2-propanediol aqueous solutions: Comparison with equilibrium, Cryobiology. 21 (1984) 183–191. doi:10.1016/0011-2240(84)90210-4.
[3]
P.H. Rasmussen, B. Jorgensen, J. Nielsen, Aqueous solutions of proline and NaCl studied by differential scanning calorimetry at subzero temperatures, Thermochim. Acta. 303 (1997) 23–30.
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[99]
IP T SC R U
Figr-1Figure 1: SmC influence on DSC crystallization and melting peaks during cooling and thawing, respectively, after
A
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dilution in pure water (encapsulated in a DSC sample pan). Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 101; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. WR: +10 °C min-1. The term “Endo Up” for heat flow representation implies that a positive exchange (during an endothermic reaction) is represented at the top and a negative exchange at the bottom (exothermic). All freezing thermograms were aligned from –1.5 to +0.5 °C and then stacked on the same line (–2 mW). All thawing thermograms were aligned from –30 to –10 °C and then stacked on the same line (+2 mW). Some thermograms were omitted for easier reading.
Figure 2: SmC influence on Tn during cooling. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1 (logarithmic scale representation). CR: –2.5 °C min-1. Circles represent average (n = 3) data. At a given concentration, each cross represents one of the 3 data. The bold blue line represents the average temperature for pure water samples and the dotted lines show its standard deviation. Turner Classification [80]
Group 1
More than
Type I
Class A
(–4.0 to –2.0 °C)
1 mg L-1
(above –4 °C)
(above –4.4 °C)
Type II
Class B
(–7 to –5 °C)
(–5.7 to –4.8 °C)
Type III
Class C
(–10 to –7 °C)
(below –7.6 °C)
Group 2 10-1 mg L-1 (–6.0 to –5.0 °C) Group 3 (–8.7 to –7.0 °C)
Between 10-3 and 101 mg L-1
Protein Aggregate Repetitions [26,34,81]
Presence Probabilities from Literature [26,34,43,81]
Presence Probabilities from our Experiments
From 50 to 100
Extremely rare
Rare
Undefined
Low probability
Rare
2 to a few
High probability
Highly probability
Isolated value 10-3 mg L-1
1 or dusts
(–13.6 °C) None
(–24 to –17 °C)
Less than 10-4 mg L-1 and without Snomax
0
None
Could happen
Always possible
U
Group 4
IP T
Yankofsky Classification [79]
SC R
Tn-groups
Snomax Concentration (SmC)
A
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Table 1: Tn-groups distribution obtained for the different studied SmCs during the DSC experiment. Protocol 1. n = 3. SV:
5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. Last column: difference in the presence probabilities estimated from the analysis of the influence of SmC on Tn (Figure 2). Figure 3: SV influence on Tn during cooling for different SmCs. Protocol 1. n = 3. SV: 1; 3; 5; 10; 15 µL. SmC: 0; 0.01; 1; 10; 1000 mg L-1. CR: –2.5 °C min-1. Cross represents each value. Circles symbol lines represent average with standard deviations.
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Figure 4: CR influence on Tn during cooling for different SmCs. Protocol 1. n = 3. CR: 0,5; 1,0; 2,5; 5,0; 10; 20; 40 °C min-1. SV: 10 µL. SmC: 0; 0.01; 1; 10; 1000 mg L-1. Circles symbol lines represent average with standard deviations.
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Figure 5: SmC influence on ice melting temperature, Tm and Tonset, during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. Green columns represent average Tm values with standard deviation. Blue columns represent average Tonset values with standard deviation.
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Figure 6: SmC influence on DSC thermogram melting peaks during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. All thermograms were aligned from –30 to –20 °C and then stacked on the same line. Only one of the thermograms, obtained for each solution is shown for easier reading.
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Figure 7: SmC influence on enthalpy variations measured during crystallization (ΔHc) and melting (ΔHm) processes, calculated from the peak area. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. WR: +10 °C min-1. Blue columns represent average ΔHm values with standard deviation. Red columns represent average ΔHc values with standard deviation.
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Figure 8: SmC influence on enthalpy variations measured during crystallization (ΔHc) and melting (ΔHm) processes as revealed by peak area calculation. Protocol 2. n = 3. SmC: 0; 10-2; 1; 10; 103 mg L-1. CR: –2.5 °C min-1. WR: +2.5 °C min-1. SV: 10 µL. For pure water: SV: 3 µL on cooling and 10 µL on warming. This exception is due to the exceedance of the DSC data range heat-flow during crystallization of more than 4 µL for large supercooling. Blue columns represent average ΔHm values with standard deviation. Green columns represent average ΔHc values with standard deviation. Grey columns represent calculated Lf = f(T) data, calculated from interpolation of Boutron’s data[2].
S0040-6031(18)30593-8 https://doi.org/10.1016/j.tca.2018.07.022 TCA 78055
To appear in:
Thermochimica Acta
Received date: Revised date: Accepted date:
15-11-2017 27-7-2018 29-7-2018
Please cite this article as: Desnos H, Baudot A, Teixeira M, Louis G, Commin L, Buff S, Bruy`ere P, Ice induction in DSC experiments with Snomax®, Thermochimica Acta (2018), https://doi.org/10.1016/j.tca.2018.07.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Ice induction in DSC experiments with Snomax® Hugo Desnosa,b,*, Anne Baudotb,c, Magda Teixeiraa, Gérard Louisb,c, Loris Commina, Samuel Buffa, Pierre Bruyèrea a: Université de Lyon, VetAgro Sup, UPSP ICE (2011-03-101) & CRB-ANIM (ANR11-INBS-0003), Marcy L’Etoile, France b: Université Sorbonne Paris-Cité, Université Paris Descartes, Paris, France c: INSERM U1148, Paris, France *: [email protected]
HighlightsSnomax allows to control the nucleation temperature during DSC experiment. Several nucleation temperature groups are achievable. A key factor is the presence probabilities of the Pseudomonas. syringae INA classes.
Solutions’ thermodynamic characteristics are not affected at Snomax concentration below 103 mg L-1. Key-Words: DSC; Ice Nucleation; INA; Snomax®; Crystallization; Self-triggered
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Abstract
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The Snomax concentration and the sample volume determine this factor.
For differential scanning calorimetry (DSC) analysis, controlling the degree of supercooling at which crystallization begins may be required for several studies, experiments, and applications. In this paper, the use of Snomax, an ice nucleating agent (INA), was evaluated to create ice at a desirable
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temperature range in a DSC aluminum sample pan. The effect of Snomax on the nucleation
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temperature (Tn) was studied in pure water. Best practices and methods are described in terms of the Tn dependence on three experimental parameters: (i) the Snomax concentration that controls the Tn
A
value for different groups according to the three classes of the Pseudomonas syringae protein
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aggregates (from which Snomax originates); (ii) the sample volume that affects the presence probabilities of the different INA subpopulations in the solution and that could also favor their
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deterioration; and (iii) the cooling rate that does not seem to further affect the Tn value. There is noevidence of time dependence of the nucleation process promoted by Snomax. The presence of artifacts or disturbances introduced by the addition of Snomax into the solution was evaluated. No
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major disturbances of the thermodynamic characteristics of these solutions were observed with the addition of Snomax below 103 mg L-1 concentration. This underscores the possible use of Snomax for
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controlling ice nucleation during DSC experiments.
1. Introduction
Differential scanning calorimetry (DSC) studies enable thermodynamic characterization of aqueous
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solutions by the analysis of the phase transitions of water molecules. For example, in many applications or theoretical developments, it is important to be able to follow the appearance of ice in aqueous solutions[1–6]. This technology provides essential information on the solution properties (such as glass transition temperature, equilibrium temperature, and maximal amount of ice) and on the phase change process (such as kinetics, amplitude, and specific heat changes). Nevertheless, the crystallization processes do not begin at the equilibrium temperature between the liquid solution and the crystalline phases. The system can exist in a supercooling state below the phase
equilibrium temperature. The initiation of the appearance of ice, which is called the nucleation process, must occur to break the metastable state and to start crystallization. After nucleation, the crystallization process occurs as a result of a dendritic ice front growth[7–9]. The speed, mode, and driving force of this process are defined by several parameters, including the extent of the supercooling state[10–14]. Therefore, the temperature of the first appearance of ice crystals in the solution, known as the nucleation temperature (Tn), is an important factor for some experiments. In DSC experiments especially, precise, repeatable, and self-triggered control of the appearance of ice may be required.
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For example, in cell cryopreservation, nucleation control is imperative during most slow-freezing protocols[15–17]. Consequently, control of this step may be required for experiments with procedural
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developments or efficiency evaluation of cryopreservation solutions.
Nucleation is a local phenomenon during which some molecules arrange themselves into a molecular conformation close to the crystalline phase (an ice-like structure)[18–21]. This germ of molecules, organized in a minimizing Gibbs free energy conformation (in comparison to the surrounding
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molecules)[22], catalyzes the transfer to the crystalline phase. The study of the nucleation
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phenomenon for pure water, is an expanding branch of science whose theoretical explanations are still late to predict all observable phenomena. Two types of nucleation exist: homogeneous and
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heterogeneous. The classical nucleation theory[23,24] assumes that it is possible for a certain quantity
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of water molecules to be pre-organized in a stochastic manner into an ice-like structure and thus, be able to initiate the nucleation phenomenon. According to this theory, in the absence of
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defects/heterogeneities in the spatial water molecule distribution, the quantity of water molecules cannot exceed the number of molecules present within a water cluster. This kind of germ, stemming from the molecules of the solution, corresponds to homogeneous nucleation. The homogeneous
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temperature of a pure water solution is assumed to occur in the range of –35 to –38 °C, depending on the cooling rate and volume[25]. This type of supercooling magnitude can only be achieved in special
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cases that are difficult to obtain as they imply an absence of external perturbation. Consequently, the nucleation of water (even pure water) often starts from the heterogeneous nucleation phenomenon. This type of nucleation involves a spatial heterogeneous distribution of water molecules, which favors the appearance of an ice germ larger than water cluster size. These heterogeneities can have different
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origins and can be introduced by impurities or organizational disturbances within the system[18]. Not all impurities favor ice nucleation at high temperatures, but there are some compounds whose structural or surface properties are particularly prone to nucleation. Such compounds are called ice nucleating agents (INA). The best INA is an ice crystal itself, which can theoretically initiate crystallization from the equilibrium temperature between the crystalline and the liquid phase (0 °C for pure water). In addition, some INAs can initiate the ice nucleation process from a supercooling state
of only a few degrees[21,26,27]. There are several types of INAs, and the two major types are bacterial and mineral INAs. Some microorganisms, such as Pseudomonas syringae, synthesize proteins on extracellular aggregates, which can initiate the ice nucleation process inside aqueous solutions[26,28]. The nucleation ability of these kinds of extracellular aggregates has been widely studied[26,29–34]. In nature, these bacteria are implicated as the source of frost at relatively high temperatures[35–37]. A freeze-dried powder containing P. syringae cells and sterilized by gamma irradiation is currently marketed by the York
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Snow® Company[38]. This product, Snomax, is used and sold to winter sports resorts to make artificial snow. It is thus a potential INA. Other potential INAs have also been proposed (AgI, SIO2, ATD,
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IceStart®)[19,20,27,39–41].
The nucleating effect of P. syringae on ice appearance has been previously described[32], and the effect of the Snomax concentration on the Tn value was first reported by Maki (1974)[31] and then by other teams[42–47], including studies with DSC[27,48–51]. Snomax is a particularly efficient INA.
for
example,
in
cryobiology
studies[15,57–60]
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solutions[34,44,52–56],
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Hence, Snomax or P. syringae is widely used to control ice crystal appearance in aqueous with
optical
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experiments[5,6,61–64] or in DSC experiments[4,6,65–72]. However, all these studies were conducted under different experimental conditions, thus preventing comparisons among them. In particular, the
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studies were not conducted on bulk samples but often on emulsions. The present study aimed to investigate a tool that controls ice nucleation to optimize DSC experiments
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with bulk samples. We evaluated the Snomax INA activity in DSC experiments to induce ice nucleation at a selected temperature range to define how to use the tool relevantly. In this study, we focused on
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the use of the Snomax compound inside a DSC aluminum pan with bulk samples of pure water. Two aspects were evaluated. We first studied the impact on Tn of namely, the Snomax concentration
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(SmC), the sample volume (SV), and the cooling rate (CR). As Snomax is an INA that interacts with surrounding water molecules and because it replaces some of the water molecules present in a solution, the system evolves from a pure compound description to a mixture. Consequently, perturbations of the thermodynamic responses could be linked to the addition of Snomax. Thus, as
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Snomax is supposed to be used inside solutions during the thermodynamic characterization of these solutions, we evaluated the effects of the addition of Snomax on the solution’s thermodynamic properties.
2. Materials and Methods 2.1. Materials
DSC (Diamond, Perkin-Elmer; Pyris 11.1.1) with Cryofill (liquid nitrogen cooling system) was used to obtain thermal, kinetic, and calorimetric information in particular on ice crystallization and melting in aqueous mixtures during cooling and warming. The atmospheric pressure was not controlled during the experiment (estimated around 1000±50 hPa according to [73]). Sample was loaded into a sealable standard aluminum sample pan (Perkin Elmer 0219-0062 designed for volatile samples) at room temperature. The evolution of pressure inside the sample pan as a function of temperature is unknown and uncontrolled.
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Calibration: The temperature range of the DSC was carefully calibrated at +2.5 °C min-1 with the structural conformation transition (c/c) temperature of cyclohexane (–87.06 °C)[74] and the onset of
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the melting transition temperature of pure bi-osmosed water (0.00 °C)[75]. Heat flow was calibrated, at +2.5 °C min-1 (for a normal data range of 320 mW) with the heat of c/c transition of cyclohexane
(79.58 J g-1)[74] and the heat of the melting of pure bi-osmosed water (333.446 J g-1)[75]. The empty oven baseline of the DSC, which was systematically subtracted from the sample thermograms, was
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regularly recorded with care. The validity of the calibration was verified regularly. DSC instrument
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energy (previously verified in our instrument[76,77]).
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uncertainties specified by Perkin Elmer are: u(p) = ±0.11 °C for temperature and ur(p) = 0.11 J g-1 for
Sample Table
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Chemical Name H2O Snomax®
Source
Laboratory bi-osmosis purification, Purelab Prima, Elga
York Snow Company, Snowmakers AG, Steffisburg, Switzerland
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Sample preparation: Solutions were freshly made in mass per volume percentage (% m v-1) and preserved for less than 2 weeks at +4°C. Before testing, the solutions were warmed to room temperature for 30 minutes. The weighing of one or two grains of the Snomax powder was conducted
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with a high sensibility scale balance (XS105 DualRange; Metler Toledo; resolution: 10-5 g). The highest concentration solution was made first; all other concentrations were then obtained with a 10-time
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dilution pipetting (100 µL of the solution with Snomax in 900 µL of pure bi-osmosed water). The solutions were mixed through a series of capsizing (about 20) before pipetting. For DSC experiments, 1 to 15 µL of each medium was loaded into a sealable standard aluminum sample pan at room temperature. These sample pans were previously cleaned using a protocol provided by Perkin Elmer
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(with xylene and acetone). Sample weighing was conducted using the high sensibility scale balance. To ensure the sealed pans’ integrity, weights were measured at the end of the experiments and compared with the weights obtained before the DSC measurements. Each medium was tested three times, and for each test, a new sample pan was prepared. 2.2. Methods
Analysis of solutions: The properties of several solutions were determined using thermograms and Pyris software tools. Tn: The nucleation temperature (Tn) of a solution is the temperature at which the phase change from liquid to solid is initiated. For heterogeneous nucleation, this temperature is not a thermodynamic property and is only linked to the most efficient ice-germ inducer in the solution. In a DSC record, this temperature is revealed by the onset of the crystallization peak. Tonset: For a pure compound during warming at ambient pressure, one temperature exists at which the
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crystalline phase is no longer the minimizing Gibbs free energy conformation. From this temperature,
the thermal agitation can break some bonds between molecules, and the crystalline phase starts to melt. This temperature is the specific melting temperature of the compound at ambient pressure. It is
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a thermodynamic property, revealed in DSC experiments by the beginning/onset of the melting peaks
(Tonset). To calculate this temperature, two tangent lines are drawn from the beginning of melting peaks.
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Tm: During a mixture’s melting process, the maximum melting temperature (Tm) gives valuable information about the properties of the solution. At this temperature, we consider that the last ice
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crystal disappears from the solution. Thus, during cooling, it is assumed that a mixture liquid solution
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evolves into the supercooling state below the Tm. During a DSC analysis, this value is raised to the top
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of the melting peak.
ΔHc and ΔHm: To precisely measure the amount of ice that has crystallized during cooling or that has melted during thawing, the areas of peaks obtained on DSC thermograms during phase transitions are
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evaluated. The peak area is correlated with the amount of energy exchanged during the thermal event in solution (exothermic or endothermic). This allows calculation of enthalpy (ΔH) of the corresponding
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phase transition as it corresponds to the amount of energy, per gram of sample, which is released or absorbed during the phase transition. Consequently, in an aqueous solution, this area is correlated
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with the amount of water involved in the phase transition. ΔH can be evaluated during the crystallization process (crystallization enthalpy: ΔHc) or the melting process (melting enthalpy: ΔHm). The areas were calculated using a sigmoid curve- baseline.
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Protocols: Two types of protocols were used during the experiments. Protocol 1: From +10 °C to –40 °C at “CR” °C min-1 From –40 °C to +20 °C at +10 °C min-1 Protocol 2: From +10 °C to -150 °C at –100 °C min-1 From –150 °C to +10 °C at +2.5 °C min-1 From +10 °C to –150 °C at –2.5 °C min-1 From –150 °C to +20 °C at +20 °C min-1
Snomax control of ice nucleation: Protocol 1 rapidly allows the characterization of Tn for three experimental parameters: SmC, SV, and CR.
Effect of the SmC on the Tn: 11 SmC (from 10-6 to
104 mg L-1) were tested with a CR of 2.5 °C min-1 and an SV of 5 µL.
Effect of the SV on the Tn: 5 SV (1, 3, 5, 10, and 15 µL) were tested with a CR of 2.5 °C min-1 and 4 SmC (10-2, 1, 10, and 103 mg L-1).
Effect of the CR on the Tn: 7 CR (0.5, 1.0, 2.5, 5.0, 10, 20, and 40 °C min-1) were tested with an SV of 10 µL and 4 SmC (10-2, 1, 10, and 103 mg L-1).
Snomax impact on solution properties: All the results obtained with protocol 1 during the study of the SmC impact on Tn were used to study Tm, Tonset, ΔHc, and ΔHm. Complementary studies were conducted
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with protocol 2 previously described by our team to characterize cryopreservation solutions[76,77]. This time-consuming protocol was used to compare ΔHc and ΔHm more precisely, with a slow cooling or warming rate (CR and WR equal to 2.5 °C min-1) for four SmC (10-2, 1, 10, 103 mg L-1) and SV of 3 µL
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or 10 µL. To obtain the latent heat of solidification of supercooled water (Lf) at the measured phase
change temperature (we considered this temperature to be Tn), we interpolated Boutron’s[2] data. He calculated the temperature dependence of Lf from Angell’s[78] measurements of the specific heat capacity (cp) of supercooled water at different temperatures and the corresponding values of ice given
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in HandBook[79].
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3. Results & Discussion
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To evaluate ice nucleation with Snomax during DSC experiments, we studied two aspects: the Snomax
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nucleation activity and the Snomax effect on solutions’ properties. Figure 1, which shows thermograms
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with crystallization and melting processes, gives an overview of characterization of this compound.
Figure 1: SmC influence on DSC crystallization and melting peaks during cooling and thawing, respectively, after dilution in pure water (encapsulated in a DSC sample pan). Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102;
103; 104 mg L-1. CR: –2.5 °C min-1. WR: +10 °C min-1. The term “Endo Up” for heat flow representation implies that a positive exchange (during an endothermic reaction) is represented at the top and a negative exchange at the bottom (exothermic). All freezing thermograms were aligned from –1.5 to +0.5 °C and then stacked on the same line (–2 mW). All thawing thermograms were aligned from –30 to –10 °C and then stacked on the same line (+2 mW). Some thermograms were omitted for easier reading.
3.1. Snomax control of ice nucleation Nucleation in pure water is a stochastic process during a DSC experiment and is influenced by heterogeneities induced by experimental conditions. These heterogeneities can be created by
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impurities, edges, or perturbations naturally present in a solution within the DSC sample pan. In
consequence, in a sample pan during cooling at –2.5 °C min-1, 5 µL of pure water sample solution
usually crystallizes at a temperature range between –24 and –17 °C. Crystallization occurs from the
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heterogeneous nucleation within that temperature range. 3.1.1. Snomax concentration influence on Tn value
The DSC characterization presented in Figures 1 and 2 demonstrates a large range of crystallization
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peak distribution on the temperature scale. Peak shapes and sizes are not identical. According to SmC, four distinct groups of Tn can be defined. Between –24 and –17 °C, there is a group (the Tn-group 4)
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composed of pure water and solutions with very low SmC (less than or equal to 10-4 mg L-1). At –13.6 °C,
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there is a single Tn data with a high dilution of Snomax (10-3 mg L-1). Between –8.7 and –2.0 °C, there
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are three groups composed of all the solutions with the other SmC (from 10-3 mg L-1): one between – 8.7 and –7.0 °C with SmC of 10-3 to 10-1 mg L-1 (the Tn-group 3); one between –6.0 and –5.0 °C with SmC
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of 10-1 mg L-1 (the Tn-group 2); and one between –4.0 and –2.0 °C with SmC of 1 to 104 mg L-1 (the Tngroup 1). The logarithmic scale in Figure 2 emphasizes the influence of the SmC on the Tn for a pure water solution. From average values, two horizontal asymptotes can be observed: the first one for SmC
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less than or equal to 10-4 mg L-1, with noise variations of 1 or 2 °C around –20 °C; and the second one for SmC greater than or equal to 1 mg L-1, around –3 °C. For the other SmC, only a small proportion of
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Snomax (10-3 mg L-1) induces an increase in the Tn as compared to pure water. A larger amount of Snomax added to water reduces slightly more the possibility of achieving a large supercooling. As Snomax contains INAs, this result is partly a direct consequence of the stochastic nature of nucleation. The increase in the concentration of impurities increases the chance to favor the nucleation and thus
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increases Tn. The heterogeneous nucleation temperature (Tn) is determined by the presence of INA particles in the solution, which will cause the ice germ to become critical at the highest temperature. According to the CNT, the abilities of an INA to trigger nucleation do not depend on the presence of other INAs in solution[80]. When the INA germ becomes critical, the nucleation phenomenon is triggered, and the crystallization starts from this point[81].
To date, three INA subpopulations from P. syringae have been reported and are linked to three specific INA activities and three specific presence probabilities[21,26,29,30,33,82,83]. Each subpopulation corresponds to different sizes of protein aggregates consisting of the repetition of a 150 kDA protein[26,30,32,84,85]. In an aggregate, nucleating proteins act cooperatively to nucleate supercooled water. According to the classical nucleation theory, the largest INA surface will preorganize the largest amount of water molecules on an ice-like structure (in its surroundings) and will be the most efficient germ in the solution[86]. Thus, the larger an aggregate of an ice nucleating
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protein, the more it triggers nucleation at high temperature[26,33]. Two different subpopulation
classifications have been proposed. Yankofsky et al.[82] proposed one classification of the subpopulations, named as type I, II, and III, with ice-nucleating temperature in the range of –4 to –2 °C,
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–7 to –5 °C, and –10 to –7 °C, respectively. Turner et al.[83] proposed another subpopulation
classification as follows: class A, active above –4.8 °C; class B, between –5.7 and –4.8 °C; and class C, below –7.6 °C. INA protein type I, or class A, from P. syringae, is considered to be one of the most
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A
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active INA known after an ice crystal[32,87].
Figure 2: SmC influence on Tn during cooling. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1 (logarithmic scale representation). CR: –2.5 °C min-1. Circles represent average (n = 3) data. At a given concentration, each cross represents one of the 3 data. The bold blue line represents the average temperature for pure water samples and the dotted lines show its standard deviation.
As Snomax is derived from P. syringae, these different subpopulations could influence the ability of Snomax to nucleate ice. Because the nucleation event starts from the most efficient ice germ inducer in the solution, the temperature at which an aqueous solution with Snomax will nucleate depends on the presence of INA subpopulations of P. syringae, which depends on the Snomax quantity. The presence probability of the third INA subpopulation is higher than that of the second, which is itself higher than that of the first. Assuming a link between the different Tn-groups and the different INA classes, it is possible to explain the Tn step-variation with SmC. In consequence, we can assume that Tn
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depends on the presence probability of the different INA subpopulations, which is linked to the SmC.
Increasing the SmC increases the chance to have a most efficient INA in solution and increases the T n value. The presence of the different subpopulations with their own presence probabilities could
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consequently cause the step variation of Tn with SmC.
Snomax has been recently proposed as a biological ice nucleant test substance[43] to compare ice nucleation measurement techniques. In the present study and other studies[45,88], the reproducibility
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of the ice nucleation activity of the Snomax was proven for specific experimental conditions. We can
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then preliminary assume the stability of the presence probabilities of each INA subpopulation.
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In our experimental conditions, as mentioned in Table 1, four distinct Tn-groups were achieved using different SmCs. In Tn-group 4, the Tn values obtained for the low SmC are equivalent to those obtained
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without Snomax. We therefore conclude that there is a maximum Snomax dilution below which, no influence on the nucleation effect can be noticed. We can thus expect that there is only a minor chance
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of active INA traces of P. syringae. The probability of creating a germ, whose activity is superior to that of a bi-osmosed pure water solution encapsulated in a DSC sample pan is negligible. In Tn-group 3, we
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assume that the nucleation is triggered by the third INA subpopulation (III or C). In Tn-group 2, we assume that the nucleation is triggered by the second INA subpopulation (II or B). This group is small and contains only two values of Tn. Finally, nucleation in the last Tn-group (named Tn-group 1) is
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supposedly triggered by the first INA subpopulation (I or A). The repartition of all these Tn-groups can be observed in Figures 1 and 2. For the concentration of 10-3 mg L-1, there are two kinds of Tn values: an isolated value localized at –
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13.6 °C and two values around –7.5 °C in Tn-group 3. This isolated value is far from the temperature range of the third INA subpopulation. Three hypotheses could explain this value. First, a study[26] proposed that the ice nucleation activity of a single INA protein could trigger ice nucleation between –13 and –12°C, which is close to our value. Second, residual materials or impurities in the Snomax powder (other than protein compounds from the active INA protein) or dust could induce this nucleation. These residual materials or impurities could be more efficient as INA than those usually
present on the DSC sample pan. Third, the stochastic aspect of the nucleation classically involved in sample pan, for a pure water sample does not exclude the occurrence of this type of Tn value. For the concentration of 10-2 mg L-1, all values were obtained in Tn-group 3, which indicates that the probability of obtaining the first or second INA subpopulation is low as opposed to that of obtaining the third INA subpopulation, which is high. For the concentration of 10-1 mg L-1, two Tn values were obtained in Tn-group 2, and the other was part
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of Tn-group 3. Consequently, the probability of obtaining the second INA subpopulation is quite high but not exclusive in our experimental conditions, and the probability of obtaining the first INA
subpopulation seems negligible. Tn-group 2 corresponds to a quite small interval of temperature, and
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the Tn occurrence from this group is low. As Snomax is a commercial product, this result could be due to its production method. The bacterial strains of P. syringae selected and cultivated for the production of Snomax may have been designed to achieve a high probability of the presence of the first INA subpopulation. Consequently, the probabilities of the presence of the first and the second INA
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subpopulation could be similar. In addition, the range of SmC for which the probability of the presence
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of the second INA subpopulation is large, and the probability of the presence of the first INA
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subpopulation is low, is probably small. The evaluation of this range could be the objective of a specific study, especially by following the nucleation rate of a solution containing these INAs, as described by
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Vali [81]. It was shown that this kind of studies can provide information on the INA properties and can prove the presence and efficiency of different INA subpopulations[43]. However, we were not able to
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achieve this kind of results with our experimental device, and it would be difficult to make comparisons with the present data.
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Finally, we considered that the probability of obtaining the first INA subpopulation in the solution is quite high from 1 mg L-1. By increasing the SmC, the Tn values do not increase significantly. Thus, as
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reported earlier[31], a concentration limit exists above which it is no longer possible to change the Tn. The best INAs are already present in the solution, and it is no longer possible to obtain better INA.
Tn-groups
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Group 1 (–4.0 to –2.0 °C) Group 2 (–6.0 to –5.0 °C) Group 3 (–8.7 to –7.0 °C) Isolated value (–13.6 °C) Group 4 (–24 to –17 °C)
Snomax Concentration (SmC) More than 1 mg L-1 10-1 mg L-1 -3
Between 10 and 10 1 mg L-1
-
Yankofsky Classification [82]
Turner Classification [83]
Type I (above –4 °C) Type II (–7 to –5 °C) Type III (–10 to –7 °C)
Class A (above –4.4 °C) Class B (–5.7 to –4.8 °C) Class C (below –7.6 °C)
10-3 mg L-1 Less than 10-4 mg L-1 and without Snomax
Protein Aggregate Repetitions [26,34,84] From 50 to 100
Presence Probabilities from Literature [26,34,43,84]
Presence Probabilities from our Experiments
Extremely rare
Rare
Undefined
Low probability
Rare
2 to a few
High probability
Highly probability
1 or dusts
None
Could happen
None 0
Always possible
Table 1: Tn-groups distribution obtained for the different studied SmCs during the DSC experiment. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. Last column: difference in the presence probabilities estimated from the analysis of the influence of SmC on Tn (Figure 2).
Consequently, according to the influence of the SmC on the Tn, the control of the nucleation temperature is conceivable. This control is conditional upon the selection of the most suitable experimental conditions and an SmC in which the proper INA subpopulation is highly probable compared to all other efficient INAs. Thus, nucleation will be triggered at a temperature equivalent to
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the nucleating range of this desired INA subpopulation. Although some authors[89,90] explain that Tn studies need to be conducted with repetition of freezing tests for more than 200 times to be accurate, the main goal of this work was to highlight the
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parameters allowing the control of Tn in DSC experiments, and the SmC parameter was one of them.
To conclude, the Tn values are affected by the presence probability of the different INA subpopulations. However, the results we just discussed have been obtained under well-defined experimental
U
conditions (–2.5 °C min-1 and 5 µL). Our knowledge of the possibility of controlling the Tn value had to be completed by studies on the influence of different parameters that can modify the presence
A
3.1.2. Sample volume influence on Tn value
N
probability, such as the SV.
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The effect of the droplet size of water solution with Snomax, on the T n value, was studied by Wex et al.[43]. They observed a dependence of the solution freezing ratio with the droplet size. In our
ED
experiments, bulk sample volume could also modify the Tn values. Indeed, the SV can be a critical parameter on Tn, as revealed by Figure 3, for different SmCs. Here, we noted the presence of three of the different Tn-groups described above (Tn-groups 1, 3, and 4). For the
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pure water sample, a variation of the Tn value with SV is observable between –24 and –14 °C. Nevertheless, the small number of repetitions, in comparison to the stochastic aspect of this event,
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prevents us from further analyzing these results. Concerning the concentration of 10-2 mg L-1, the absence of a Tn variation (stagnation between –7.3 and –8.0 °C) is a sign of a still-sufficient presence probability of the third INA subpopulation (while the
A
presence probabilities of the first and second INA subpopulations are negligible). The influence of the SV on Tn is not remarkable here.
Figure
3:
SV
influence on Tn during cooling for different
SmCs.
Protocol 1. n = 3. SV: 1; 3; 5; 10; 15 µL. SmC: 0; 0.01;
1;
10;
1000 mg L-1. CR: –
represents value.
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2.5 °C min-1. Cross each Circles lines
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symbol
represent average with
standard
deviations.
For large SV (beyond 5 µL), there is no variation of Tn. An increase in SV by a factor of 2 or 3 (5
N
U
For the concentration of 1, 10, and 103 mg L-1, there are two situations:
A
to 10 µL or 5 to 15 µL) does not appear to affect Tn. Assuming a link between the different Tngroups and the different INA classes, these results seem logical for these values of SmC. In fact,
M
when using an SV of 5 µL, the experimental conditions are identical to those used during the evaluation of the influence of SmC on Tn. During this evaluation, the SmC led to nucleation
ED
processes triggered by the first INA subpopulation. Consequently, by increasing the SV, the presence probability of the first INA subpopulation will increase, and the nucleation will still
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be triggered by this subpopulation. The Tn value will consequently remain in Tn-group 1. In contrast, for small SV (less than 5 µL), the SV seems to influence Tn. In fact, a decrease in Tn was observed. This decrease is more pronounced for the less concentrated solution, 1 mg L-1,
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than for 10 and 103 mg L-1 concentrated solutions. For 1 mg L-1, the decrease seems to be caused by a change in the INA subpopulation, which triggers nucleation. For an SV of 3 µL, one Tn seems to be triggered by the first INA subpopulation, while the other two seem to be
A
triggered by the third INA subpopulation. Consequently, a jump was observed between Tngroup 1 and Tn-group 3. This observation reinforces the possibility that the presence probabilities of the first and second INA subpopulations are closer to each other in the Snomax composition. For the same SmC and an SV of 1 µL, one Tn seems to be triggered by the second INA subpopulation, one by the third INA subpopulation, and the last T n is positioned between these two subpopulations. The separation between the different INA subpopulations is therefore probably not always clear. Consequently, the decrease in the solution of the
presence probabilities of high activity INAs influences the result. A combination of SmC and SV should correspond to a presence probability of an INA subpopulation and consequently to a Tn. For 10 and 103 mg L-1, several Tn values are located between –5 and –4 °C and are consequently between Tn-groups 1 and 2. Most of the other values are still in Tn-group 1, but around its smallest value (–4 °C). Consequently, it seems that a slight decrease in the INA action of the first INA subpopulation is observed, and a presence probability comparison does not explain all the observed
thus be removed, or deteriorated, or their activities could be reduced.
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differences. Thus, other explanations have to be proposed. The INAs of the first subpopulation could
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Authors[40,45] have already reported the affinity of INAs for the liquid-air interfaces when INAs are in solution. The hydrophobic nature of the protein aggregates leads to this effect. When the size of the aggregate is large, this effect is accentuated. At the interface, we can assume a decrease in the INA nucleation strength[45] or an increase in its fragility. Some studies[40,91] argue that the presence of
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the INA near the liquid-air interface did not induce a reduction of the INA activity, but these studies
N
did not use a large INA (more than 1000 kDa) for confirmation. Furthermore, our sample preparatory
A
mode requires the use of a micro-graduated pipette. When passing through the micropipette cone, some of the best INAs may be damaged or removed from the solution. As the surface to volume ratio
M
of the solution in contact with the cone increases, the damage could be more intense. For example, the ratio for 1 µL would be higher than that for 5 µL. This difference could explain the Tn variation with
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the SV, which is unexpected even for a small SV at the high SmC (10 and 103 mg L-1). Finally, with the concentration of 10-2 mg L-1, no effect of the SV on Tn is noticeable. These findings
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indicate that the third INA subpopulation could be insensitive to the interface selection or the distance to the interface. The smallest protein aggregates of the third INA subpopulation and the largest
1
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amount of its presence probability might explain this observation (it may be possible that at 10-2 mg L, the number of INA of the third INA subpopulation is so large that the effects related to the interfaces
could be negligible).
3.1.3. Experimental caution regarding influence on Tn value
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On the basis of the results presented above, using Snomax as a nucleating agent requires attention to the preparation of the experimental solutions. In particular, at each pipetting or solution bath change, a selection or deterioration of particularly fragile INA may occur. We wondered if the preparation mode did not remove the most active INA from the solution. To reduce the damaging effect of small micropipette cones, a transfer was made with large cones between each water dilution by a factor of 10 for the different SmCs. In a preliminary study, the use of vortex and small cones resulted in a decrease in the INA activity (measured on the most active INA in Snomax). The solution mixing seemed
to be a significant cause of protein aggregate deterioration and should be done carefully. Consequently, the vortex was eliminated in the study and mixing was performed by a series of capsizing. Storage of solutions with Snomax is another critical parameter. As already mentioned by some authors[30,45], we noted a rapid decrease in the INA activity during storage, even at +4 °C. The best INA subpopulation disappeared during the storage period, perhaps because of coagulation and/or settling of the protein aggregates or protein denaturation in water[45]. Another explanation could be
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the disappearance (by “melting”) of the semi-permanent ice germs hooked to INA. Indeed, it was demonstrated by Pandey et al. that a certain amount of water could be organized in an ice germ form
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around an INA, even at a temperature warmer than the critical one[22]. This amount increases when the temperature decreases[22]. These semi-permanent germs could not remain stable over time at the storage temperature. This phenomenon could be due to Brownian motion disturbance and could involve a decrease with time of the INA ability. Based on our findings and those of previous
U
studies[29,30,45,83], the most efficient INA protein aggregates (type I or class A) seem to be more
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vulnerable to alteration than the smaller aggregates (type III or class C, containing a lower protein aggregate amount). We observed differences between grain ability of Snomax powder as reported by
A
Polen[45]. Consequently, we recommend to use freshly made solutions and to prefer, if it is needed,
process can involve massive damage.
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the shortest storage at 4 °C. Storage at a temperature below 0 °C is inadvisable because the freezing
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3.1.4. Cooling rate influence on Tn value
An INA can pre-organize the surrounding water molecules in an ice-like conformation. The amount of
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pre-organized water molecules is linked to the active surface area of the INA. The association of the INA and the organized water molecules constitutes the nucleating germ. At a specific temperature, when the minimization of Gibbs free energy (linked to the phase change of the concerned water
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molecules) is bigger than the consumption of interfacial surface energy, the ice germ reaches its critical size, and the nucleation process occurs. In the absence of INAs, the homogeneous ice nucleation has been defined to be a stochastic process whose probability increases with time[25]. For the
A
heterogeneous ice nucleation description, the process could be temperature dependent or both temperature and time dependent[92]. A certain amount of water molecules is already organized even at a high temperature around an INA. The quantity of molecules increases with the decrease in the temperature[22]. Consequently, the formation of an ice germ triggered by an INA is possibly affected by the time given for water molecules to organize around the INA and thus by the CR amplitude. The CR could consequently affect the temperature at which the germ size becomes critical (Tn).
To analyze the CR effect on the Tn value and verify whether we could neglect the time dependence, we conducted experiments with different CRs, for different SmCs (Figure 4). Tn values obtained for all the solutions are approximately affected equally for the various CRs applied. A decrease in Tn was revealed by an increase in the applied CR, whereas data uncertainties were not affected by this parameter. Between –0.5 and – 40 °C min-1, the evolution of the mean Tn values obtained for the
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studied concentration seem to be about –2.26; –2.10; –2.94; – 1.95; and –2.04 °C, respectively,
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for SmC of 0; 10-2; 1; 10; and 103 mg L-1. Figure 4: CR influence on Tn during
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cooling for different SmCs. Protocol 1. n = 3. CR: 0,5; 1,0; 2,5; 5,0; 10; 20;
N
40 °C min-1. SV: 10 µL. SmC: 0; 0.01; 1;
represent
average
with
A
10; 1000 mg L-1. Circles symbol lines standard
M
deviations.
These results show a CR dependence on the nucleation process promoted by Snomax. Nevertheless,
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on a DSC, there is a thermal lag led by the time of heat transfer between the occurrence of a phenomenon in a sample pan and its detection by the device[6,72,93–95]. This effect results from the
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delay of heat transfer between the sample and the sensor. The thermal lag is considered to be constant for a given scan rate and is taken into account when the temperature is calibrated at this rate. In
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consequence, the measured temperatures are accurately evaluated when the effects are recorded in the same experimental conditions as those used to record the calibration standards. However, because a change in the scan rate implies a modification of the transit delay of the heat between the sample and the sensor, the thermal lag changes with the rate. In consequence, the values recorded at the
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highest CR are affected and understated. Thus, because variations in temperature are slight and constant for a large range of CR and because we assume a thermal lag for those values, we can suppose that if the building of the germ is time dependent, this phenomenon is not as important. In the range of the CRs we studied, the nucleation process should not be influenced by a localized organizational delay. Budke and Koop[47] have concluded that it is possible to neglect the time dependence of the nucleation process promoted by
Snomax in water. Thus, for our use of the Snomax compound during DSC tests, we will not consider time dependence on its nucleating ability. 3.2. Impact of Snomax on the properties of solutions Before using a compound in an experimental procedure, it is necessary to study the modifications this compound might promote. For this purpose, we conducted experiments to reveal the eventual effect of Snomax on several characteristics of water. As described above, Snomax is a useful tool to induce
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ice at a desired temperature during cooling. Nevertheless, we can question the existence of a thermodynamic impact resulting from the addition of Snomax, related for example to a cryoscopic effect.
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A cryoscopic effect resulting from the addition of a product to a solution is related to a decrease in the
sample water activity and the water chemical potential in solution[96,97]. In thermodynamics, the difference in the chemical potential between the water molecules in solution and the water molecules
U
in ice determines the equilibrium temperature value between the two phases. Thus, a cryoscopic effect corresponds to a decrease in the thermodynamic equilibrium temperature. Otherwise, it is well
N
established that a decrease in the water activity of a solution imply a decrease in the homogeneous
A
nucleation temperature of this solution[20]. In other words, if the water activity decreases, the critical
M
ice germ size will increase for a corresponding temperature[19]. The heterogeneous nucleation temperature triggered by an INA will, in the same way, be affected and reduced. When the “phase change equilibrium” temperature value decreases, it affects and decreases the extent of the
ED
supercooling state and thus, the resulting driving force to the crystallization process. Snomax is composed of INA proteins, cell membrane fragments, and other cellular dust; thus, it could
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influence the solution properties. All these compounds can interact with water. In particular, the large size of the INA protein aggregates can organize a large volume of the surrounding water. Besides, some
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ions or salts present in the cellular dust can also interact with water. 3.2.1. Crystallization and melting kinetics The peak size of DSC thermograms can be linked to the kinetics of the sample phase change. In fact, the horizontal peak shape gives information on the duration and temperature spread of the thermal
A
phenomenon. The vertical peak shape gives information, at each temperature or time, on the quantity of energy exchanged between the sample and the DSC oven. A comparison of the different peak sizes and shapes allows an evaluation of the influence of Snomax on crystallization and melting kinetics. As revealed by Figure 1, the crystallization peaks are distributed in a large range across the temperature scale. Peak shapes and sizes are not identical. For large supercooling, peaks are thin and high. However, for small supercooling, peaks are thick and short. Peak size seems to increase quasi-
linearly with the decrease in the Tn. Furthermore, as described before, crystallization kinetics depend on the temperature at which nucleation occurs and could be evaluated from DSC peak shapes and sizes. Consequently, it seems that the addition of Snomax affects the Tn value, which modifies the crystallization kinetics. In contrast, all the melting peaks are superimposed in a quasi-uniform way and with a similar peak shape and size. The reproducibility of the shape, size, and position on the temperature scale of all these peaks demonstrates that the Snomax does not affect the melting process.
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3.2.2. Equilibrium temperature Tm and Tonset
For a pure compound, the melting process starts at the thermodynamic equilibrium temperature
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between the crystalline and the liquid phase. Aqueous solutions mixtures do not have one transition
temperature because of the liquid phase concentration modification correlated with the ice crystallization ratio. Indeed, when ice crystallizes in the solution, a certain amount of water molecules is extracted from the solution. If we consider the crystallization process to be restricted only to water
U
molecules, the amount of water molecules extracted from the solution is then correlated with the
N
crystallization ratio. Because water is the major organic solvent, this extraction of water molecules increases the other product concentrations, and the evolution of the concentration modifies the
A
chemical potential (and water activity) of the resulting liquid. This implies a reduction in the phase
M
equilibrium temperature. Thus, the tendency of water molecules to integrate an ice crystal is dependent on the freezing ratio. In consequence, there is not only one transition temperature but a
ED
range of melting temperatures, the values of which are correlated with the freezing ratio. For a pure water sample, the melting process starts at 0 °C, and the corresponding temperature is measured at the beginning of the melting pic (Tonset). In our highly diluted solutions, it would be incorrect to analyze
PT
only Tonset value. We therefore studied the evolution of Tonset and the end of melting temperature Tm
A
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because they can demonstrate a cryoscopic effect in aqueous solutions.
Figure 5: SmC influence on ice melting temperature, Tm and Tonset, during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. Green columns represent average Tm values with standard deviation. Blue columns represent average Tonset values with standard deviation.
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No particular influence of the SmC
was observed on Tm. The mean values remain close to +3.5 °C (Figure 5). This
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confirms that the melting process is
not affected by the presence of Snomax. Overlapping melting peaks, presented in Figure 6, seem to confirm these findings. Almost all
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Tonset values are close to +0.3 °C. A small decrease in the obtained values was revealed for the two
N
highest SmC. For 103 and 104 mg L-1, Tonset was obtained at 0.0 and –0.3 °C, respectively. Thus, no cryoscopic effect was detected on Tm, but we wondered whether this effect exists on Tonset for these
A
two values unless this decrease is explained by a pre-melting effect.
M
3.2.3. Pre-melting phenomenon extent
A pre-melting phenomenon is a melting process that occurs before the phase equilibrium temperature.
ED
This effect is correlated to the amount of the ice surface that is in contact or interacts with solid materials. Wilson et al.[98] discussed several possible explanations of the pre-melting phenomenon in aqueous solutions during DSC analysis, such as long-range and short-range hydration forces. For a pure
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water sample, this effect is often negligible and difficult to detect. On the contrary, the Snomax induces an increase in the surface contact between water molecules and Snomax materials, which can magnify
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the pre-melting phenomenon.
Selections extracted from thermograms are presented in Figure 6. This enlargement illustrates the beginnings of the melting processes of the different samples, after alignment and stacking of thermograms. The finding demonstrates that an apparent superimposition of peak shapes with
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melting starts before 0 °C for all data. This pre-melting effect seems to be more obvious than that which seems to demonstrate the corresponding Tonset reported in Figure 5. The difference is a result of the determination method of Tonset with two tangent lines. Nevertheless, we notice a clear difference at the start of the melting process for the solutions containing 104 and 103 mg L-1 (and a few also for 102 mg L-1). The Pyris software functionality: “%Area” allows the calculation of the pre-melting phenomenon amount under 0 °C. It is expressed as the surface area percentage of the entire peak with
temperature. We obtained a surface area percentage under 0 °C as follows: less than 2.5% for 104 mg L1
, less than 0.6% for 103 mg L-1, and less than 0.25% for 102 mg L-1. We also observed a decrease in the
baseline leap (corresponding to a change of the sample “mass-specific heat capacity”) for the highest SmC. From our measurements, the pre-melting effect is not negligible for a large amount of Snomax. Thus, at a concentration of 103 mg L-1, we will consider the Snomax influence on the pre-melting process as insignificant. Figure 6: SmC influence on DSC thermogram melting peaks during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 1010-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. All thermograms were aligned from –30 to –20 °C and then
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4;
stacked on the same line. Only one of the thermograms, obtained for each solution is shown for easier reading.
The
Tonset
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calculation
method (with two tangent lines) is
M
A
N
U
designed to ignore the noise and the low
pre-melting
process
that
always occurs in DSC experiments. In
consequence,
ED
the reduction of Tonset values obtained for 103 and 104 mg L-1, in contrast to the other values, cannot be explained only by the pre-melting phenomenon. In fact, the major endothermic form of the leading
PT
edge of the peak seems to shift toward lower temperatures for these two values as compared to the other values. This argues for the existence of a perceptible cryoscopic effect for these two
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concentrations.
3.2.4. Phase change enthalpy calculation During a phase change, the system evolves into another molecule conformation, which minimizes the Gibbs free energy of the system. This operation consumes or releases thermal energy. Energy is
A
consumed during melting and released during crystallization. The amount of energy exchanged is named the “transition latent heat” (Lf) and corresponds to a phase change enthalpy (ΔH). This amount of energy is correlated to the amount of water molecules (mass) involved in the transition. In consequence, bonded water molecules isolated from the bulk solution can become unfreezable and will thus not participate in the phase change enthalpies of crystallization and melting (ΔHc and ΔHm)[99,100].
Figure 7: SmC influence on enthalpy variations measured
during
crystallization (ΔHc) and melting (ΔHm) processes, calculated from the peak area. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 104;
10-3; 10-2; 10-1; 1; 10;
+10
°C
min-1.
WR:
°C
min-1.
Blue
columns
represent
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2.5
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102; 103; 104 mg L-1. CR: –
average ΔHm values with standard deviation. Red columns
represent
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average ΔHc values with
N
standard deviation.
ΔHc and ΔHm obtained for different SmC are presented in Figure 7. No correlation between the ΔH m
A
obtained and the SmC is apparent. All data are close to 335 J g-1, with one exception for the solution
M
of 104 mg L-1 concentration, which exhibits a small decrease in this value (331 J g-1). Indeed, the amount of energy released during the endothermic melting process is proportional to the amount of water
ED
molecules concerned with the phase change. This amount of “concerned molecules per gram of solution” is reduced when the SmC increases. In fact, for a sample of 5 mg of the 104 mg L-1 solution for example, there are fewer water molecules than those in 5 mg of pure water. This reduction is
PT
estimated to be around 1% of the water mass proportion in the sample (since 104 mg of Snomax in 1 L of water corresponds to the same mass of Snomax in 106 mg of water; thus the sample water mass
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proportion is 106 / (106 + 104) = 0.99). In consequence, the corresponding ΔHm should be reduced to 333.4 x 0.99 = 330.1 J g-1. For lower SmC, the influence of that effect can be neglected. In contrast, for crystallization, when Tn decreases, a small decrease in the ΔHc value is observed (from
A
306 to 298 J g-1). Interpretations are not straightforward for several reasons: crystallization peaks areas at 0, 10-6, 10-5, and 10-4 mg L-1 were not correctly computed because of the following reasons: exceedance of the DSC data range; a difference between the cooling and the warming rates applied to record peaks increases difficulties in comparison; and Lf varies with the temperature. To allow more accurate comparisons, we conducted other experiments with protocol 2. We chose a larger SV (10 µL) to increase result resolution, with an exception for pure water on cooling for which an SV less than 4 µL is required to prevent the data range heat-flow exceedance (that truncates the
peak). Thus, for pure water, 3 µL SV thermograms (n = 3) were analyzed for the crystallization process calculation and 10 µL SV thermograms (n = 3) for the melting process calculation. The same cooling and warming rate was configured (± 2.5 °C min-1) to enable to conduct comparison analysis. Results are given in Figure 8. During warming, the absence of variation on the calculated ΔHm (≈ 332 J g1
) also argues for an absence of impact of the Snomax on the melting phenomenon process. During
cooling, based on the evolution of the latent heat with temperature[78], we compared measured values to the expected values calculated at the corresponding temperatures. To realize this calculation,
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we used the interpolation of Boutron’s data[2].
We did not obtain a good correlation between our experimental ΔHc and the corresponding Lf
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calculated at the same Tn temperature. This may be because our experimental values were obtained
from a DSC peak area determination, while calculated Lf originates from a theoretical link[78] between the cp values of both phases and Lf. Nevertheless, during calibration, this effect is considered. Thus, the
Figure 8: SmC influence on enthalpy variations measured during crystallization (ΔHc) and melting
(ΔHm)
revealed
by
processes peak
as area
calculation. Protocol 2. n = 3. SmC: 0; 10-2; 1; 10; 103 mg L-1. CR: –2.5 °C min-1. WR: +2.5 °C min-1. SV: 10 µL. For pure water: SV: 3 µL on cooling and 10 µL on warming. This exception is due to the exceedance of the DSC data range heat-flow during crystallization of more
than
supercooling.
4
µL Blue
for
large
columns
represent average ΔHm values with standard deviation. Green columns represent average ΔHc
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ED
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A
N
U
absence of correlation needs to be explained by other reasons.
values with standard deviation. Grey columns represent calculated Lf = f(T) data, calculated from interpolation of Boutron’s data[2].
The irreversibility of the crystallization process has already been advanced to explain differences between measured ΔHc and the corresponding Lf[101]. Contrary to a reversible transition, the experimental crystallization latent heat can be reduced because of defects present between ice crystals. The number of defects is influenced by the amount of supercooling when crystallization
occurs[101,102]. These defects imply that a certain amount of water will not participate in the phase change. Furthermore, the presence of defects implies a cost of energy that will not be released by the crystallization process. The presence of these defects also implies an increase in the sample entropy, involving an increase in the cp of ice[101], and indeed a decrease in ΔHc. In consequence, the larger the amount of supercooling is, the more ΔHc will be reduced. On the other hand, during warming, defects could be annihilated because of the reorganization of water molecules at a higher temperature (without any DSC detection before melting peaks). Thus, the corresponding ΔHm will not be affected
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by the temperature at which crystallization occurred (Tn).
Moreover, for large supercooling, the rapidity of the freezing process exceeds the power
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compensation provided by the DSC. This involves slight warming of the sample (particularly observable for Tn-group 4 in Figure 1). In this case, the cooling speed that occurs in the sample does not correspond
to the setting. Because the cooling speed needs to be known to analyze the peak area, the ΔHc could be inaccurate. Besides, the transition temperature could occur at a temperature warmer than T n,
U
whereas Tn is used as a reference to compare measured exchanged enthalpies and Lf. This effect could
N
also lead to a reduction in crystal defects. As a result, and as reported for Bertolini et al. values[103] in Cantrell’s discussion[101], the corresponding measured ΔHc could be higher than the corresponding Lf
A
calculated at Tn.
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Adding Snomax to water creates heterogeneities that increase system entropy. If the cp value of both liquid and ice are affected, all ΔH values would be affected. But no ΔHm variation is observed, even for
ED
large SmC. We therefore assume that this effect can be ignored. Our measurements were performed during ramped temperature variation; thus, the crystallization
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process does not occur at one temperature. This temperature variation causes a bias in the comparison of ΔHc with Lf, because a ΔHc value is obtained from the area calculation in a temperature range, while
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an Lf value is calculated by cp measurement at a specific temperature. During the heat flow calibration, this effect was considered to correlate the peak area measured for a reference standard material to its tabulated value. This calibration was realized on warming, with kinetics specific to the melting process. However, this study focused on crystallization during cooling, which includes variable
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crystallization kinetics. These two factors may impair a precise comparison of the measured value with standard values. To conclude, as opposed to ΔHm, there is no obvious explanation of the variation in ΔHc obtained with the different studied samples. Deveriddy et al.[65] assumed that the effect of P. syringae on the heat release during a DSC experiment is around ±2% for experiments conducted in cryobiological samples. Hu et al.[55] also published data, but we cannot use that data for comparison because their
experimental conditions are different from ours. In our study, we brought to light a few arguments that could explain the observable differences between ΔHc and ΔHm. In practice, we will consider negligible the effect of Snomax on the crystallization of water in solution for concentrations below 103 mg L-1.
4. Conclusion This study indicates that the addition of Snomax to aqueous solution allows the introduction of
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different materials that have specific abilities to induce the heterogeneous nucleation process at a
defined temperature. Three INA subpopulations were described in the Snomax powder (which
originates from P. syringae). Each subpopulation reaches a singular presence probability in the solution
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according to the experimental conditions (concentration and sample volume). We assumed that these INAs subpopulations explained partly our results. With this knowledge, it is conceivable to trigger
crystallization at the desired short range of temperature. To do this, it is required to define the proper
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conditions in which the desired INA subpopulation is highly probable, while all the other more efficient INAs are not. In our experimental conditions (5 µL), we demonstrated that if the presence probability
N
of the first INA subpopulation is high enough, which is reached for an SmC greater or equal to 1 mg L1
A
, the ice nucleation will occur in the range of –4 to –2 °C; if the presence probability of the second INA
subpopulation is large enough, and the presence probability of the first INA subpopulation is low,
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which is reached for an SmC around 10-1 mg L-1, the ice nucleation will occur in the temperature range of –6 to –5 °C; finally, if the presence probability of the third INA subpopulation is high, and the
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presence probabilities of the first and second INA subpopulations are low, which is reached for an SmC between 10-3 mg L-1 and 10-2 mg L-1, the ice nucleation will occur at the temperature range between –
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10 to –7 °C. As expected, the SV influences the presence probabilities of the different INA subpopulations in solution. Nevertheless, small volumes (less than 5 µL in our experiments) seem also
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to favor the deterioration of Snomax INA, especially for large protein aggregates. Consequently, care must be taken to reduce the INA protein aggregate deterioration (sample preparation, shaking, small SV, storage, etc.). Finally, with the use of this kind of INA, no proof was found about time dependence (through the influence of CR) on Tn control.
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To validate the use of this nucleating trigger, it was required to verify whether artifacts or disturbances were induced by the Snomax in solution. Thermodynamic characteristics (Tm and Tonset) of water solutions with Snomax were compared, and no major disturbances were observed below 103 mg L-1 concentration. No variation was observed for the energy released by the melting of ice (ΔHm), which excludes a possible increase in unfreezable water quantity in solution. Our results seem to indicate that the Snomax presence triggers only negligible modifications of the thermodynamic response of the
solutions and these modifications are only measurable for SmC higher than 103 mg L-1. Thus, Snomax effects on the thermodynamic properties seem to be negligible for lower SmC. This paper paves the way for a large Snomax use in DSC experiments for several applications, especially for cryobiological purposes. Indeed, this compound can help to standardize ice nucleation in studies linked to water crystallization. Conflicts of interest: None. Acknowledgments: The authors thank Cindy Morris for providing the Snomax powder and Kevin Laouer for his help during
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his internship.
Funding: This work was supported by a grant from the Veterinary Campus of Lyon (VetAgro Sup, Lyon, France) and the
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Figr-1Figure 1: SmC influence on DSC crystallization and melting peaks during cooling and thawing, respectively, after
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A
N
dilution in pure water (encapsulated in a DSC sample pan). Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 101; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. WR: +10 °C min-1. The term “Endo Up” for heat flow representation implies that a positive exchange (during an endothermic reaction) is represented at the top and a negative exchange at the bottom (exothermic). All freezing thermograms were aligned from –1.5 to +0.5 °C and then stacked on the same line (–2 mW). All thawing thermograms were aligned from –30 to –10 °C and then stacked on the same line (+2 mW). Some thermograms were omitted for easier reading.
Figure 2: SmC influence on Tn during cooling. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1 (logarithmic scale representation). CR: –2.5 °C min-1. Circles represent average (n = 3) data. At a given concentration, each cross represents one of the 3 data. The bold blue line represents the average temperature for pure water samples and the dotted lines show its standard deviation. Turner Classification [80]
Group 1
More than
Type I
Class A
(–4.0 to –2.0 °C)
1 mg L-1
(above –4 °C)
(above –4.4 °C)
Type II
Class B
(–7 to –5 °C)
(–5.7 to –4.8 °C)
Type III
Class C
(–10 to –7 °C)
(below –7.6 °C)
Group 2 10-1 mg L-1 (–6.0 to –5.0 °C) Group 3 (–8.7 to –7.0 °C)
Between 10-3 and 101 mg L-1
Protein Aggregate Repetitions [26,34,81]
Presence Probabilities from Literature [26,34,43,81]
Presence Probabilities from our Experiments
From 50 to 100
Extremely rare
Rare
Undefined
Low probability
Rare
2 to a few
High probability
Highly probability
Isolated value 10-3 mg L-1
1 or dusts
(–13.6 °C) None
(–24 to –17 °C)
Less than 10-4 mg L-1 and without Snomax
0
None
Could happen
Always possible
U
Group 4
IP T
Yankofsky Classification [79]
SC R
Tn-groups
Snomax Concentration (SmC)
A
CC E
PT
ED
M
A
N
Table 1: Tn-groups distribution obtained for the different studied SmCs during the DSC experiment. Protocol 1. n = 3. SV:
5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. Last column: difference in the presence probabilities estimated from the analysis of the influence of SmC on Tn (Figure 2). Figure 3: SV influence on Tn during cooling for different SmCs. Protocol 1. n = 3. SV: 1; 3; 5; 10; 15 µL. SmC: 0; 0.01; 1; 10; 1000 mg L-1. CR: –2.5 °C min-1. Cross represents each value. Circles symbol lines represent average with standard deviations.
IP T SC R
U
Figure 4: CR influence on Tn during cooling for different SmCs. Protocol 1. n = 3. CR: 0,5; 1,0; 2,5; 5,0; 10; 20; 40 °C min-1. SV: 10 µL. SmC: 0; 0.01; 1; 10; 1000 mg L-1. Circles symbol lines represent average with standard deviations.
A
CC E
PT
ED
M
A
N
Figure 5: SmC influence on ice melting temperature, Tm and Tonset, during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. Green columns represent average Tm values with standard deviation. Blue columns represent average Tonset values with standard deviation.
IP T SC R U N
A
Figure 6: SmC influence on DSC thermogram melting peaks during thawing. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. WR: +10 °C min-1. All thermograms were aligned from –30 to –20 °C and then stacked on the same line. Only one of the thermograms, obtained for each solution is shown for easier reading.
A
CC E
PT
ED
M
Figure 7: SmC influence on enthalpy variations measured during crystallization (ΔHc) and melting (ΔHm) processes, calculated from the peak area. Protocol 1. n = 3. SV: 5 µL. SmC: 0; 10-6; 10-5; 10-4; 10-3; 10-2; 10-1; 1; 10; 102; 103; 104 mg L-1. CR: –2.5 °C min-1. WR: +10 °C min-1. Blue columns represent average ΔHm values with standard deviation. Red columns represent average ΔHc values with standard deviation.
IP T SC R U
A
CC E
PT
ED
M
A
N
Figure 8: SmC influence on enthalpy variations measured during crystallization (ΔHc) and melting (ΔHm) processes as revealed by peak area calculation. Protocol 2. n = 3. SmC: 0; 10-2; 1; 10; 103 mg L-1. CR: –2.5 °C min-1. WR: +2.5 °C min-1. SV: 10 µL. For pure water: SV: 3 µL on cooling and 10 µL on warming. This exception is due to the exceedance of the DSC data range heat-flow during crystallization of more than 4 µL for large supercooling. Blue columns represent average ΔHm values with standard deviation. Green columns represent average ΔHc values with standard deviation. Grey columns represent calculated Lf = f(T) data, calculated from interpolation of Boutron’s data[2].