Tunable resistive pulse sensing (TRPS)

CHAPTER 3.1.4

Tunable resistive pulse sensing (TRPS) Yiwen Peia, Robert Vogelb, Caterina Minellia a

National Physical Laboratory, Teddington, United Kingdom School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia

b

Abbreviations DCS DLS DNA FWHM LDL NIST PBS PDMS PTA SEM SMPS TEM TRPS VPM

differential centrifugal sedimentation dynamic light scattering deoxyribonucleic acid full width at half maximum lower detection limit National Institute of Standards and Technology phosphate-buffered saline polydimethylsiloxane particle tracking analysis scanning electron microscopy scanning mobility particle sizing transmission electron microscopy tunable resistive pulse sensing variable pressure module

Introduction Tunable resistive pulse sensing (TRPS) is an analytic technique that allows particle-byparticle detection and analysis of colloids ranging from approximately 40 nm to hundreds of micrometre in diameter. TRPS belongs to a family of analytic techniques, called resistive pulse sensing (RPS), in which colloidal particles are suspended in a conductive solution and pass through a single or multiple pores in a membrane. RPS technology allows high-throughput analysis of particles and biomolecules, and it has been widely applied in biomedical fields since the 1950s [1]. The Coulter counter, also known as a resistive pulse counter, was first created by Dr. Wallace Coulter in 1953 [1] to count and measure size of micrometre objects such as bacteria, cells, and microorganisms. Conventional Coulter counters contain two counterparts filled with aqueous electrolyte, equipped with an Ag/AgCl electrode on each side of the membrane continuously recording ionic current during particle translocation. When an electric potential is applied across the membrane, in the absence of particles, a stable ionic current is measured. If an analyte travels through the pore, it occludes the ionic current, causing drop in current, which is termed a ‘resistive pulse’ due to the replacement of conductive electrolyte solution by the Characterization of Nanoparticles https://doi.org/10.1016/B978-0-12-814182-3.00009-2

© 2020 Elsevier Inc. All rights reserved.

117

118

Characterization of nanoparticles

nonconductive solid particle. The height, width, and frequency of these resistive pulses provide the information needed to determine size, surface charge properties, and total concentration of the analyte [2,3]. As the field of pore fabrication matures, it becomes possible to effectively minimize the pore size from microscale to nanoscale to enable the measurement of single molecule, protein and nanoparticle detections, and analysis of the complex structure of biopolymers, for example, DNA. For instance, submicron particle analysis using Coulter counting was first reported by DeBlois and co-workers in the 1970s [4]. In those studies, they exploited individual submicron pores etched in plastic sheets to analyse a range of nanoparticles including viruses with an average diameter of approximately 60 nm and polystyrene nanospheres 90 nm in diameter. The first studies of single-stranded DNA detection using Coulter-based resistive pulse sensing was documented by Kasianowicz et al. in 1996 [5], in which a biological α-hemolysin pore (ca. 1.4 nm at its narrowest point) was used to discriminate slight differences in the size of the four DNA base pairs. Since then, there has been significant interest in the field of nanopore-based DNA sequencing [6,7]. Compared with biological pores, solid-state ones generally afford more chemical versatility. A large selection of materials including graphene, carbon nanotubes [3,8,9], glass [10], silicon [11], PDMS [12], and polycarbonate [13] have been utilized as the pore substrate for particle analysis. Although RPS techniques have stimulated scientist globally to develop synthetic procedures for fabricating both biological and solid-state pores and to find applications in DNA, proteins, and nanoparticle sensing, this is not our focus here as the review articles found elsewhere [8,13–24] have covered these topics in depth. The focus of this chapter is the use of TRPS technique for nanoparticle characterization and to provide a practice guide to users.

TRPS A unique feature of TRPS is that the pore substrate is an elastomer. The nature of the material allows the membrane to be reversibly stretched on a millimetre length scale that allows mechanical alteration of the size of the pore at the nanoscale level. These tunable pores offer significant advantages over the static ones in solid state and biological membranes. In TRPS, the elastomeric pore size can be tuned in real time to suit a wide range of analytes of interest. Moreover, tunable pores allow effective recovery from a potential blockage during data capture as the membrane can be reversibly stretched. Furthermore, the signal-to-noise ratio of the resistive pulse signals can be manually optimized during the setup of the instrument as a strategy to improve measurement sensitivity, for example, applying optimized stretch, voltage, and pressure to the analyte of interest. TRPS therefore offers a level of flexibility, which is not typically achievable with static pores used for RPS. Since the first report in 2007 [25], TRPS (initially known as scanning ion occlusion spectroscopy) has evolved as a major particle characterization technique [26], and several TRPS protocols have now been established to perform precise and accurate measurements of particles size [27–40], number concentration [26,41–47], and surface charge

Tunable resistive pulse sensing (TRPS)

[37,40,48–55]. Currently, all TRPS apparatus in the market are available from a New Zealand-based company, Izon Science Ltd. They supply a range of commercial TRPS apparatus (i.e. qNano and qViro instruments) equipped with a specially designed variable pressure module (VPM) to precisely control the translocation of nano-/microparticles during analysis. Other commercial RPS-based apparatus for nanoparticle analysis include these microfluidic devices produced by Spectradyne LLC using static pores. The currently available TRPS apparatus is composed of two-vertically located fluid reservoirs filled with aqueous electrolyte, each containing an electrode and connected by a small conical pore, as shown in Fig. 1. The conical pore shape gives rise to a

Fig. 1 Typical TRPS apparatus and tunable elastomeric pore specimens. (A) Schematic diagram of particles travelling through a tunable conical pore, indicating important parameters used in TRPS technology. Lower left and right, typical experimental data showing resistive pulse signals and one individual pulse on an expanded time scale. (B) An image of the tunable pore membrane (left), 3D image developed from confocal microscopy of an elastic pore membrane (upper right) and SEM images of two pore openings of different sizes (lower right, scale bars are 1 and 20 μm, respectively). (C) The qNano TRPS apparatus (Izon Science) with the magnified fluid cell and the location of pore membrane. ((A) and (B) Reproduced from E. Weatherall, G.R. Willmott, Applications of tunable resistive pulse sensing, Analyst 140 (2015) 3318–3334 with permission from The Royal Society of Chemistry. (C) Reproduced from E.L.C.J. Blundell, L.J. Mayne, E.R. Billinge, M. Platt, Emergence of tunable resistive pulse sensing as a biosensor, Anal. Methods 7 (2015) 7055–7066—published by The Royal Society of Chemistry.)

119

120

Characterization of nanoparticles

resistance gradient profile through the pore with a focused electric field at the small pore opening, reducing the sensing zone and enhancing measurement sensitivity. When passing through the conical pore, analytes are detected as a change of the resistance across the pore, which is measured as a change in ionic current. In TRPS, each resistive pulse signal (typically a drop of ionic current) is known as a ‘blockade event’. The conical nature of the pore results in an asymmetric resistive pulse signal where the highest resistance occurs at the smallest pore opening. The transit of the analyte causes a sharp drop in current, which then tails back towards the baseline value as the resistance diminishes towards the bottom of the pore (see Fig. 1A). The blockade magnitude and blockade signal duration can be used to simultaneously determine the size and surface charge of the analyte, whilst the frequency of the blockade events relates to the concentration of the sample. Tunable pores in TRPS systems need to be calibrated before one can measure the analyte particles. Commercially available, spherical, monodisperse polystyrene beads (e.g. CPC100 from Izon Science) are typically used as calibrants. The calibration process must be done in conjunction with sample measurements under the same conditions (stretch, pressure, and voltage). Pioneering work from Vogel, Willmott, Kozak, and Trau [38,43,54,56–58] on physical modelling and data interpretation set the cornerstone for the TRPS technology. These physical models typically take transport mechanisms such as convection, electro-osmosis, and electrophoresis into account and also describe the calibrant properties. Several TRPS protocols [26,38,44,51,54] have been developed based on these models to precisely measure particle size, charge, and concentration. TRPS has been exploited to detect both organic [38,44,59] and inorganic particles [29,37,53], excellular vesicles [36,41,42,45,55,60], liposomes [33,55,61], DNA-coated particles [48,55], and other materials [27,49,62]. The accessible range of particle size spans from ca. 40 nm to the size of cells (ca. 1–100 μm), covering more than three orders of magnitude in length scale. The range of particle concentrations measured using TRPS is ca. 105–1012 mL
1. A large selection of aqueous electrolytes can be used with a typical salt concentration around 100 mM. The high ionic strength, however, means that some colloids (typically metallic particles) are more likely to agglomerate due to a reduction in length of the electrical double layer and its related reduction of effective surface charge. Tunable pores are fabricated by puncturing a thermoplastic polyurethane membrane using a chemically etched tungsten needle. Conical pores are produced in the ca. 200–300 μm thick circular septum located in the centre of a membrane (Fig. 1B). All membranes are then tested for the pore size and classified based on the size range of the particles that can be detected. The smallest pore currently commercially available is the NP80 that has an analysis range of 40–250 nm. A typical NP80 membrane is defined, as an example, as a nanopore that can detect particles with approximately 80 nm in diameter when applying 45 mm stretch, and the mean measured blockade magnitude is 0.3 nA at a baseline current of 100 nA.

Tunable resistive pulse sensing (TRPS)

In a typical TRPS measurement, the cross-shaped tunable pore is mounted by eyelets to teeth on the instrument above the lower fluidic well, as shown in Fig. 1C. The membrane is then stretched biaxially and symmetrically by adjusting the distances between teeth in the eyelets on the ends of the arms using the stretch adjustment handle. It has been shown that applying a stretch of 10 mm decreases the membrane thickness by roughly 15% whilst increasing the pore size by 54% [18]. Since there are limitations to how much each membrane can be stretched, that is, typical value between 43 and 47 mm, it is important to select a membrane with a suitable pore size to match the analyte of interest. Overstretching a membrane may cause rupture of elastomeric pores and therefore adversely effects the TRPS measurements. The pressure difference between the upper and lower fluid well, indicated as P1 and P2 in Fig. 1A, can be controlled and altered by the variable air pressure module (VPM, Izon Science). This provides pressure and vacuum control to the TRPS instrument with a scale of 0–20 cm, where each centimetre is equivalent to applying ca. 100 Pa to the system [54]. The hydrostatic pressure between top and bottom fluid cell due to gravity is typically smaller than 40 Pa but needs to be considered in the total applied pressure. The VPM allows users to tune convection forces in comparison with electrokinetic forces (electro-osmosis and electrophoresis) to control particle motion. For concentration measurements, convective particle motion is set to dominate over electrokinetic particle motion, whilst for ζ-potential measurements, the opposite is the case. The VPM can also be exploited to obtain an optimized particle count rate during analysis. It is recommended to obtain particle rates typically between 200 and 1500 particles per minute for reliable and reproducible TRPS measurements.

Instrument setup The TRPS setup procedure is well documented. There are a few articles [63,64] containing some useful tips on this topic. Users can also find some protocols within the user manuals and through the useful trouble-shooting methods available online from Izon Science Ltd. The protocols presented here are those we adopt as part of best practice at the National Physical Laboratory (NPL). To start, apply 75 μL of aqueous electrolyte to the lower fluid well for a few minutes and then remove it again. This wetting process reduces the risk of air bubble formation in the lower fluid well when the pore is in position. The pore membrane is then mounted to the teeth via the four arms and the desirable stretch (ca. 47 mm) is applied. The electrolyte (75 μL) is carefully placed in the lower fluid well without introducing any air bubbles and avoiding spillage of the salt solution on the membrane. When the upper fluid well is in position, the user should check the baseline current at an applied voltage before adding 35-μL electrolyte in the upper fluid well. The stable baseline should not exceed approximately 1 nA. If the baseline is drifting or unstable, it suggests that there is electrical contact between top and bottom electrode,

121

122

Characterization of nanoparticles

possibly caused by a fluid leak or salt bridge. If this is the case, the edges of the bottom fluid cell should be wiped and any liquid at the edges removed. If the system remains unstable, the instrument needs to be checked and cleaned thoroughly before restarting the setup procedure. Once a small and stable current is obtained, the electrolyte can then be added into the upper fluid well, and the Faraday shield should be placed over the fluid cell to reduce the background noise. Before measurements, it is important to establish optimal setting conditions. Typical values for applied membrane stretch are 45–47 mm, and the applied voltage should not exceed 1.6 V. In these conditions, the baseline ionic current should be between 120 and 150 nA with the root mean square (RMS) noise of <10 pA. A variety of aqueous electrolytes can be used, the most common ones being sodium chloride and phosphate-buffered saline (PBS) with physiological buffering range. In general, a salt concentration of ca. 10–400 mM is sufficient for most of the analysis, whereas low molarities such as 10 mM are used for large particles (>1000 nm) and large pores. In the case of particles smaller than 100 nm in size, measurement sensitivity can be improved by increasing the concentration of the electrolyte (e.g. to 150–400 mM). An increase in ionic strength will increase both blockage magnitude and baseline current and maximize the achievable signal-to-noise ratio. A higher ionic strength electrolyte may also allow for a lower applied voltage that may reduce RMS noise for a given current and stabilize the baseline current. Nonetheless, a higher salt concentration may weaken the repulsive electrostatic forces between charged particles and therefore increase the risk of particle agglomeration. Users should select the concentration of the electrolyte depending on the analyte of interest and the desired experimental conditions and outcomes. Preparation of the elastomeric membrane is of primary importance to successful TRPS measurements. Full wetting of the pore, without any trapped air bubbles, is also essential to achieve accurate and repeatable TRPS measurements. If the signal trace is low and unresponsive when applying voltage and stretch, it suggests that there may be air bubbles trapped within the pore (even where there is no visible bubble in the upper or lower fluid well). In this case, user should increase the stretch (up to 47 mm) whilst applying pressure, accompanied with occasional ‘clicking’ using the Faraday shield to get rid of the trapped air bubbles. It is not uncommon for users to come across pore blockage during the TRPS measurements, especially for agglomerated particles and complex biological samples. To ensure accuracy of TRPS measurements, it is important to identify the occurrence of pore blockage and unblock the pore in a timely manner. A blocked pore can manifest itself in a nonlinear particle rate trace during sample recording or a decrease in the baseline current as compared with the expected value. If in doubt, it is a good practice to carry out three consecutive measurements at variable pressure under identical conditions to determine the pore quality. For an unblocked pore, the particle count rate should linearly increase with increasing applied pressure. To dislodge trapped particles, users can pause the data collection and then gently pipette to mix the upper electrolyte/sample or gently

Tunable resistive pulse sensing (TRPS)

tap the Faraday shield of the instrument or stretch and open the pore whilst applying a pressure/vacuum to effectively force the electrolyte and blockage through. If the pore blockage cannot be resolved by these steps, it might be helpful to refresh the analyte suspension and to make sure samples are well dispersed with an appropriate concentration. It should be noted that if pore blockage occurs during measurements, stretching of the membrane is not recommended. However, preconditioning of the membrane will help restore the pore geometry when the same membrane stretch is applied. TRPS measurements become increasingly challenging when the particle size is below 100 nm. In this case, it is recommended to ‘coat’ or ‘condition’ the membrane using the commercially available TRPS Reagent Kit (Izon Science), which contains a coating agent, a wetting agent, sodium azide, and PBS prior to any sample measurements, in particular of biological samples. These steps can reduce any potential particle–membrane interactions and therefore prevent pore blockage. Additionally, care needs to be taken during the sample changeover because this is the most common place where blockage could occur, especially when concentrated electrolytes are in use. It is critical that the top surface of the pore stays hydrated between recordings and changeovers of analyte suspensions should be done efficiently to avoid pore drying. A list of trouble-shooting methods is shown in Table 1.

Measurements of particle size In most of TRPS analysis, particles are modelled as spheres, and particle sizes refer to diameter of their spherical equivalence. However, it is also possible to analyse nonspherical particles, including viruses [30], bacterial chains [27], and self-assembled aggregates [32], using the semianalytical model. For instance, Matt et al. [35] has demonstrated that individual and aggregated rodlike particles possess distinguished blockade magnitude and blockade full width at half maximum (FWHM), which can be used to extract their size information. TRPS offers high-resolution size distribution measurements [37,65]. For instant, it is reported [37] that TRPS technique can be used to quantify the populations of agglomerated particles, that is, dimers and trimers, in biological media (Fig. 2B). A number of studies have used TRPS in parallel with other particle sizing techniques, including dynamic light scattering (DLS), differential centrifugal sedimentation (DCS), particle tracking analysis (PTA), and scanning and transmission electron microscopy (SEM and TEM). A comparison work from Bell et al. [29] shows the general agreement in the TRPS and TEM size and size distribution measurements of monodisperse samples of five submicron St€ ober silica nanoparticles (see Fig. 2A). More recently, the same group [53] made an experimental comparison of TRPS with electrophoretic light scattering (ELS), DLS and DCS to study size, and size distribution and surface charge of plain and aminated silica nanoparticles in different media. Although all techniques give consistent results for the size measurements of particles in Tris buffer, TRPS is the only technique that can

123

124

Characterization of nanoparticles

Table 1 TRPS best practice guide—problem, possible cause, and mitigation Problem

Possible cause

Mitigation

1. Blockades not visible

• Pore blockage

• Mix or refresh the fluid in the upper and lower wells

• Gently tap or use the plunger to apply a continuous pressure to the fluid cell

• Sample concentration too low or too high • Incorrect pore stretch

• Clean the pore and dilute sample accord•

2. Blockade magnitude too small

• Unsuitable mea-

3. Nonlinear rate plot

• Partially

•

blocked pore • Loss of applied pressure

•

• Trapped air bubbles

•

• Electrolyte leakage

•

• Partially

•

4. Fluctuating or drifting baseline current

5. Sharp current drop

6. RMS noise level >20 nA

surement settings

• • • •

blocked pore

• Trapped air bubble

•

• Trapped air bubbles

•

or partially blocked pore • Electrical interference

•

• Electrolyte leakage

•

• Blocked pore

•

ingly; if the resulting particle rate is too low, then apply pressure Slowly increase to 47 mm and then reduce the applied stretch, for example, 0.5 mm per step, until the blockades become visible Increase voltage (up to 1.6 V) Decrease pore stretch (<43 mm) Decrease pressure For calibration standards a mean blockade magnitude with respect to baseline current (dI/I) of at least 0.0025 is recommended Increase stretch by 0.5–1.0 mm, refresh the fluid in the upper well and apply the plunger Disconnect the VPM from the upper well and mix fluid in the well before reconnecting the VPM nozzle Refresh fluid in the upper well; If the current is still unstable, replace the electrolyte in the lower fluid as well Carefully check both the membrane and the fluid cells; remove spillage with a lint-free tissue Mix or refresh the fluid in the upper well; clean or replace the pore if the current remains low Gentle tap on the Faraday cage and/or refresh the fluid in both upper and lower well Use the plunger to apply a continuous pressure to the system whilst gently tap the Faraday cage Identify the cause by systematically checking nearby electrical equipment, that is, power supply and laptop battery packs Ensure that both lower and upper fluid cells are free of spillage Mix or refresh the fluid in both wells; clean the pore thoroughly and ensure sample is well dispersed

Tunable resistive pulse sensing (TRPS)

40

TEM

400 nm

TEM

30

SMPS DLS TRPS NTA DCS

20 10 30

0

DLS

50

100

150

200

Diameter (nm)

(A)

Proteins

0.8

SiO2

0.6 0.4

0

TRPS

20

10

0

6

PTA 4

Norm. distribution

2

1

0.8

0

0.6

0.2

0.2

0.4

z-Potential (mV) Norm. distribution

TRPS 1

10

Number-based PSD q0(X) (%)

0

Number-based PSD q0(X) (%)

20

0

20

DCS

–15 –35

10

–55 0

–75 0

(B)

0

50 100 150 200 250 300 350

Diameter (nm)

(C)

100

200

300

400

500

600

700

800

Equivalent diameter x (nm)

Fig. 2 High-resolution size distribution measurements by TRPS and comparison of TRPS with other particle characterization techniques. (A) Mean diameter value of monodisperse silica nanoparticles measured by TRPS, TEM, scanning mobility particle sizing (SMPS), DLS, NTA (or PTA), and DCS [29]; (B) size and ζ-potential distribution of plain silica particles in Tris buffer and in serum solution measured with TRPS; (C) size distribution of multimodal polystyrene particles obtained by TRPS, TEM, DLS, DCS, and PTA (equivalent to NTA) [28]. ((A) Adapted with permission from N.C. Bell, C. Minelli, J. Tompkins, M.M. Stevens, A.G. Shard, Emerging techniques for submicrometer particle sizing applied to Stober silica, Langmuir 28 (2012) 10860–10872. Copyright 2018 American Chemical Society. (C) Reproduced from W. Anderson, D. Kozak, V.A. Coleman, Å.K. Ja€mting, M. Trau, A comparative study of submicron particle sizing platforms: accuracy, precision and resolution analysis of polydisperse particle size distributions, J. Colloid Interface Sci. 405 (2013) 322–330.)

provide sensitive and high-resolution measurements to study particle properties in biological media (see Fig. 2B). These techniques are often used together for size comparison studies whilst each of them provides distinct complementary size information. For examples, SEM and TEM are vacuum techniques and typically used for characterizing dry powders whilst the other techniques measure particles in liquid. DLS measures the hydrodynamic size of the particles, whilst TRPS measures the particle volume impenetrable to the ions in solution. Anderson et al. [28] have carried out a quantitative comparison study of TRPS with DLS, NTA, DCS and TEM for particle size distribution measurements using both monodisperse and multimodal polystyrene dispersions, as shown in Fig. 2C. Using a mixed sample containing monodisperse, submicron (nominal modal size: 220, 330 and 410 nm) particles, they found that only TRPS and DCS provide

125

126

Characterization of nanoparticles

sufficient size resolution to resolve all three particle populations and the size distributions obtained are consistent with the ‘reference’ TEM measurements of individual particles. The relationship between the particle volume and magnitude of the resistive pulse ΔR generated by a TRPS instrument is linear, and hence, the diameter of the sphere with equivalent volume impenetrable to ions can be determined with high accuracy (Eq. 1). For example, doubling the diameter of a spherical particle causes an eightfold increase in resistive pulse magnitude, resulting in high sensitivity to differences in particle size. For a spherical particle and a cylindrical pore, the simplified relationship is ΔR d3 ¼ 2 R DL

(1)

where R is the pore resistance, D is the pore diameter, d is the particle diameter, and L is the effective pore length (which is the geometric length + 0.8 D) [66]. This represents the simplest case of a spherical particle and a cylindrical pore. For more complex particle and pore geometries, the expression is more involved [18]. For a conical pore with a length L, Heins et al. [67] first calculated the change in the resistance (ΔR) using Eqs. (2), (3): Z L dz ΔR ¼ ρ
R (2) 0 AðzÞ R¼

4Lρ πDL DS

(3)

where AðzÞ is the unobstructed cross-sectional area of the pore perpendicular to the pore axis z; R is the resistance of the empty pore; DL and DS are the largest and the smallest pore diameters; and ρ is the electrolyte resistivity, which is assumed to be homogeneous throughout the pore. Vogel and co-workers [38] first described the linear relationship between the maximum change in electric resistance across the pore during particle transit (ΔR) and the particle volume in TRPS analysis. Blockade magnitudes are usually recorded as a change in ionic current jΔIj and the conversion between ΔI and ΔR is performed using Eq. (4): ΔI ΔR ¼ (4) I ΔR + R where I and R are the steady-state baseline current and the overall resistance, respectively. It is worth noting that this relationship is independent of pore shape and the difference between ΔI/I and ΔR/R is typically <10% [38]. To guarantee accurate and reliable measurements of particle size, the pore is calibrated with calibrant particles traceable to a National Institute of Standards and Technology (NIST) standard of certified size.

Tunable resistive pulse sensing (TRPS)

For example, calibrant particles CPN (from Izon Science) are NIST-traceable polystyrene nano-/microspheres, with their sizes being compared with certified NIST polystyrene nano-/microspheres (Standard Reference Material 1963, 1691 or 1690). This becomes particularly important when analysing samples that contain multimodal or aggregated populations.

Measurements of particle number concentration The particle flux ( J) is related to the particle number concentration (C) and the velocity of particles through the pore (υ) via the Eq. (5): J ¼Cυ

(5)

The Nernst–Planck equation [57] has been widely used to describe the motion of a particle in liquids by considering transport mechanisms acting upon it. The three dominant transport mechanisms for TRPS are electrophoresis (ep), electro-osmosis (eo), and convection (con). Electrophoresis and electro-osmosis are both electrokinetic effects that are caused by the voltage applied to the system and the charge on the pore surface. Electrophoretic mobility is related to the movement of charged particles, whilst electroosmosis force is caused by the movement of the dispersion medium. Convection is fluid flow resulting from the pressure difference across the pore. The particle flux J (typical units: particles m
2 s
1) can take the form   J ¼ Jep + Jeo + Jcon    (6) ¼ C υep + υeo + υcon where υ includes the velocity contributions from electrophoresis, electro-osmosis, and convection. When convection is the dominant transport mechanism, Eq. (5) is equivalent to BF ¼ C  Q, where BF (blockade frequency) is the particle count rate, C is the particle concentration, and Q is the fluid flow rate [66]. Q is proportional to pressure, and hence, the particle count rate is proportional to both the particle concentration and the applied pressure [16]. A plot of blockade frequency (BF) versus applied pressure (P) gives a gradient proportional to C. For a pore of unknown length and diameter, the use of a calibration standard of known concentration and size allows the unknown concentration and size distribution of the sample to be calculated. No certified particle number concentration standards currently exist, and the development of such concentration reference materials remains one of the challenges for achieving traceability in the concentration measurements. Most accurate concentration results are obtained when using a multipoint calibration procedure where particle rates for sample and calibration are measured at three or more pressures.

127

128

Characterization of nanoparticles

In their seminal paper, Willmott and co-workers [26] developed a TRPS protocol to calculate concentration of an unknown analyte, set by calibrating the pressure-driven flow using calibrants with known concentration. For pressure-driven measurements, they found that the blockade rate (unit: particles s
1) is linearly proportional to the applied pressure and independent of particle type, including size and surface charge variations. Using 100 nm carboxylated polystyrene particles, they demonstrated that this linear relationship is valid for a range of membrane pressures (between 0 and 1.8 kPa) and particle concentrations (between 7  108 and ca. 5  1010 mL
1). This approach has been exploited to measure the concentration of both synthetic and biological nanoparticles [44,45]. Alternatively, an elaborated calibrant-free approach permits measurement of the particle concentration using the geometric parameters of the pore [43]. In addition, Broekman et al. [41] described an internal calibration technique, which is suitable for samples in complex biological media. Fig. 3A shows the capability of TRPS to perform high-resolution size and number concentration distribution measurements using a mixed sample containing monodisperse polystyrene particles with nominal modal sizes of 110 and 210 nm in diameter. It was found that the measured concentration ratio between the two populations is in very good agreement with the expected value over a range of relative concentrations (unpublished results). In TRPS, like in other particle technologies, the measured ‘total’ particle concentration depends on the probed size range and lower detection limit of the instrument. Hence, in TRPS, it is critical to select a membrane to match the particle population of interest, especially for polydisperse analytes. It is also customary to report TRPSmeasured particle concentrations accompanied by the size range accessed. This is clearly illustrated by Vogel and co-workers [45] in a careful study on extracellular vesicles, where their measured number concentration and size is shown to be depend upon the choice of the pore size (Fig. 3B). The mode of the size distribution is shifted to a smaller particle diameter when using a smaller pore [Fig. 3B, in green (light grey in the print version)] with a lower detection limit (ca. 50 nm) compared with a larger pore [Fig. 3B, in red (grey in the print version)]. Measurement with the smaller pore also results in a significant increase of measured particle concentration within the size range between 80 and 250 nm. For highly polydisperse samples, it is not uncommon and sometimes essential to carry out TRPS concentration measurements with a range of pore sizes. Using three different pore membranes and multiple pore stretch settings, as illustrated in Fig. 3C, the concentrations of polydisperse phospholipids microbubbles whose sizes ranged from ca. 100 to 5 μm was measured with high resolution by TRPS.

Measurements of particle ζ-potential Like DLS (see Chapter 3.3.1), TRPS has the useful feature to measure particle ζ-potential on a particle-by-particle basis and simultaneously with their size. A particle’s ζ-potential is related to its surface charge and is defined as the potential difference between the

Tunable resistive pulse sensing (TRPS)

2.5´109 Ratio: 2.6 (3)

Ratio: 1.17 (1)

Concentration (/ml)

2.0´109

Ratio: 0.11 (0.11)

TRPS

TRPS

1.5´109

TRPS

50:50

1.0´109 5.0´108

10:90

75:25

0.0 0

50

100

150

200

250

300

0

50

100

150

200

250

300

0

50

100

150

200

250

300

Diameter (nm)

(A)

1011 8.0´10

9

1010

Concentration (/ml)

Concentration (/ml)

C80–250 6.0´109

4.0´109

2.0´10

109

108

107

9

106

105

0.0 0

(B)

50

100

150

200

250

Diameter (nm)

300

350

400

0

(C)

500

1000 1500 2000 2500 3000 3500 4000 4500 5000

Diameter (nm)

Fig. 3 (A) TRPS particle-by-particle size and concentration distributions of mixtures of 110 and 210 nm polystyrene spheres in different proportions. Expected number concentration ratios (from left to right): 3, 1, and 0.11. Measured ratios (from left to right): 2.6, 1.17, and 0.11 (unpublished results); (B) TRPS size distribution of extracellular vesicles measured using tunable pores of two different lower detection limits (DL). The size distribution in green (light grey in the print version) was recorded with a pore whose LDL is at approximately 50 nm, whilst the one in red (grey in the print version) was measured using a larger pore with a LDL of around 80 nm. C80–250 represents the measured number concentration of particles in the size range between 80 and 250 nm. The blue-dashed lines (dark grey in the print version) and the subscript number range indicate the size range over which particle number concentration was evaluated [45]; (C) TRPS number concentration measurements of polydisperse phospholipids microbubbles using a range of pore size membranes and stretching setting (unpublished results). ((B) Reproduced from R. Vogel, F.A.W. Coumans, R.G. Maltesen, A.N. Bo€ing, K.E. Bonnington, M.L. Broekman, M.F. Broom, E.I. Buzás, G. Christiansen, N. Hajji, A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing, J. Extracell. Vesicles 5 (2016) 31242 under a Creative Commons Attribution 4.0 International License, https://creativecommons.org/licenses/by/4.0/.)

dispersion medium and the stationary layer of fluid attached to the dispersed particles. From a theoretic point of view, it is the electrical potential at the slipping plane of the interfacial double layer. ζ-potential is often used as an indicator for colloidal stability. For charge-stabilized particles, ζ-potential magnitudes of j30 mVj or higher are representative of a stable colloidal system. Also, a particle’s ζ-potential is useful in identifying biological analytes carrying different surface charges when their sizes are comparable. To achieve accurate and reliable particle ζ-potential measurements by TRPS, instrument settings should ensure that the electrophoretic forces are the dominant contribution

129

130

Characterization of nanoparticles

to particle transport mechanism. For this reason, the pressure range applied during ζ-potential measurements must be set to 10-fold lower than size and concentration measurements by pulling a lever in the VPM (Izon Science). Nanopores suitable for highresolution ζ-potential measurements are tested by Izon Science for their suitability. In general, a good quality membrane should possess the following three features: (1) ability to separate charged and uncharged particles, that is, being able to distinguish two particle populations using unmodified and carboxylated polystyrene nanoparticles at a 3:1 concentration ratio; (2) mean blockade FWHM durations of carboxylated calibration particles not exceeding 0.6 ms at a voltage returning a baseline current of 130–150 nA; (3) blockade FWHM durations (which are inversely proportional to the particle velocities) decreasing with increasing applied voltage applied. Like in other technologies, the electrophoretic mobility (μ) is measured to calculate the particle ζ-potential in aqueous media and moderate electrolyte concentrations. It relates to the velocity of particles υ in a unit of electric field (E). The particle ζ-potential is typically related to the electrophoretic mobility by the Smoluchowski approach (Eq. 7): ημ ζ¼ (7) ε where η and ε are, respectively, the dynamic viscosity of the electrolyte and its dielectric constant. To date, several TRPS protocols have been used to characterize the electrophoretic mobility (ζ-potential) of individual particles. Vogel and co-workers [54] demonstrated the use of blockade rate measurements at variable pressure to elucidate the mean ζ-potential of a particle ensemble using Eq. (8) (derived from Eq. 6). J¼

 Cε  CQ ζparticle + ζ pore E + η A

(8)

where ζ particle and ζ pore are the ζ-potential of the analytes and the tunable pore, respectively. E is electric field and Q is the volume flow rate. A is the cross-sectional area of the pore restriction, and C is the particle concentration. The Smoluchowski approximation is adopted to calculate electrophoresis and electro-osmosis contributions for the particles. Calculation of ζ-potential is achieved by setting J ¼ 0 in Eq. (8), so that the applied external pressure balances the combined effects of electrophoretic, electro-osmosis, and internal pressure head. The equilibrium can be identified using either the greatest value of the FWHM duration or the minimum blockade rate [50,54]. Pressure can be altered either continuously or in discrete steps to determine the mean ζ-potential. More recently, it has been reported [55] that robust and reproducible single-particle ζ-potential measurements can be achieved by recording the translocation duration of nanoparticles as a function of both voltage and pressure. This calibration-based, convection-inclusive, ζ-potential methodology involves the use of the proportional

Tunable resistive pulse sensing (TRPS)

0

Background

DNA added

Control

current

Current

T0.3 T0.4 T0.5 T0.6

Zeta potential (mV)

–5

L0.4 L0.6

–10 –15 –20 –25

Blockade magnitude

–30

Time

(A)

–10

0

50

100

150

200

250

300

350

Time (s)

0

CPC100 CPN100 CPC70 Adem+DNA Adem Mix

Binding capacity –5

Zeta potential (mV)

Zeta potential (mV)

0

(C)

–20

–30

–10 Enzymatically cleaved DNA bound particles Hyperbolic fit Kinetics from (A) Binding capacity

–15 –20 –25 –30

–40

–35 0

(B)

50

100

150

200

Diameter (nm)

250

300

0

(D)

20,000

40,000

60,000

80,000

100,000

DNA to particle ratio

Fig. 4 (A) A variety of translocation points are marked (e.g. T0.4), which represents the position of a particle in the pore, for example, T0.4 represent the time at which the particle blockade magnitude is 40% of its maximum and the particle is in position L0.4; (B) ζ-potential versus particle size distributions of a pentamodal mix [yellow dots (light grey in the print version)], including CPN100, CPC70, CPC100, DNA-functionalized and unmodified magnetic nanoparticles (i.e. Adem + DNA and Adem); (C) kinetic studies and (D) binding capacity of biotinylated DNA immobilized onto the iron oxide particles (Bio-Adembeads from ADEMTECH) carried out via particle ζ-potential TRPS measurements [55]. (Reproduced from R. Vogel, A.K. Pal, S. Jambhrunkar, P. Patel, S.S. Thakur, E. Reátegui, H.S. Parekh, P. Saá, A. Stassinopoulos, M.F. Broom, High-resolution single particle zeta potential characterisation of biological nanoparticles using tunable resistive pulse sensing, Sci. Rep. 7 (2017) 17479 under a Creative Commons Attribution 4.0 International License, https:// creativecommons.org/licenses/by/4.0/.)

blockade magnitude as a measure of particle position within the pore. For example, with reference to Fig. 4A, T1.0 is the time the blockade is at 100% maximum value, whilst T0.4 is the time at which the resistive pulse signal reaches 40% blockade maximum, with respective positions within the pore being L1.0 and L0.4. By measuring the time of blockage at a range of positions (e.g. T0.3, T0.4, T0.5, and T0.6) under variable voltage and pressure, differential electrokinetic mobility and convection can be determined from the 1/T versus voltage and 1/T versus pressure curves of a calibrant, respectively.

131

132

Characterization of nanoparticles

A pentamodal mix of five particles, including monodisperse polystyrene beads, CPN100, CPC70, CPC100 (Izon Science), and unmodified and DNA-functionalized magnetic nanoparticles, could be fully resolved in both size and ζ-potential (see Fig. 4B) with TRPS. The associated distributions are not affected by different applied pressures, confirming reproducibility and robustness of this TRPS protocol. Furthermore, highly sensitive TRPS kinetic studies were carried out to study the immobilization of oligonucleotides onto magnetic particles, providing information about particle properties that is of utmost importance for applications like DNA sequencing, biosensing, and drug delivery. Fig. 4C and D shows ζ-potential measurements over time, illustrating the binding capacity of surface-functionalized magnetic particles to DNA. Compared with a control sample, immobilization of DNA onto the surface of the streptavidin-coated superparamagnetic particles was seen to complete in approximately 100 s after the addition of the biotargets, and its binding capacity was determined by measuring the ζ-potential at equilibrium for various DNAs to particle ratios [55]. It is worth observing that these experiments were carried out with minimal DNA consumption, making TRPS an attractive technique for characterization of expensive biological analytes available in limited quantities.

Other surface properties by TRPS measurements It should be noted that resistive pulse rate and blockade FWHM can also be exploited to deduce changes in surface properties without explicitly calculating ζ-potential of a substrate [26,43,68]. For instance, TRPS technology has been utilized to detect the addition of uncharged polyethylene glycol to particle surfaces to prevent adhesion in biological environment [52]. Since the tunable pore carries slightly negative charge, it is also possible to functionalize the surface of the pore [69]. Care has been taken when analysing challenging biological samples to prevent particle–pore interactions and surface modification of the pore. For most biological samples, Izon pore coating solution (i.e. reagent kit from Izon Science) has been used to alleviate nonspecific binding to the pore surface [45]. It can be useful to carry out additional runs using calibrants at the end of ζ-potential measurements to confirm that the tunable pore remains intact and unchanged during analysis [70]. Neutral and weakly positively charged particles can be measured together with negatively charged ones by applying positive voltage and positive pressure. Nevertheless, an increase of the applied pressure may result in an increase of ζ-potential measurement uncertainty and therefore reduce its accuracy.

Summary with recommended settings for various TRPS measurements A summary of recommended setting for various TRPS measurements is shown in Table 2, and a reference guild to pore selection can be found in Table 3.

Tunable resistive pulse sensing (TRPS)

Table 2 Typical settings for various TRPS measurements TRPS measurements Settings

Size

Concentration

ζ-Potential

Applied voltage Applied pressure Sample size range Sample concentration References

1.6 V 2 kPa 50 nm–200 μm Size dependent [38]

1.6 V 2 kPa 50 nm–200 μm Size dependent [26, 44]

0–10 V 0.2 kPa 50 nm–2 μm Size dependent [53–55]

Table 3 A reference guide to pore selection, calibrant, and target particle concentrations Tunable pore

Diameter range (nm)

Calibrant (from Izon Science)

Target concentration (mL21)

NP80 NP100 NP200 NP400 NP1000 NP4000

40–225 50–330 85–500 185–1100 490–2900 1990–11,300

CPC70, CPC100 CPC100, CPC200 CPC200, CPC400 CPC400, CPC800 CPC1000, CPC2000 CPC4000

1  1010 1  1010 2  109 5  108 5  107 5  105

The size range shown in the succeeding can be detected across the standard stretch range between 43 and 49 mm under optimal setting conditions.

Conclusions TRPS provides a versatile technology platform for the characterization of colloidal nanoparticles. It allows scientists to perform high-resolution measurements using relative low volume of analytes (ca. 35 μL). Other advantages of TRPS have included its low cost, quick analysis, portability, wide accessible ranges of analytes and electrolytes. Using the TRPS protocols, a large amount of information about individual particles can be extracted from the resistive pulse signals to determine the size, size distribution, surface charge, and number concentration of particles in suspensions. Challenges associated with the TRPS technology include the limited accessible lower size range and measurement uncertainties caused by changing pore geometry and particle–pore interactions, which could be overcome by advances in nanofabrication of pore membranes. Extraction of particle density, porosity, shape, and orientation information from TRPS would be of broad interest, especially in fields such as nanomedicine and nanotoxicology, and some preliminary work has been carried out [35,71]. We predict that as this technique continues to evolve, TRPS will become more capable of analysing complex systems in biological environment, and combining TRPS with other analytical techniques promises tremendous advancements in the nanotechnology and nanoscience community.

133

134

Characterization of nanoparticles

References [1] W.H. Coulter, Means for Counting Particles Suspended in a Fluid, 1953. [2] ISO, 13319:2007—Determination of Particle Size Distributions—Electrical Sensing Zone Method, International Organization for Standardization, 2007. [3] T. Ito, L. Sun, R.M. Crooks, Simultaneous determination of the size and surface charge of individual nanoparticles using a carbon nanotube-based Coulter counter, Anal. Chem. 75 (2003) 2399–2406. [4] R.W. Deblois, C.P. Bean, Counting and sizing of submicron particles by the resistive pulse technique, Rev. Sci. Instrum. 41 (1970) 909–916. [5] J.J. Kasianowicz, E. Brandin, D. Branton, D.W. Deamer, Characterization of individual polynucleotide molecules using a membrane channel, Proc. Natl. Acad. Sci. U. S. A. 93 (1996) 13770–13773. [6] Y. Feng, Y. Zhang, C. Ying, D. Wang, C. Du, Nanopore-based fourth-generation DNA sequencing technology, Genomics Proteomics Bioinformatics 13 (2015) 4–16. [7] G.F. Schneider, C. Dekker, DNA sequencing with nanopores, Nat. Biotechnol. 30 (2012) 326. [8] Y. Choi, L.A. Baker, H. Hillebrenner, C.R. Martin, Biosensing with conically shaped nanopores and nanotubes, Phys. Chem. Chem. Phys. 8 (2006) 4976–4988. [9] T. Ito, L. Sun, R.R. Henriquez, R.M. Crooks, A carbon nanotube-based Coulter nanoparticle counter, Acc. Chem. Res. 37 (2004) 937–945. [10] R. An, J.D. Uram, E.C. Yusko, K. Ke, M. Mayer, A.J. Hunt, Ultrafast laser fabrication of submicrometer pores in borosilicate glass, Opt. Lett. 33 (2008) 1153–1155. [11] C. Dekker, Solid-state nanopores, Nat. Nanotechnol. 2 (2007) 209. [12] O.A. Saleh, L.L. Sohn, An artificial Nanopore for molecular sensing, Nano Lett. 3 (2003) 37–38. [13] M. Ulbricht, Advanced functional polymer membranes, Polymer 47 (2006) 2217–2262. [14] H.J.G.E. Gardeniers, Chemistry in nanochannel confinement, Anal. Bioanal. Chem. 394 (2009) 385–397. [15] J.J. Kasianowicz, J.W.F. Robertson, E.R. Chan, J.E. Reiner, V.M. Stanford, Nanoscopic porous sensors, Annu. Rev. Anal. Chem. 1 (2008) 737–766. [16] A. Kocer, L. Tauk, P. Dejardin, Nanopore sensors: from hybrid to abiotic systems, Biosens. Bioelectron. 38 (2012) 1–10. [17] M.L. Kovarik, S.C. Jacobson, Nanofluidics in Lab-on-a-Chip Devices, ACS Publications, 2009. [18] D. Kozak, W. Anderson, R. Vogel, M. Trau, Advances in resistive pulse sensors: devices bridging the void between molecular and microscopic detection, Nano Today 6 (2011) 531–545. [19] L. Luo, S.R. German, W.-J. Lan, D.A. Holden, T.L. Mega, H.S. White, Resistive-pulse analysis of nanoparticles, Annu. Rev. Anal. Chem. 7 (2014) 513–535. [20] I. Makra, R.E. Gyurcsa´nyi, Electrochemical sensing with nanopores: a mini review, Electrochem. Commun. 43 (2014) 55–59. [21] R. Mulero, A.S. Prabhu, K.J. Freedman, M.J. Kim, Nanopore-based devices for bioanalytical applications, J. Assoc. Lab. Autom. 15 (2010) 243–252. [22] L.T. Sexton, L.P. Horne, C.R. Martin, Developing synthetic conical nanopores for biosensing applications, Mol. BioSyst. 3 (2007) 667–685. [23] D.H. Stoloff, M. Wanunu, Recent trends in nanopores for biotechnology, Curr. Opin. Biotechnol. 24 (2013) 699–704. [24] Y. Tian, L. Wen, X. Hou, G. Hou, L. Jiang, Bioinspired ion-transport properties of solid-state single nanochannels and their applications in sensing, ChemPhysChem 13 (2012) 2455–2470. [25] S.J. Sowerby, M.F. Broom, G.B. Petersen, Dynamically resizable nanometre-scale apertures for molecular sensing, Sens. Actuators B: Chem. 123 (2007) 325–330. [26] G.R. Willmott, R. Vogel, S.S.C. Yu, L.G. Groenewegen, G.S. Roberts, D. Kozak, W. Anderson, M. Trau, Use of tunable nanopore blockade rates to investigate colloidal dispersions, J. Phys. Condens. Matter 22 (2010) 454116. [27] C.S. Allen, J.F.C. Loo, S. Yu, S.K. Kong, T.-F. Chan, Monitoring bacterial growth using tunable resistive pulse sensing with a pore-based technique, Appl. Microbiol. Biotechnol. 98 (2014) 855–862. [28] W. Anderson, D. Kozak, V.A. Coleman, A˚.K. J€amting, M. Trau, A comparative study of submicron particle sizing platforms: accuracy, precision and resolution analysis of polydisperse particle size distributions, J. Colloid Interface Sci. 405 (2013) 322–330.

Tunable resistive pulse sensing (TRPS)

[29] N.C. Bell, C. Minelli, J. Tompkins, M.M. Stevens, A.G. Shard, Emerging techniques for submicrometer particle sizing applied to Stober silica, Langmuir 28 (2012) 10860–10872. [30] S. Heider, C. Metzner, Quantitative real-time single particle analysis of virions, Virology 462 (2014) 199–206. [31] D. Kozak, W. Anderson, M. Grevett, M. Trau, Modeling elastic pore sensors for quantitative single particle sizing, J. Phys. Chem. C 116 (2012) 8554–8561. [32] J.R. McDaniel, I. Weitzhandler, S. Prevost, K.B. Vargo, M.-S. Appavou, D.A. Hammer, M. Gradzielski, A. Chilkoti, Noncanonical self-assembly of highly asymmetric genetically encoded polypeptide amphiphiles into cylindrical micelles, Nano Lett. 14 (2014) 6590–6598. [33] C. Minelli, A. Sikora, R. Garcia-Diez, K. Sparnacci, C. Gollwitzer, M. Krumrey, A.G. Shard, Measuring the size and density of nanoparticles by centrifugal sedimentation and flotation, Anal. Methods 10 (2018) 1725–1732. [34] A.K. Pal, I. Aalaei, S. Gadde, P. Gaines, D. Schmidt, P. Demokritou, D. Bello, High resolution characterization of engineered nanomaterial dispersions in complex media using tunable resistive pulse sensing technology, ACS Nano 8 (2014) 9003–9015. [35] M. Platt, G.R. Willmott, G.U. Lee, Resistive pulse sensing of analyte-induced multicomponent rod aggregation using tunable pores, Small 8 (2012) 2436–2444. [36] E. Pol, F.A.W. Coumans, A.E. Grootemaat, C. Gardiner, I.L. Sargent, P. Harrison, A. Sturk, T.G. Leeuwen, R. Nieuwland, Particle size distribution of exosomes and microvesicles determined by transmission electron microscopy, flow cytometry, nanoparticle tracking analysis, and resistive pulse sensing, J. Thromb. Haemost. 12 (2014) 1182–1192. [37] A. Sikora, A.G. Shard, C. Minelli, Size and ζ-potential measurement of silica nanoparticles in serum using tunable resistive pulse sensing, Langmuir 32 (2016) 2216–2224. [38] R. Vogel, G. Willmott, D. Kozak, G.S. Roberts, W. Anderson, L. Groenewegen, B. Glossop, A. Barnett, A. Turner, M. Trau, Quantitative sizing of nano/microparticles with a tunable elastomeric pore sensor, Anal. Chem. 83 (2011) 3499–3506. [39] E. Weatherall, P. Hauer, R. Vogel, G.R. Willmott, Pulse size distributions in tunable resistive pulse sensing, Anal. Chem. 88 (2016) 8648–8656. [40] G.R. Willmott, Tunable resistive pulse sensing: better size and charge measurements for submicrometer colloids, Anal. Chem. 90 (2018) 2987–2995. [41] J. De Vrij, S.L.N. Maas, M. Van Nispen, M. Sena-Esteves, R.W.A. Limpens, A.J. Koster, S. Leenstra, M.L. Lamfers, M.L.D. Broekman, Quantification of nanosized extracellular membrane vesicles with scanning ion occlusion sensing, Nanomedicine 8 (2013) 1443–1458. [42] S.L.N. Maas, J. De Vrij, M.L.D. Broekman, Quantification and size-profiling of extracellular vesicles using tunable resistive pulse sensing, J. Vis. Exp. (2014) e51623. [43] G.S. Roberts, D. Kozak, W. Anderson, M.F. Broom, R. Vogel, M. Trau, Tunable nano/micropores for particle detection and discrimination: scanning ion occlusion spectroscopy, Small 6 (2010) 2653–2658. [44] G.S. Roberts, S. Yu, Q. Zeng, L.C.L. Chan, W. Anderson, A.H. Colby, M.W. Grinstaff, S. Reid, R. Vogel, Tunable pores for measuring concentrations of synthetic and biological nanoparticle dispersions, Biosens. Bioelectron. 31 (2012) 17–25. [45] R. Vogel, F.A.W. Coumans, R.G. Maltesen, A.N. B€ oing, K.E. Bonnington, M.L. Broekman, M.F. Broom, E.I. Buza´s, G. Christiansen, N. Hajji, A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing, J. Extracell. Vesicles 5 (2016) 31242. [46] G.R. Willmott, M.G. Fisk, J. Eldridge, Magnetic microbead transport during resistive pulse sensing, Biomicrofluidics 7 (2013) 064106. [47] L. Yang, M.F. Broom, I.G. Tucker, Characterization of a nanoparticulate drug delivery system using scanning ion occlusion sensing, Pharm. Res. 29 (2012) 2578–2586. [48] E.L.C.J. Blundell, R. Vogel, M. Platt, Particle-by-particle charge analysis of DNA-modified nanoparticles using tunable resistive pulse sensing, Langmuir 32 (2016) 1082–1090. [49] M.-C. Chung, S. Dean, E.S. Marakasova, A.O. Nwabueze, M.L. Van Hoek, Chitinases are negative regulators of Francisella novicida biofilms, PLoS One 9 (2014) e93119. [50] J.A. Eldridge, G.R. Willmott, W. Anderson, R. Vogel, Nanoparticle ζ-potential measurements using tunable resistive pulse sensing with variable pressure, J. Colloid Interface Sci. 429 (2014) 45–52.

135

136

Characterization of nanoparticles

[51] D. Kozak, W. Anderson, R. Vogel, S. Chen, F. Antaw, M. Trau, Simultaneous size and ζ-potential measurements of individual nanoparticles in dispersion using size-tunable pore sensors, ACS Nano 6 (2012) 6990–6997. [52] J.-M. Rabanel, P. Hildgen, X. Banquy, Assessment of PEG on polymeric particles surface, a key step in drug carrier translation, J. Control. Release 185 (2014) 71–87. [53] A. Sikora, D. Bartczak, D. Geißler, V. Kestens, G. Roebben, Y. Ramaye, Z. Varga, M. Palmai, A.G. Shard, H. Goenaga-Infante, A systematic comparison of different techniques to determine the zeta potential of silica nanoparticles in biological medium, Anal. Methods 7 (2015) 9835–9843. [54] R. Vogel, W. Anderson, J. Eldridge, B. Glossop, G. Willmott, A variable pressure method for characterizing nanoparticle surface charge using pore sensors, Anal. Chem. 84 (2012) 3125–3131. [55] R. Vogel, A.K. Pal, S. Jambhrunkar, P. Patel, S.S. Thakur, E. Rea´tegui, H.S. Parekh, P. Saa´, A. Stassinopoulos, M.F. Broom, High-resolution single particle zeta potential characterisation of biological nanoparticles using tunable resistive pulse sensing, Sci. Rep. 7 (2017) 17479. [56] W.-J. Lan, C. Kubeil, J.-W. Xiong, A. Bund, H.S. White, Effect of surface charge on the resistive pulse waveshape during particle translocation through glass nanopores, J. Phys. Chem. C 118 (2014) 2726–2734. [57] R.B. Schoch, J. Han, P. Renaud, Transport phenomena in nanofluidics, Rev. Mod. Phys. 80 (2008) 839. [58] G.R. Willmott, B.E.T. Parry, Resistive pulse asymmetry for nanospheres passing through tunable submicron pores, J. Appl. Phys. 109 (2011) 094307. [59] A.H. Colby, Y.L. Colson, M.W. Grinstaff, Microscopy and tunable resistive pulse sensing characterization of the swelling of pH-responsive, polymeric expansile nanoparticles, Nanoscale 5 (2013) 3496–3504. [60] S.L.N. Maas, M.L.D. Broekman, J. De Vrij, Tunable resistive pulse sensing for the characterization of extracellular vesicles, in: Exosomes and Microvesicles, Springer, 2017. [61] N. Kothalawala, T.K. Mudalige, P. Sisco, S.W. Linder, Novel analytical methods to assess the chemical and physical properties of liposomes, J. Chromatogr. B 1091 (2018) 14–20. [62] F. Akpinar, J. Yin, Characterization of vesicular stomatitis virus populations by tunable resistive pulse sensing, J. Virol. Methods 218 (2015) 71–76. [63] E.L.C.J. Blundell, L.J. Mayne, E.R. Billinge, M. Platt, Emergence of tunable resistive pulse sensing as a biosensor, Anal. Methods 7 (2015) 7055–7066. [64] E. Weatherall, G.R. Willmott, Applications of tunable resistive pulse sensing, Analyst 140 (2015) 3318–3334. [65] E.R. Billinge, J. Muzard, M. Platt, Tunable resistive pulse sensing as a tool to monitor analyte induced particle aggregation, Nanomater. Nanosci. 1 (2013) 1. [66] R.W. Deblois, C.P. Bean, R.K.A. Wesley, Electrokinetic measurements with submicron particles and pores by the resistive pulse technique, J. Colloid Interface Sci. 61 (1977) 323–335. [67] E.A. Heins, Z.S. Siwy, L.A. Baker, C.R. Martin, Detecting single porphyrin molecules in a conically shaped synthetic nanopore, Nano Lett. 5 (2005) 1824–1829. [68] M. Low, S. Yu, M.Y. Han, X. Su, Investigative study of nucleic acid-gold nanoparticle interactions using laser-based techniques, electron microscopy, and resistive pulse sensing with a nanopore, Aust. J. Chem. 64 (2011) 1229–1234. [69] E.L.C.J. Blundell, L.J. Mayne, M. Lickorish, S.D.R. Christie, M. Platt, Protein detection using tunable pores: resistive pulses and current rectification, Faraday Discuss. 193 (2016) 487–505. [70] E.L.C.J. Blundell, M.J. Healey, E. Holton, M. Sivakumaran, S. Manstana, M. Platt, Characterisation of the protein corona using tunable resistive pulse sensing: determining the change and distribution of a particle’s surface charge, Anal. Bioanal. Chem. 408 (2016) 5757–5768. [71] N. Arjmandi, W. Van Roy, L. Lagae, Measuring mass of nanoparticles and viruses in liquids with nanometer-scale pores, Anal. Chem. 86 (2014) 4637–4641.