Applications for Nanodispersions

CHAPTER

Applications for Nanodispersions

9

Nanodispersions are dispersed systems that contain nanoparticles. There is almost complete consensus achieved with regard to the definition of the nanoparticles: these are particles with sizes under 100 nm. This definition would include microemulsions and other soft particles. However, we would retain old term microemulsions for such equilibrium soft particles systems. We present applications for such systems in Chapter 10. This chapter is devoted to the solid, rigid nanoparticulates. Ultrasound techniques allow easy, very precise characterization of particle size and z-potential in such systems. Applicability of these methods for nanodispersions is proved by special verification tests presented in Sections 6.5 and 6.6. Here we discuss several publications describing industrial applications of these techniques in nanotechnology. There are three publications regarding online characterization of nanodispersion: by Stenger et al. [1] on nanomilling, by Wilhelm and Stephan [2] on monitoring coating by nanoparticles, and by Wang et al. [3] about online particle-size measurement. General particle size and z-potential measurements of nanodispersions are presented in [4e6]. We will discuss here in more details some other examples of nanodispersions characterization performed in our laboratory. The first one is about reference material for nanoparticulates. There is one specific nanodispersion that has been used as a reference material for a long time. It is silica Ludox. We present characteristics of this dispersion measured in collaboration with Horiba USA and discuss its differences with other potential reference/certified materialdgold sols. Silica Ludox is currently under consideration by European Union Institute of Standards as certified material for nanodispersions particle sizing. Then we present our studies of polydisperse systems containing nanoparticles fraction. Ultrasound offers possibility to characterize these systems without special sample treatment. This allows monitoring fraction of nanoparticles in such systems with broad size distributions [7]. Alternatively, there is a possibility of measuring large particles content when nanoparticles dominate size distribution. There are two possible ways to achieve this, one using acoustic [8], the other one with electroacoustic z-probe. We determine sensitivity of ultrasound-based methods for these two extreme tasks, which present important industrial applications in cosmetics, chemical mechanical polishing (CMP) slurries, nanotoxicology, etc. At the end we discuss possible limitations of ultrasound-based method for characterizing nanodispersions. Characterization of Liquids, Dispersions, Emulsions, and Porous Materials Using Ultrasound http://dx.doi.org/10.1016/B978-0-444-63908-0.00009-0 Copyright © 2017 Elsevier B.V. All rights reserved.

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9.1 REFERENCE NANOMATERIAL FOR THE PARTICLE SIZING AND z-POTENTIAL IN DILUTE AND CONCENTRATED SYSTEMS: COLLOIDAL SILICA LUDOX Instruments based on acoustic attenuation are “first principle” techniques that do not require calibration, similar to light scattering instruments. However, they should be subjected to verification tests on a regular basis. Verification in this sense involves challenging the system with a known sample and comparing the calculated result with the expected value. If the calculated versus expected result varies beyond the determined maximum deviation, then the system typically requires service attention (alignment, source, or detector replacement, etc.) to return the system to proper operating condition. On the other hand, electroacoustic instrument do require calibration, as specified in Chapter 7. It would be very desirable to have material that could serve both purposes, verification and calibration at once. It would be even better if such material could be tested with other methods as well. It turns out that there is a candidate that could meet these requirementsdcolloidal silica Ludox. We discuss this material here. Different nomenclature can be used when discussing materials used to calibrate or verify particle characterization instruments. Terms used include standards, reference materials, and certified reference materials. For the purposes of this document the terminology defined by ISO guide 30 [9] will be used. A Reference Material is “sufficiently homogeneous and stable with respect to one or more specified properties, which has been established to be fit for its intended use in a measurement process.” A Certified Reference Material is “characterized by a metrologically valid procedure for one or more specified properties, accompanied by a certificate that provides the value of the specified property, its associated uncertainty, and a statement of metrological traceability.” Using these definitions, then what many people refer to as a standard is a certified reference material since this includes a certificate of analysis and statement concerning traceability. A reference material can be any particulate sample meeting the defined conditions of being homogenous, stable, and established to be fit for a given use. The current supply of certified reference materials available for use in particle characterization can also be further classified as either monodisperse or polydisperse. Monodisperse (or monosized) reference materials are typically polystyrene latex spheres with extremely narrow distributions. An example distribution may be 1.020
0.022 mm. These materials are so narrow that typically only a midpoint (such as the mean) to the distribution is used for calibration or verification. Polydisperse reference materials have a broader distribution so that other points of the distribution (such as the d10 and d90) can also be cited and used during the verification process. Many polydisperse reference materials are glass beads [10,11] with certified values including d10, d25, d50, d75, and d90.

9.1 Reference Nanomaterial for the Particle Sizing

Although several standard practice documents [12,13] suggest using polydisperse reference materials “over one decade of size” (1e10, 10e100, etc.) for the verification process, in practice none of the available materials meet this criteria. The National Institute of Standards and Technology (NIST) standard reference material (SRM) 1003c may be the best characterized sample of this type and would be accepted as appropriate by any regulatory or enforcement agency. The NIST certificate for 1003c provides certified diameters from the d5 to d95 in 5% increments ranging from 18.9 to 43.3 mm, hardly a 10e100 mm spread. Unfortunately, NIST standard reference material cannot be used for concentrated systems and is not well suited for calibrating z-potential instruments. Instead, manufacturers of ultrasound-based instruments have been using colloidal Ludox Silica for at least 20 years. This material is a potential candidate for certification as standard reference material by European Union Institute of Standards. Ludox is a trademark first issued to DuPont, later acquired by Grace in 2000, for a stable colloidal silica suspension. Scientific references to the particle size of Ludox dates back to the 1960s [14]. Ludox is used for a variety of industrial applications including coatings, ink receptive papers, metal casting, refractory products, catalysts, and as a clarifying agent. The suspension is electrostatically stabilized, preventing agglomeration over time, and assuring easy mixing. Ludox TM silica has been used by particle characterization manufacturers and end users for many years due to several unique properties of this material including: • • • • •

availability and low cost extremely long shelf life understood particle size (30e34 nm) [14,15]. understood z-potential (near 38 mV) known concentration (50%wt)

Until recently, very few well-characterized materials in the 30 nm size range were available. For all of these reasons instruments based on acoustic spectroscopy have been using Ludox TM as a particle size and z-potential reference material for over 20 years. In order to have statistically verified parameters of this material, we measured particle size of it with two different techniques: acoustic spectroscopy (DT-1200) and dynamic light scattering (HORIBA LB-550). We present results of the sizing with DT-1200 first. The Ludox TM-50 was first diluted from 50 to 10%wt using the following procedure: • • • •

Prepare 1 L 0.01 M KCl by adding 0.745 g KCl into 1 L volumetric flask and fill to 1 L with deionized water Tare 250 mL beaker on balance Add 25 g of 50% Ludox concentrate Dilute to 125 g total weigh. using 0.01 M KCl

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The 10%wt sample was measured in the DT-1201 without further dilution. The only input required for the DT-1201 measurements was the weight percent of the sample. Fig. 9.1 demonstrate typical particle-size distribution (PSD) and attenuation spectra measured with DT-1200. Attenuation spectrum is raw data and its presentation serves important purpose of confirming validity of the calculated PSD. Dynamic light scattering (DLS) measurement requires more dilution. In order to achieve a proper signal level, five drops of the 50%wt sample were added to a standard DLS cuvette containing 3 mL of the 0.01 M KCl solution. The particle-size analysis is calculated on a volume basis using the refractive index for colloidal silica: 1.45 real, 0.0 imaginary. The sample is dilute enough so as to approximate the viscosity of water. Fig. 9.2 demonstrate typical PSD of Ludox measured with LB-550. The PSD of Ludox is fairly narrow, although not nearly as narrow as a latex reference material, with a span (d90-d10/d50) near 0.5 or standard deviation (SD) near 0.1. For this reason, only mean values are reported for this study. This study has been performed with five different DT-1200 and three different LB-550 instruments. Samples were measured five times with each of these instruments. Averaged median particle size and SD are shown in Table 9.1.

3.0

2.5 0.4 2.0 Attenuation [dB/cm/MHz]

PSD,weight basis

360

1.5

1.0

0.5

0.0 0.01

0.1 diameter [um]

0.3

0.2

0.1

0.0 100

101

102

Frequency [MHz]

FIGURE 9.1 Typical particle-size distribution and attenuation spectrum of 10% silica Ludox at 25 C measured with DT-1200.

9.1 Reference Nanomaterial for the Particle Sizing

FIGURE 9.2 Typical particle-size distribution measured with LB-550.

Table 9.1 Averaged Median Size and Standard Deviation of the Silica Ludox Measured With Acoustic Spectrometer DT-1200 and Dynamic Light Scattering LB-550 D50 (Micron) DT-1200 LB-550

31 30.08

Standard Deviation (Micron) 0.9 0.46

For most cases the results created by different particle characterization techniques can generate a wide spread of mean values. Ludox appears to be the very rare sample where multiple particle-sizing techniques generate very similar results. Reasons for this could include the nature of the particles themselves (round, narrow-size distribution) and the stability of the suspension at various concentrations. This unique combination of availability, low expense, and similar results using multiple techniques suggests that Ludox could be used as a nearly universal particle-size reference material in many industries. In addition, silica Ludox can serve as reference and calibration material for z-potential instruments. Small-particle size significantly simplifies the calculation of z-potential because inertia effects are not important. This explains why this material has been used for this purpose in electroacoustic instruments for 20 years. Diluted as stated above, this material has pH ¼ 9.3 and conductivity 0.23 S/m. In these conditions, value of Smoluchowski z-potential equals 38 mV. Statistical test with five different DT-1200 instruments shows that this parameter is measured with SD of 0.36 mV. Value of z-potential for this material was measured independently with Lazer Zee Meter model 501 by PenKem Inc. This instrument reports value of z-potential calculated according to Smoluchowski theory. This makes results of two instruments consistent.

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pH 0 2

3

4

5

6

7

8

9 0.9

-10

0.7 zeta 0.6

-20 0.5 0.4

-30 conductivity -40

conductivity [S/m]

0.8

zeta [mV]

362

0.3 0.2

FIGURE 9.3 Typical titration of the 10% silica Ludox at 25 C measured with DT-1200.

Modern electroacoustic instrument DT-1200 also reports results calculated using advanced electroacoustic theories described in Chapter 5. This includes following parameters for silica Ludox: • • • • • • •

Debye length ¼ 2.4 nm ka ¼ 6.5 MaxwelleWagner frequency ¼ 52 MHz dynamic mobility ¼ 4.53 surface charge density ¼ 1.7/106 C/cm2 Dukhin number ¼ 0.4 z (surface conductivity included) ¼ 46.3

These parameters indicate that silica Ludox is on the border of the thin double layer (DL) model. Surface conductivity becomes important and contributes substantially to absolute value of z-potential. This material also serves well for testing titration capability. Fig. 9.3 shows typical titration curve measured with DT-1200. As a final conclusion we can state that silica Ludox is very useful universal reference material. We hope that eventually it would be certified as nanoparticle standard.

9.2 LARGE PARTICLE CONTENT RESOLUTION USING ACOUSTICS Characterization of abrasive slurries and modern CMP materials present a specific challenge for measuring techniquesdmonitoring content of large particles that

9.2 Large Particle Content Resolution Using Acoustics

could ruin quality of polishing. One can find extensive investigations of various characterization techniques for CMP slurries in publication by Anthony et al. [16,17], as well as our own [8]. Three aspects of this application cause difficulties when using instruments based on traditional techniques. First, the particle sizes of many abrasive slurries are too small for sedimentation-based instruments or electric-zone instruments. Typically, the mean size of such materials is approximately 100 nm, with no particles, or preferably just a few, larger than 500 nm. The desired range over which particle characterization is desired is very large, which eliminates most classical techniques. Third, abrasive systems are typically shear-sensitive. Shear caused by the delivery system or the polishing process itself may induce an unpredictable assembly of the smaller particles into larger aggregates. However, these aggregates may be weakly formed, and easily destroyed by subsequent sonication, high shear, or dilution. Therefore, any technique which requires dilution or other sample preparation steps may in fact destroy the very aggregates that one is attempting to quantify by measurement. We suggest that abrasive systems must be characterized as is, without any dilution or sample preparation. Acoustic spectroscopy provides an exciting alternative to the more classical methods; this technique resolves all three issues mentioned above. This is the conclusion of the Bell Labs tests [16,17] and we completely agree with it after our own extensive study using Dispersion Technology instruments. In addition to particle sizing, ultrasound offers a simple and reliable way to characterize z-potential using electroacoustics. The importance of electrokinetic characterization of CMP slurries are stressed by Osseo-Asare [18]. In this section, we present results of our study with model slurries using Dispersion Technology instruments for characterizing both particle size using acoustics and z-potential using electroacoustics. We emphasize here one feature of acoustic spectroscopy that thus far has not been described sufficiently in the literature: namely the ability to characterize a bimodal PSD. Although Takeda et al. [19e21] demonstrated that acoustic spectroscopy is able to characterize bimodal distributions of mixed alumina particles, the ultimate sensitivity in detecting one very small subpopulation in combination with another dominant mode was not studied. Yet, it is just this feature, the ability to recognize a small subpopulation, which is most critical for abrasive studies. This section addresses this important issue. Unfortunately, there is no agreement in the literature as to the number of larger particles which might be acceptable in abrasive slurry. A set of experiments was made to test whether the attenuation spectra changed reproducibly, when a small amount of larger particles was added to a single component slurry of smaller particles. We used three silica materials altogether. The small-size particles were used, namely silica Ludox TM (50%wt). Two large-size particles were employed, namely, Geltech 0.5 micron and Geltech 1.5 micron. We assumed a density of 2.1 g/cm3 for all silica particles.

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Slurries were prepared at 12%wt for each material as follows. The silica Ludox was diluted to 12 %wt with 0.01 M KCl solution, resulting in a sample pH of 9.3. The Geltech samples were supplied as a dry powder, which was dispersed in 0.01 M KCl solution, and adjusted to pH 9.6 with KOH. The dispersion was repeatedly sonicated, stirred, and allowed to equilibrate for 5 h before being measured. Table 9.2 presents particle-size data provided by the manufacturer for each of these samples and measured with acoustic spectrometer DT-1200. The goals of the experiment were met in three steps. Step 1dReproducibility: The objective was to prove that the DT-1200 acoustic spectrometer is able to measure the attenuation spectra with the required precision of 0.01 dB/cm/MHz. To prove this, we measured five different fillings of the same 12% wt silica Ludox TM. Each filling was measured nine times. A statistical analysis of the measured attenuation spectra yielded an average variation of the attenuation measurement, over the frequency range from 3 to 100 MHz. Step 2dAccuracy: The goal here was to prove that acoustic spectroscopy can characterize the particle size with sufficient accuracy. To achieve this goal, all five silica slurries were measured individually. The measured particle size was then compared with independent information provided by the manufacturer. Step 3dBimodal Sensitivity: This is the key step in the investigation. Model systems with bimodal PSD were prepared by mixing a small-particle slurry (Ludox TM) with increasing doses of the large-particle slurries (Geltech 0.5 or Geltech 1.5). The objective was to evaluate the accuracy of the PSD calculated by the DT-1200 software for these known systems. We used Ludox TM as the major component of two test slurries. The Geltech 0.5 or Geltech 1.5 were added as “large” and “larger” particles to the above test slurries to form various mixed slurries. In each case the minor fraction was added to the Ludox TM in steps. Each addition increased the relative amount of the larger particles by 2%. The attenuation spectrum was measured twice for each mixed system in order to demonstrate reproducibility. Figs. 9.4 and 9.5 demonstrate the results of these mixed system tests. It can be seen that attenuation increases with increasing amounts of the “large” or “larger” particles. The increase in the attenuation with increasing doses of the Geltech content is in all cases significantly larger than precision of the instrument. This demonstrates that the DT-1200 data contains significant information about small amount of Table 9.2 Particle Size of the Initial Silica Samples, Expected and Measured Acoustically Ludox TM Geltech 0.5 Geltech 1.5

Manufacturer

Acoustics

22 nm (area basis) 0.5 micron 1.5 micron

31 nm (weight basis) 0.65 micron 1.72 micron

9.2 Large Particle Content Resolution Using Acoustics

0.5

Attenuation [dB/cm/MHz]

0.4 9% 9% 7.4% 7.4% 5.7% 5.7%

0.3

0.2

3.8% 3.8% 2% 2% ludox only

0.1

0.0 100

101 Frequency [MHz]

102

FIGURE 9.4 Attenuation spectra measured for Ludox TM-50 silica with various additions of Geltech 0.5 silica. Total solid content is 12%wt. The legend shows the fraction of the total solid content corresponding to the Geltech silica. 0.5

9% 9% 7.4% 7.4% 5.7% 5.7% 3.8% 3.8% 2% 2% ludox only

Attenuation [dB/cm/MHz]

0.4

0.3

0.2

0.1

0.0 100

101 Frequency [MHz]

102

FIGURE 9.5 Attenuation spectra measured for Ludox TM-50 silica with various additions of Geltech 1.5 silica. Total solid content is 12%wt. Legend shows the fraction of the total solid content corresponding to the silica Geltech.

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the large particles. The final question is to determine whether the calculated PSD calculates a correct bimodal distribution for these mixed model systems. Table 9.3 answers this question. The DT-1200 always calculates lognormal and bimodal distributions that best fit the experimental data. These two PSDs are best in the sense that the fitting error between the theoretical attenuation (calculated for the best PSDs) and the experimental attenuation is minimized. These fitting errors are important criteria for deciding whether the lognormal or bimodal PSD is more appropriate for describing a particular sample. For instance, the PSD is judged to be bimodal only if the bimodal fit yields substantially smaller fitting error than a lognormal PSD. The values of the lognormal and bimodal fitting errors are given in Table 9.3. It can be seen that fitting errors for the bimodal PSD are better than the lognormal fits for all of the mixed systems over the whole concentration range, and for both the large and larger sized particles. According to the fitting errors, all PSD in the mixed Ludox-Geltech systems are bimodal, which of course is correct for these known mixed systems. At the same time our experience tells us that this fitting error criterion alone is not a sufficient test for the bimodality of the PSD. It is always possible to obtain a better fit to a data set by allowing more degrees of freedom in the solution. A bimodal PSD provides at least four adjustable parameters for fitting the experimental attenuation curve, whereas a simple lognormal PSD provides only two parameters. The value of the larger particle content is another important parameter which must be taken into account. We demonstrated that the precision of the DT-1200 is sufficient to detect larger particles present at concentrations larger than 2%. Therefore the software will not claim a bimodal PSD if the content of the large particles is less than 2%, even if this yields a marginally better fit. Referring to Table 9.3, we see that the calculated large particle content in all of the mixed Ludox TM/Geltech systems is well above this 2% threshold and therefore gives an additional argument to claim a bimodal PSD for these systems. It is important that the calculated content of the large particles increases with the actual content of the added Geltech particles which confirms consistency of the PSD analysis. As a final conclusion we can declare that acoustic spectroscopy is capable of resolving at least 2% of large particles in concentrated nanodispersions with no dilution and practically no sample preparation.

9.3 MONITORING PRESENCE OF LARGE PARTICLE USING ELECTROACOUSTICS Electroacoustic z-potential probe DT-300 is usually used for measuring z-potential of concentrated particulates. It turns out that it can be used for a completely different purposedmonitoring the presence of large particles in nanodispersions. In order of using it for such purpose, the probe must be placed vertically in a suitable stand that orients it such that the face of the probe with the gold electrode is on top, as it is shown in Fig. 9.6. A cylindrical fixture around the top of the probe creates

Calculated for Geltech 0.5

Calculated for Geltech 1.5

Actual Geltech Content (%)

Content (%)

Larger Size Micron

Lognormal Fitting Error (%)

Bimodal Fitting Error (%)

Content (%)

Larger Size Micron

Lognormal Fitting Error (%)

Bimodal Fitting Error (%)

9 9 7.4 7.4 5.7 5.7 3.8 3.8 2 2

14 15 8 12 7 10 4 4 4 5

0.7 0.9 0.9 1 0.9 1.3 0.9 0.7 0.6 1.2

14.1 10.9 13.6 15.4 13.8 12.9 9.5 12 10.8 12.2

7.3 8.7 4.5 7.1 4.3 7.2 3.7 4.3 3.6 3.7

11 12 10 10 7 6 5 6 3 4

1.6 1.7 1.9 1.9 1.9 1.6 1.3 1.6 1.9 1.6

24.5 17.7 17.8 17.3 17.2 18.5 18.1 16.9 12.2 12.3

4.4 5.5 6 5.4 4.1 4.7 3.9 3.8 2.6 3.9

9.3 Monitoring Presence of Large Particle Using Electroacoustics

Table 9.3 Characteristics of the Larger Particles (Geltech Silica) Calculated From the Attenuation Spectra of Figs. 9.4 and 9.5

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SAMPLE

Plastic cup Ultrasound pressure wave

DEPOSIT Hmax

Zeta Probe

FIGURE 9.6 Electroacoustic probe with novel sample handling system that allows measurement of particulates deposits and porous bodies.

a cup with the probe face serving as a bottom of the said cup. This cup can be filled with nanodispersion. Ultrasound pulses generated by the electroacoustic probe enter this sample through the gold electrode. These sound pulses generated by the probe propagate directly into the sample and generate an electroacoustic signal. Magnitude of the electroacoustic signal is roughly proportional to the volume fraction of the particles, as shown in Chapter 5. In the case of stable system with no sedimentation volume fraction of the particles near the surface of the probe is constant and signal should be unchanged in time. In the case when nanodispersion contains sedimenting large particles, they would settle on the surface of the probe, which would lead to increased volume fraction there. This would lead ion turn to increasing magnitude of the electroacoustic signal. This time dependence can be used as a measure of the large sedimenting particles presence. In order to verify sensitivity of this method we used two different solid material particles: silica Ludox, silica Geltech 0.5. Each was prepared at a solid concentration of 10%wt dispersed in distilled water. The pH was adjusted in each case to provide good stability. Fig. 9.7 shows the time trend of the colloid vibration current (CVI) signal for five 10%wt Ludox samples, each successive sample containing increased amounts of Geltec from 0% to 1%. The electroacoustic signal of the unadulterated Ludox

9.4 Monitoring Nanoparticles Content in Systems

Ludox only Ludox + 0.1% Geltech Ludox + 0.3% Geltech Ludox + 0.5% Geltech Ludox + 1 % Geltech

5.4E+06 5.2E+06

CVI magnitude [mV*(sec/g)^1/2]

5.0E+06 4.8E+06 4.6E+06 4.4E+06 4.2E+06 4.0E+06 3.8E+06 3.6E+06 3.4E+06 0

50

100 150 time [minutes]

200

250

FIGURE 9.7 Kinetics of electroacoustic magnitude evolution due to deposit of silica Geltech particles added in small amounts to silica Ludox.

sample does not change with time, which is expected since these very small 31 nm particles do not sediment appreciably during the period of measurement. However, the added small amount of the larger silica Geltech particles causes a time rate of change proportional to the amount of the added large particles due to an accumulation of these particles near the probe surface. We see that the measurement is very sensitive to even small amounts of the larger particles, as little as 0.1%. This test yields just qualitative result regarding large particle content, but with higher sensitivity than acoustic method from the previous section. It also has advantage comparing with the acoustic method being much simpler, there are no special calculations required for verifying the presence of large particles. This simplicity makes it very attractive.

9.4 MONITORING NANOPARTICLES CONTENT IN SYSTEMS WITH A BROAD POLYDISPERSE SIZE DISTRIBUTION Nanoecology and nanotoxicology are rapidly growing scientific disciplines for studying the impact of nanotechnology on human health and the environment [22e27]. A growing number of consumer products, cosmetics in particular, that

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contain engineered nanoparticles create an immediate need for careful studies. This is reflected in a law recently adopted by European Parliament, on March 24, 2009, and supported by European Cosmetics Association. One problem that must be addressed is the selection of an accurate method for monitoring the nanoparticle content of various products, many being dispersions containing mixtures of not only nano-sized particles, but also particles of much larger size. While there are known methods for measuring systems containing only nanoparticles, there are no well-established techniques for monitoring nanoparticles mixed with larger ones. Development of such methods is imperative for reliable investigation of nanoparticles in natural and engineered systems, such as cosmetic products [28]. The first logical step in this regard is an investigation with existing, commercially available instruments preferably by an independent institution, which has in possession all the instruments that are relevant for this task. In the absence of such institutions for undertaking a project, instrumentation companies can do such a study with their instruments in collaboration with academic institutions. To make the study reliable, it is necessary that it be conducted with the same set of nanoparticles. This approach also has the advantage of the test being done by staff trained for the particular instrument. Toward this purpose, as a first step researchers at the Langmuir Center for Colloids and Interfaces at Columbia University and Dispersion Technology Inc. have undertaken this study to compare different samples with acoustic spectroscopy and transmission electron microscope (TEM), since these techniques have been reported to be well suited for characterizing nanoparticles with rather narrow-size distributions [1e6,9]. The goal of this study is to determine the sensitivity of the acoustic spectroscopy to nanoparticles in the broad PSDs with large amount of coarser particles. Acoustic spectroscopy might have significant advantages over light scattering methods in studying samples with broad polydisperse PSDs. Weight basis is innate for this technique because attenuation of ultrasound due to viscous dissipation by submicron particles is proportional to the particles weight. This means that raw data for calculating particle size depends only on the third power of the particle size. On the contrary, scattered light depends on much higher power of the particles size, as large as sixth. This makes scattering by large particles dominating raw data and overshadowing contribution of the smaller particles in the nano-size range. Acoustic attenuation’s weaker dependence on the particles size makes contribution of the small (nano) and larger particles more even and the method potentially more sensitive to the nanoparticles content even in the very broad size distributions. ZnO powder was selected for this study since this substance, widely used in sunscreen products, has played an important role in recent nanoecology discussions. In order to make samples easily available for all future studies, they were purchased from Fisher Scientific Inc., which offers powders from a variety of sources. Eight different powders produced by various manufacturers are listed below and in Table 9.4.

9.4 Monitoring Nanoparticles Content in Systems

Table 9.4 Median Particle Size and Percentage of the Nanoparticles (<100 nm) in Various ZnO Samples at 5%wt Stabilized at pH 10 With Hexametaphosphate

Powder Name, Manufacturer Zinc oxide, 99.5þ% by Acros Organics (5 loads, 25 measurements) Zinc oxide, ACS reagent grade by Acros Organics (1 load, 10 measurements) Z50-500 USP powder packaged by Fisher Scientific (1 load, 10 measurements) Z52-500 USP powder packaged by Fisher Scientific (4 loads, 28 measurements) S80249 by Fisher Scientific (1 load, 5 measurements) Zinc oxide ACS reagent grade by MP Biomedicals, LLC (1 load, 5 measurements) Zinc oxide Polystormor by Mallinckrodt Chemicals (3 loads, 46 measurements) Zinc oxide Nanopowder by American Elements (3 loads, 15 measurements)

• • • • • •

Median Size (Microns)

Cumulative % of Nanoparticles According to Acoustics, <100 nm

0.273
0.01

11.0
1.4

0.430
0.02

7.0
0.5

0.561
0.017

2.7
0.39

0.660
0.037

1.9
0.4

0.398
0.001

6.1
0.2

0.349
0.017

8.2
2.1

0.223
0.009

19.6
1.8

0.631
0.1

4.7
2.5

Zinc oxide ACS reagent grade and Zinc oxide, 99.5þ% manufactured by Acros Organics Z50-500 and Z52-500 USP powder packaged by Fisher Scientific S80249 by Fisher Scientific Zinc oxide ACS reagent grade by MP Biomedicals, LLC Zinc oxide Polystormor by Mallinckrodt Chemicals Zinc oxide Nanopowder by American Elements

For adjusting pH of ZnO dispersions, we used potassium hydroxide 1.000N by VWR International. For enhancing electric surface charge of ZnO particles, we used 10%wt solution of sodium hexametaphosphate by Fluka dissolved in distilled water. We use three different methods, one based on ultrasound (DT-1200), the second one is electron microscopy, the third is dynamic light scattering.

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Electron microscopy photos were made with a JEOL TEM. The particles were firstly dispersed in pH 10 sodium hydroxide solutions. After equilibrating and mixing the suspension overnight, we prepared the TEM samples by loading a droplet of the dispersion onto a 300 mesh gilder copper TEM grid (Electron Microscopy Sciences, Inc.). After the water in the droplet had evaporated, particles precipitated to the grid. This grid were then put into the TEM sample holder and inserted into the TEM instrument. The accelerating voltage applied was 100 kV. The magnifications for all the samples were kept similar for comparison. We used two different dynamic light scattering instruments. Instructions for both of them suggest using filtration for removing large particles. Strong scattering by even a few large particles could cause artifacts with this technique. Filtration would not acceptable for the broad PSDs of the current study with large quantities of large particles. Nevertheless, we have performed some trial measurements that failed. Results contradicted TEM photos even qualitatively. We do not discuss them here. All samples were prepared at 5%wt by adding 5 g of powder to 95 g of distilled water and sonicating for making it homogeneous. Value of pH naturally increased from 6 to 7. This sample was then poured into the DT-1201 measuring chamber with a magnetic stirrer for preventing sample sedimentation. All the measurements then are performed with this sample without taking it out of the measuring chamber. The main purpose of sample preparation is to break up the flocs and disperse the particles. This is achieved by increasing the surface charge of the particles and z-potential measurements allow optimization of this procedure. The software option for running multiple measurements continuously is used for z-potential measurements. Initial value of z-potential is about þ10 mV for all samples. As it is too low for preventing flocculation, as a first step of dispersion, pH is adjusted to 10 by adding small amounts of 1N KOH. Value of z-potential becomes negative, between 10 and 15 mV, which is not enough to keep particles from aggregating. Hence as a second step small increments of 10% hexametaphosphate (HMPH) solution were added and the sample was sonicated after each addition. Simultaneous and continuous measurement of z-potential displays a gradual increase in its absolute value. Under HMPH saturation conditions, the z-potential eventually reaches a sufficiently significant negative value. This saturation value differs from one powder to another; it is more than 30 mV for all of them but not exceeding 40 mV. Monitoring the increase in z-potential is an important part of the sample preparation procedure, since HMPH overdosing can cause the particles to flocculate again. This preparation usually takes about 1 h, although some particles exhibited much longer equilibration time. This might show up during the particle-size measurement as drifting of the measured attenuation spectra with time. In such cases the sample was mixed for prolonged equilibration time. Multiple measurements of PSD were performed with every sample. Some of the samples were prepared several times. All these data has been statistically treated.

9.4 Monitoring Nanoparticles Content in Systems

Table 9.4 presents the arithmetic average of the median particle size and average absolute deviation of this parameter for each ZnO sample. The tested powders were observed to have very different sizes in the range of 220e660 nm. One powder (Nanopowder by American Elements) was expected to be the smallest one since it was the only powder that had the term “nano” in its name. This sample was prepared three times, three different loads, and multiple sonications were tried to disperse it without success. In this case the size was observed to vary from one preparation to another, which is reflected in relatively large variation coefficient. In any case, the amount of nanoparticles in this powder dispersed in water was still very small. Broadness of all the PSD is illustrated in Fig. 9.8. Interestingly, all distributions are broad with sizes in the nano, submicron, and micron scales. Table 9.4 also presents the averaged content of nanoparticles for each ZnO. Precision of this measurement is defined as ratio of the “absolute average deviations” to the “average content.” It is clear that this precision varies significantly from sample to sample. The worst number is 2.5% for Nanopowder by American Elements. However, this sample is not stable. It is strongly aggregated, which reduces reproducibility of PSD from one sample preparation to another. Precision of practically all other samples is better than 2%. We would claim this number as characteristic of the method. The powder with the largest size is Z52-500. This powder has the least amount of nanoparticles, about 2%, according to Table 9.4. The powder with the largest amount of nanoparticles is Mallinckrodt. According to the Table 9.4 it contains 20% of

1.0

Cumulative, weight basis

0.9 0.8 0.7 0.6 0.5 Zinc oxide, ACS Acros Organics Zinc oxide, 99.5+% Acros Organics Z50-500 USP Z52-500 USP S80249 by Fisher Scientific Zinc oxide 99.99% by Alfa Aesar Zinc oxide ACS MO Biomedicals, LLC Polystormor, Mallinckrodt Chemicals Nanopowder America Elements

0.4 0.3 0.2 0.1 0.0 10-2

10-1

100 Diameter [um]

FIGURE 9.8 Cumulative particle-size distributions for eight different ZnO samples.

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nanoparticles. We used these two powders for making artificial mixtures for consistency test. Comparison of the PSDs produced by acoustics and electron microscopy is complicated by the difference in their innate basis. Acoustic yields PSD on the weight basis, whereas electron microscopy’s innate basis is number distribution. Difference between weight and number distribution can be tremendous for a broad PSD. Fig. 9.9 shows as an example size distribution for Z52-500 measured using acoustics and recalculated for both weight and number basis. Weight basis indicates the weight of the particles with a certain size. For instance, Z52-500 contains only 2% of ZnO weight in the particles with sizes under 100 nm. Number basis presents number of particles with certain size. For Z52-500 number of nanoparticles with size below 100 nm is about 75%. The same cumulative number is more than 35 times different if expressed either on number or weight basis. TEM photos provide information for calculating PSD on number basis. In order to compare this statistical data with acoustical PSD it should be converted to weight basis. Unfortunately, this approach is not realistic due to the complexity of generating complete and representative PSD using TEM. There is an International Standard describing this procedure, ISO 13322-1 [29]. In particular, this standard determines the number of treated particles required to reach 95% of “correct” PSD. In the case of broad PSD it is several thousands. For comparison, each photo shown in Fig. 9.10 contains only up to 60 particles. Statistical treatment of hundreds photographs for generating a single reliable PSD is not a realistic approach for resolving problems of monitoring nanoparticles content. number basis

1.1 1.0

weight basis

0.9 particle size distribution

374

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 10-2

10-1 100 diameter [microns]

FIGURE 9.9 Particle-size distribution of Z52-500 on weight and number basis.

101

9.4 Monitoring Nanoparticles Content in Systems

Situation would become even more hopeless if we take into account possibility of various artifacts associated with interpretation of complex particles and aggregates shapes in the photographs. It is possible that TEM sample preparation aggregated initially separate nanoparticles. That is why calculation based on number of the nanoparticles in each TEM photo includes particles within aggregates. This might lead to overestimating the nanoparticles content. This short analysis brings us to the conclusion that statistical histograms similar to the ones presented in Fig. 9.10 and calculated using just a single photo for the each sample cannot be directly, quantitatively compared with weight-based produced by Mallinckrodt Probabilities of Particles in the Bin

1.0 Mallingckrodt 0.8 0.6 0.4 0.2 0.0 0

Probabilities of Particles in the Bin

Z52-500

100 200 300 400 500 600 Size (nm)

1.0 Z52-500

0.8 0.6 0.4 0.2 0.0

0

100 200 300 400 500 600 Size (nm)

FIGURE 9.10 TEM photographs and size histogram of the Mallinckrodt and Z52-500 ZnO dispersion. Bar is 500 nm.

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acoustics. We can use these photos only for some qualitative comparison of these two methods. Photographs of all other ZnO samples can be found in [7]. These TEM photos yield some information that correlates with the results of acoustic measurement. Here are some conclusions derived from comparison of the TEM photos that agree with data given in Table 9.4. First of all, sample Mallinckrodt is clearly the smallest. There are two samples by Acros Organic. ACS grade sample has much larger sizes than 99.5%, which agrees with Table 9.4. Samples MP Biomedicals and S80249 have similar size distributions, which agrees with Table 9.4 data. Sample Nanopowder by American Elements is strongly aggregated. It looks like aggregates consist of nanoparticles. We could not find any contradiction between TEM photos and PSD results presented in Table 9.4. This comparison of the TEM photos and Table 9.4 results allows us to conclude that TEM confirms acoustics particle sizing, at least qualitatively. There is a possibility of verifying acoustic particle-sizing consistency by mixing two samples together. Also, this mixing test would yield some information on the method sensitivity to the nanoparticles content. The most informative would be additions of the small increments of the sample with the largest nanoparticles content to the sample with the least nanoparticles content. There are two ways one can estimate nanoparticles content in such mixtures. First of all, one can use known nanoparticles content in the individual samples and also known ratio of the individual samples in the particular mixture. For instance, Mallinckrodt sample contains 20% of the nanoparticles, whereas Z52500 sample contains only 2% of nanoparticles. Content of the Mallinckrodt sample in the each mixture is also known. This is sufficient for calculating expected nanoparticles content in each mixture. On the other hand, measurement of the attenuation spectra for each mixture yields raw data for calculating PSD. This is the same procedure as for individual samples. It provides nanoparticles content in mixtures similar to individual samples. If everything is consistent, both methods should yield the same nanoparticles content in a particular mixture. We have performed this consistency verification test using ZnO sample Z52-500 as a base. This sample contains the least amount nanoparticles according to the acoustics, only 2%. sample weight of the initial sample was 100 g, which is sufficient for filling DT-1200 sample chamber. Additions of the Mallinckrodt sample, with the largest nanoparticles content, to Z52-500 were made in the increments of 2 g and then 4 g. The final mixture contained 26 g of Mallinckrodt sample and 100 g of Z52-500 sample. We can calculate the nanoparticle content in each mixture using following algorithm. Initial 100 g of Z52-500 samples contains 5 g of ZnO, which in turns has only 0.1 g (2%) of nanoparticles. Let us assume that we have added M grams of the Mallinckrodt sample. This addition contains 5  M/100 g on ZnO because sample was

9.4 Monitoring Nanoparticles Content in Systems

prepared at 5%wt of ZnO. Nanoparticles content would be (5  M/100)/5 because only 20% of Mallinckrodt ZnO is nano. Total weight of ZnO in such mixtures would be (5 g þ M/20 g). Total weight of nanoparticles, assuming known individual samples PSD, would be (0.1 g þ M/100 g). Percentage of nanoparticles in the mixture of 100 g Z52-500 samples and M g of Mallinckrodt samples P equals: P¼

0:1 þ M=100  100 5 þ M=20

Alternatively, we can estimate percentage content of nanoparticles in each mixture from acoustic attenuation spectra measured for the mixture. These two numbers should be the same. This is the essence of the performed mixing test. Fig. 9.11 graphically presents the results of the test. Values of the X-axis equals to the value of percentage P calculated for each mixture. Actual results of the acoustic sizing measurements are represented with circles on Fig. 9.11. Solid line represents

7

% nano-particles, measured

6

5

4

3

2

1 1

2

3 4 % nano-particles, estimated

5

6

FIGURE 9.11 Percentage of nanoparticles in Z52-500 sample after incremental additions of Mallinckrodt sample. X-axis is a percentage calculated from the known amount of the added Mallinckrodt sample, assuming that it contains 20% nanoparticles, according to Table 9.4. Y-axis is a percentage calculated from the attenuation spectra, which is measured for the mixture.

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the ideal case when measured content exactly equals to the expected nanoparticles content P. It is seen that deviation of the acoustically measured nanocontent from the expected values is on the scale of 1%. It is significantly less than 2% precision level, which we determined as precision threshold based on the study of individual samples. We can conclude that acoustic spectroscopy yields nanoparticles content in these mixtures, which agrees well in trend with expected values. Finally, we can conclude that particle-size characterization using acoustic spectroscopy allows monitoring of nanoparticles content in the dispersions with broad PSD with precision of at least 2%. It can be achieved in concentrated dispersions with no dilution. This conclusion is confirmed by multiple measurements of individual samples of eight different ZnO dispersions and their mixtures. Electron microscopy qualitatively confirms the results of PSD characterization using acoustic spectroscopy.

9.5 STABILIZING IRON NANOPARTICLES USING GELS This section follows a study done by Torino University published in [30]. The usual approach to minimizing particle aggregation and produce stable dispersions is to enhance the repulsive interaction forces, which can be achieved in three ways: by increasing the surface charge of nanoiron (electrostatic stabilization); by preventing colloids from approaching at close distances (steric stabilization); or by a combination of the two mechanisms (electrosteric stabilization). From a kinetic point of view, stabilization can be achieved by reducing the Brownian motion and, consequently, the probability of the particles to collide. Among other factors, kinetic stabilization can be attained by increasing the viscosity of the liquid. The aims of this study are: (1) to evaluate the possibility of stabilizing for more than 10 days highly concentrated iron slurries (>10 g/L) by increasing their viscosity through the addition of a biodegradable polymer; (2) to identify the most suitable one and the required concentration; and (3) to check the effectiveness of the process under variable ionic strength solution. A 10-days’ timescale, which is largely sufficient for the injection operations, was selected to permit the exploitation of transport mechanisms which take place after the injection and which are due to the natural groundwater movement, and to test the proposed method under severe conditions. Two colloidal dispersions of reactive nanoscale zerovalent iron were provided by Toda Kyogo Corp: bare (RNIP-10DS) and polymaleic acidemodified (RNIP-10E). Both materials were composed of coreeshell nanoparticles dispersed in water. The core of the particles consists of Fe0 and the shell of Fe3O4. The average particle size is about 40 nm. Suitable stabilizer should be harmless, biodegradable, easy to dose in field, and effective over the period needed for migration in aquifer systems (e.g., 10 days). Xanthan gum was identified as the most promising polymer for this study since it fulfills all the requirements; moreover, its light absorption is very low over a broad wavelength spectrum, which is particularly suitable for optical analysis.

9.5 Stabilizing Iron Nanoparticles Using Gels

Authors conducted rheological measurements, sedimentation optical tests, and zpotential measurements. PSD was measured with acoustic spectrometer DT 1200. These measurements were performed using a frequency range between 15 and 100 MHz, in order to avoid the influence of air bubbles. In this range xanthan solutions exhibit a dynamic viscosity which is frequency independent and indistinguishable from the viscosity of water. Once the 6 g/L xanthan solution was proven capable to prevent sedimentation of 30 g/L iron nanoparticles, authors investigated their aggregation behavior through acoustic spectrometry. Based on these measurements, particles are initially nanometric, but within 10 days most of them were agglomerated into large aggregates (of about 10 mm diameter). Stable dispersions (against sedimentation and aggregation) were obtained by reducing iron particle concentration to 15 g/L. In this case no agglomeration occurred as PSD does not significantly change through the duration of the test. This PSD is shown in Fig. 9.12. Median size is very close to the expected size of 40 nm. Model that authors present for interpreting their data resembles very closely to the model suggested by Prof. Berg from Washington University for explaining his group experiments on the similar subject, [31,32]. These studies are discussed in detail in Chapter 14. The structure of xanthan has been extensively investigated and it is well established that at high polymer concentration the molecules form a gel network through

FIGURE 9.12 Particle-size distributions of xanthan suspensions (6 g/L) of 15 g/L DS (bare nanoparticle) (A) and E (polymaleic acid modified nanoparticle) (B) nanoparticles after 10 days from preparation. “Weight basis” indicates that the type of the quantity chosen to define the amount of particles of each size is weight. Reproduced from S. Comba, R. Sethi, Stabilization of highly concentrated suspensions of iron nanoparticles using shear-thinning gels of xanthan gum, Water Res. 43 (2009) 3717e3726.

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hydrogen bonding and polymer entanglement, which results in high viscosity at low shear rates and in a highly pseudoplastic flow. According to the rheological measurements, the xanthan solutions used in this study display a shear-thinning behavior, which allows authors to conclude that they have a network structure. Typically, polymers arranged in a network structure are able to trap colloidal particles and, therefore, to stabilize dispersions. Colloidal particles can either be adsorbed in the network or not: in the first case the polymer integrates the particles within the gel structure, in the latter the nonadsorbing polymer generates a network “around” the particles. Based on the z-potential measurements authors concluded that DS iron nanoparticles are bound to the xanthan network and the system can be modeled as shown in Fig. 9.13A. On the other hand, the adsorption of E-particles cannot be supported, and therefore E-particles may be either adsorbed or be free in the matrix gel (Fig. 9.13A and B). This representation holds only when the polymer network is generated by high xanthan concentrations. Steric stabilization differs from the described model in that it requires the adsorption of a non-gelling polymer. Also the depletion stabilization differs from it, being predicted in the presence of low and high concentrations of a free polymer. By using acoustic spectrometry authors demonstrated that 15 g/L iron suspensions are stable against aggregation. This result can be explained by calculating

FIGURE 9.13 Gelling xanthan polymer in colloidal dispersions: (A) xanthan adsorbed onto bare particles’ surface, and therefore integrates them into the gel structure, (B) xanthan nonadsorbing with respect to E-modified nanoparticles, and therefore generates a network around the particles. Reproduced from S. Comba, R. Sethi, Stabilization of highly concentrated suspensions of iron nanoparticles using shear-thinning gels of xanthan gum, Water Res. 43 (2009) 3717e3726.

9.6 z-Potential for Characterizing Surface Modification

the distance between two neighboring particles starting from particle concentration (15 g/L) and their modal dimension (70 nm). The distance between two neighboring particles is approximately 400 nm, which is larger than the range of the magnetic attractive force reported in the literature. The lack of aggregation can therefore be explained by the low probability of the particles to approach shorter distances due to the effect generated by the biopolymer gel matrix. The approach adopted in other studies to stabilize NZVI particles consists in the adsorption of polymer molecules on the nanoparticle surface. Electrostatically or sterically stabilized particles are free to move and their agglomeration is hindered by the prevalence of thermodynamic repulsive forces. On the contrary, in our study what hinders aggregation is the impossibility for particles to move toward each other. This is due to high xanthan polymer concentration and its ability to form a structure at a larger length scale. This claim is sustained by the high stability of DS and E-particles. Next section presents some studies that correspond to the surface modification model given in Fig. 9.13A.

9.6 z-POTENTIAL FOR CHARACTERIZING SURFACE MODIFICATION (COVERAGE) OF NANOPARTICLES There are six studies [2,33e37] published by different scientists dedicated to modification of various nanoparticles surfaces using z-potential measurement as a tool for monitoring this process. All of them used z-probe from Dispersion Technology Inc. We briefly overview these studies in this section. The first two of these [33,34] were published by the group from Swiss Federal Institute of Technology in Zurich. They studied adsorption of bovine serum albumin (BSA) and lysozyme on alumina and silica nanoparticles. In this study authors investigated the change of z-potential as a function of the amount of protein adsorbed on the surface of colloidal alumina particles. They determined the number of charges involved in the adsorption using titration experiments and proposed a new adsorption model based on the results derived from these experiments. Fig. 9.14 shows z-potential value after the addition of different protein amounts are plotted as a function of pH. The isoelectric point (IEP) of the protein-free Al2O3 was found to be at pH 9.3 after 1 h and at pH 9.0 after 16 h. The IEP decreases with increasing protein amount and levels off at pH 4.9 for 1 h of adsorption time and at pH 5.2 for 16 h of adsorption time. The z-potential at pH 7 decreases from 70 mV (Al2O3 reference, 1 h) to a minimum of about 15 mV after 1 h of protein adsorption time. The same behavior was observed after 16 h, where the z-potential decreases from 60 mV to a minimum of about 15 mV. Fig. 9.15 demonstrate shift of the alumina IEP due to addition BSA. A two-step adsorption model is proposed to further explain the protein adsorption based on the observation that even after the positive surface charge of the Al2O3 particles was fully compromised by the BSA molecules, further protein adsorption can occur.

381

FIGURE 9.14 z-potentials of 2%vol Al2O3 suspensions after 1 h of adsorption time for different amounts of added BSA from pH 3.5 to pH 11.5. Each curve consists of two separate sets of five measurements. Titration directions are indicated by the arrows. 0 ng/cm2 refers to the Al2O3 reference suspension without addition of protein. Reproduced from K. Rezwan, L. Meier, M. Rezwan, J. Varos, M. Textor, L. Gauckler, Bovine serum albumin adsorption onto colloidal alumina particles: a new model based on zeta potential and UV-vis measurements, Langmuir 20 (2004) 10055e10061.

FIGURE 9.15 IEP of alumina with adsorbed BSA as a function of the effectively adsorbed amount of BSA. The dotted lines indicate the amount which is theoretically needed to form a monolayer for different adsorption modes. Reproduced from K. Rezwan, L. Meier, M. Rezwan, J. Varos, M. Textor, L. Gauckler, Bovine serum albumin adsorption onto colloidal alumina particles: a new model based on zeta potential and UV-vis measurements, Langmuir 20 (2004) 10055e10061 [33].

9.6 z-Potential for Characterizing Surface Modification

FIGURE 9.16 Proposed adsorption model for BSA on Al2O3 particles at pH 7. The protein-particle-size ratios are to scale. In the first adsorption phase (left) the side-on monolayer is formed. The more protein the molecules adsorb, the closer the IEP of alumina (initially at pH 9) moves toward pH 5 (IEP of BSA). After the first adsorption layer, the surface charge of the Al2O3 particle is masked; that is, the IEP cannot be shifted anymore with further addition of BSA. In the second adsorption phase (right) the additional BSA molecules form dimers with already adsorbed proteins. Reproduced from K. Rezwan, L. Meier, M. Rezwan, J. Varos, M. Textor, L. Gauckler, Bovine serum albumin adsorption onto colloidal alumina particles: a new model based on zeta potential and UV-vis measurements, Langmuir 20 (2004) 10055e10061.

This model is shown in Fig. 9.16 and explained in the caption to this Figure. There are two more studies [35,37] published by scientists from Universidad de Antioquia, Columbia and Georgia Tech, USA on poly(lactic acid) nanoparticles covered with antigen. Nanoparticles built up from poly(lactic acid) (PLA) and its copolymers with poly(glycolic acid) and poly(ethylene glycol) have drawn much attention because they widely meet the requirements for delivering and transporting various types of therapeutic agents and drugs. The PLA belongs to the aliphatic polyester family, and is considered biocompatible and biodegradable. Profile and mechanism of drug release depend on the nature of the polymer, such as its chemical composition, molecular weight, degree of crystallinity, and the type of interaction between the therapeutic agent and the prepared nanoparticle. Additionally, the nanoparticle surface features govern the uptake capacity of the particle. Weak hydrophobicity and cell affinity, with limited functionality and relative low reactivity of PLA, are major drawbacks that restrict the wide application of PLA nanoparticles in medical areas.

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Table 9.5 Size, z-potential, and Standard Deviations of PLA and PLA-g-MA Nanoparticles and Ovalbumin at pH 6 Nanoparticles

Size (nm)

SD (nm)

OVA PLA PLA-g-MA

12.0 111.0 102.0

4.0 32.0 35.0

z-potential (mV) 12.0 51.0 68.0

V.H. Orozco, J. Palacio, J. Sierr, B.L. Lopez, Increased covalent conjugation of model antigen to poly(lactic acid)-g-maleic anhydride nanoparticles compared to bare poly(lactic acid) nanoparticles, Colloid Polym. Sci. 291 (2013) 2775e2781.

In this study authors grafted maleic anhydride on a commercial PLA in order to obtain maleic anhydride (MA)-grafted PLA (PLA-g-MA). The grafted and nongrafted polymers were used to prepare nanoparticles, in order to determine how this chemical modification affects the amount of adsorbed and chemically bonded ovalbumin (OVA) on the surface of the nanoparticles. The grafting was confirmed by proton nuclear magnetic resonance and z-potential measurements made by electroacoustic spectroscopy. Tables 9.5 and 9.6 present the values of z-potential for particles with modified interfaces. After analyzing the data authors made following conclusion that the most remarkable results of this study were a confirmation of maleic anhydride grafting by the differences in z-potential of the nanoparticle dispersions and the correlation of z-potential values with OVA adsorption and covalent conjugation systems. This methodology shows high potential in the field of formulation of nanoparticlee antigen complexes, which are expected to be used as vaccines against infectious diseases and cancer. There is also a study from Max Plank Institute, Germany [36] on adsorption of polyethylene glycol on silica nanoparticles for modeling polymer electrolytes. Such systems have been the subject of increased interest due to the possibility of their application at ambient and moderate temperature in lithium-based batteries. In the present study authors used polyethylene glycol (PEG) solvents as their higher viscosity promises better network stability at least with respect to sedimentation. Table 9.6 Amount of Adsorbed and Conjugated OVA on PLA and PLA-g-MA Nanoparticles Measured by Bicinchoninic Acid Method Nanoparticles PLA-OVA PLA-g-MA/OVA PLA/OVA PLA-g-MA/OVA

Size (nm) 170.0 175.0 118.0 124.0

SD (nm) 52.0 64.0 45.0 41.0

z-potential (mV) 49.0 54.0 72.0 85.0

V.H. Orozco, J. Palacio, J. Sierr, B.L. Lopez, Increased covalent conjugation of model antigen to poly(lactic acid)-g-maleic anhydride nanoparticles compared to bare poly(lactic acid) nanoparticles, Colloid Polym. Sci. 291 (2013) 2775e2781.

9.6 z-Potential for Characterizing Surface Modification

They used nano-sized silica particles with pronounced Lewis acidity as filters for better adsorption of the anions. They investigated both the electrochemical and mechanical properties to get a deeper insight into the influence of addition of oxide particles to electrolytes. They showed that indeed significant conductivity effects can be achieved which we characterize in terms of mechanism and stability. z-potential (effective) measurements yield information on the surface charge of the SiO2 particles. Studied system is a very complex network structure of undetermined average size, a quantitative evaluation was not possible and the authors concentrated on sign and qualitative variations of the magnitude of z-potential. Therefore the values are termed “effective” z-potentials. Fig. 9.17 displays the changes in this parameter as a function of SiO2 content as well as a function of different LiClO4 concentrations for composite electrolytes containing PEG-150. One notices that z-potential is negative and becomes less negative on increasing concentrations of SiO2-I and SiO2-II as well as on reducing molarity of LiClO4. Differences between both SiO2 types are small. Qualitatively, the situation is similar for PEG 350, yet the z-values are distinctly more negative. As the great difference of z is not reflected in the electrical properties of PEG-150 and PEG-350 electrolytes, this point highlights the fact that the z-potential measurements of the fractal networks are not to be taken quantitatively. We would like to comment here that authors of the study were not aware of recent application of electroacoustics to porous materials, as described in Chapter 13. The sign obtained is an evidence of a negative charging of the SiO2 particle in the presence of salt-containing electrolytes, indicating predominant anion adsorption in agreement with the acidic surface character of SiO2 (the only example where the

FIGURE 9.17 Effective z-potential as a function of weight fraction of SiO2-I or SiO2-II in LiClO4/PEG-150. Salt concentrations ranged from 0.01 to 1 M. Measurements were carried out at room temperature.

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effective z-potential has a positive sign are LiCl-containing systems, an exception that is well-known in literature). The decrease of z-potential with decreasing salt concentration and increasing f(SiO2) (Fig. 9.17) reflects a lower surface charge/surface area ratio. Authors concluded that the combined electrochemical and mechanical studies give a clear evidence of the validity of the heterogeneous doping concept of enhanced Liþ transport in the space-charge zones of the SiO2 particles. The above considerations also give guidelines as to achieve better “soggy sand” electrolytes. Finally, there is a study published by the group from Technical University in Munich, Germany [2] on coating nanosilica with titania nanoparticles, see Fig. 9.18. Peculiar feature of this study is that measurement of z-potential was used as a tool for monitoring coverage and was performed in an online mode. As can be seen from Fig. 9.19, titania and silica are negatively charged in the basic pH range. So at a basic pH, the coating would not take place, because both particles are charged in the same way and there is no electrostatic attraction between the particles, which would lead to the coating. Another important point concerning the adjustment of the pH is the stability of the individual colloidal solutions. The titania nanoparticles are not very stable in the pH range between about 6.4 and 7.0 because of their low surface charge. The silica particles have their lowest stability with rapid gelling in the absence of salts at pH 5e6, in the presence of sodium salts above pH 7.0. As a consequence, a suitable starting point for the coating would be a slightly basic pH in the case of silica and an acidic pH in the case of titania. The coating took place at a pH of 7.5 for silica and 2.0 for titania Fig. 9.19. It appeared that this is a good compromise between colloidal stability of the individual solutions and difference in surface charge. z-potential was measured after each addition of titania sol to the silica dispersion, and therefore an online tracking of the coating process was possible.

FIGURE 9.18 Schematic representation of coating process by heterogenic coagulation. Reproduced from P. Wilhelm, D. Stephan, On-line tracking of the coating of nanoscaled silica with titania nano-particles via zeta potential measurements, JCIS 293 (2006) 88e92.

9.6 z-Potential for Characterizing Surface Modification

FIGURE 9.19 Change in z-potential of 220 nm silica (A), 10 nm titania (B), and 470 nm coated particles (C) with the change of pH. Reproduced from P. Wilhelm, D. Stephan, On-line tracking of the coating of nanoscaled silica with titania nano-particles via zeta potential measurements, JCIS 293 (2006) 88e92.

The pH was also monitored and was dropped from 7.5 in the beginning to about 2.5e3 after the last addition. The results of the experiment are shown in Fig. 9.20. In the course of the gradual addition, the z-potential becomes more and more positive, which is on one hand associated with the coating of titania on the silica spheres and, on the other hand with the decrease in pH value.

FIGURE 9.20 Change in z-potential of silica particles with a particle size of 150 (A), 220 (B), 470 (C), and 590 nm (D) with the change of the amount of added titania nanosol. Reproduced from P. Wilhelm, D. Stephan, On-line tracking of the coating of nanoscaled silica with titania nano-particles via zeta potential measurements, JCIS 293 (2006) 88e92.

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Both silica and titania are more positively charged in the acidic pH range, but only titania has a positive z-potential in this range. When the slope in the graphs (Fig. 9.20) drops and becomes almost zero, the coating process is finished. That means the silica spheres are completely covered with the layer of titania. The slope is never zero, because the pH value does not stay stable during the experiment and the z-potential is very sensitive toward the changes of pH. A slight rise in z-potential can be observed, as the pH value is continually decreasing. The amount of titania needed for the coating depends on the size of the silica particles. Larger particles have a lower surface area and less titania is needed to coat them. For the silica particles with a size of 590 nm about 12 mL of the titania sol is needed for complete coating, while for 470-nm particles about 14 mL of the titania sol is needed. The small silica particles, with sizes of 150 and 220 nm, respectively, have almost the same consumption of titania, about 16 mL. That means they have the same specific surface area and the 220-nm particles may have a rough surface. To confirm that the silica spheres were really coated with titania, the z-potentials of the coated particles were measured depending on the pH value (Fig. 9.21). All coated particles show the same pH dependency as pure titania. Their IEP was between 5.7 and 6.7, while the pure titania was 6.7. Authors concluded that the measurement of z-potential showed that the titania coating was successfully applied to the silica spheres.

FIGURE 9.21 Change in z-potential of the coated 150 (A), 220 (B), 470 (C), and 590 nm (D) silica particles with the change of pH. Reproduced from P. Wilhelm, D. Stephan, On-line tracking of the coating of nanoscaled silica with titania nano-particles via zeta potential measurements, JCIS 293 (2006) 88e92.

9.7 Method for Characterizing Nanodispersions

9.7 LIMITATION OF ULTRASOUND-BASED METHOD FOR CHARACTERIZING NANODISPERSIONS

attenuation [dB/cm/MHz]

We consider ultrasound-based methods well suited for characterizing various nanodispersions and having many advantages over other traditional techniques, such as dynamic light scattering. However, there are some limitations of these methods that we would like to point out here for preventing potential confusion. The first limitation is the smallest particle size. Specifications of acoustic instruments usually place this number at 10 nm. We would like to justify this number. In order to do this, we plotted attenuation spectra calculated for silica nanodispersions with sizes 31, 20, and 10 nm in Fig. 9.12, along with background intrinsic attenuation of water. It is seen that reduction of the particle size leads to the decay of attenuation. Consequently, attenuation spectra for smaller particles approach background attenuation of water because particles contribution to attenuation diminishes. At some point contribution of the particles becomes comparable with precision of attenuation measurement and becomes undistinguishable from the liquid background. Fig. 9.22 indicates that for 10% silica dispersion. This smallest size limit can be attributed to 10 nm with attenuation very closely approaching that of water. This limit is valid for certain density and volume fraction of particles. Increasing density contrast relative to 1.2 for silica would allow measurement of somewhat smaller particles. Same would happen with increasing volume fraction. However, we would not recommend applying this particle-sizing method for particles smaller

0.4

31 nm Ludox

0.3

20 nm Ludox

0.2

10 nm Ludox water

0.1

0.0 1

10 frequency [MHz]

100

FIGURE 9.22 Theoretical attenuation spectra for 10% silica with different sizes shown in the legends and intrinsic attenuation of water.

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than 10 nm. Precision of the measurement must be improved, which would require thermostabilization of the sample. This analysis is valid for rigid particles. Situation with soft particles might be different. For instance, we presented data on microemulsions with size down to 5 nm in Chapter 10. This size is confirmed with independent methods. This makes us optimistic of applying this particle-size method for microemulsions. Question remains open for other soft particles, such as proteins. Conversion of the attenuation into the soft PSD requires thermal properties of the particle material, such as thermal expansion, heat capacity, and heat conductance. These numbers are easily measurable for oils in case of microemulsions. In the case of proteins and other macromolecules these number are unknown. This is the main obstacle of applying this method for sizing of proteins. There is one more possible complication in measuring size of very small particles. Intrinsic attenuation of the liquid is somewhat dependent on chemical composition of the liquid and temperature. Contributions of these factors could become comparable with contribution of particles when particle size becomes very small. It is our experience that this possibility must be taken into account when particles size becomes 10 nm and lower. There are also some warnings regarding using ultrasound-based methods for measuring z-potential. Measurement of the electroacoustic signal does not present a problem even for the smallest particles. Calculation of z-potential, on other side, does require some caution. The problem arises because particle size might become comparable with Debye length. Traditional Smoluchwski theory is limited only for thin DL, when particle size exceeds Debye length at least 10 times. This condition becomes somewhat violated even for silica Ludox in aqueous solution, as it follows from numbers presented in Section 9.1. Decreasing value of the parameter ka would eventually require application of the advanced theory for calculating z-potential, if user needs accurate absolute value of this parameter. This theory must take into account not only surface conductivity effects, but potential overplay of the DLs. Appropriate theory is given in the Chapter 5. At the end, we would like to repeat that these points of caution should not affect optimism regarding using ultrasound-based methods for characterizing nanodispersions.

REFERENCES [1] F. Stenger, S. Mende, J. Schwedes, W. Peukert, Nano-milling in stirred media mills, Chem. Eng. Sci. 60 (2005) 4557e4565. [2] P. Wilhelm, D. Stephan, On-line tracking of the coating of nano-scaled silica with titania nano-particles via zeta potential measurements, JCIS 293 (2006) 88e92. [3] X.Z. Wang, L. Liu, R.F. Li, R.J. Tweedie, K. Primrose, J. Corbett, F.K. McNeil-Watson, Online characterization of nano-particles suspensions using dynamic light scattering, ultrasound spectroscopy and process tomography, Chem. Eng. Res. Des. 87 (2009) 874e884.

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FURTHER READING [1] ISO 20998, ISO/TC 24/SC 4, ISO Standard. Characterization of Particles by Acoustic Methods, 2005. [2] N. Bell, J. Cesarano, J.A. Voight, S.J. Jockwood, D.B. Dimos, Colloidal processing of chemically prepared zinc oxide varistors. Part 1. Milling and dispersion of powder, J. Mater. Res. 19 (5) (2004) 1333e1340.