Dynamic light scattering and zeta potential measurements: effective techniques to characterize therapeutic nanoparticles
Abbas Rahdar
1
(
Department of physics, university of zabol, zabol, iran
)
nooshin amini
2
(
Master degree of Nanochemistry, Faculty of Pharmaceutical Chemistry, Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran (IAUPS)
)
Fazeh Askari
3
(
department of nanochemistry, university of Kharazmi
)
Md. Abu Bin Hasan Susan
4
(
Department of Chemistry, University of Dhaka, Dhaka 1000, Bangladesh
)
الکلمات المفتاحية: Zeta Potential, Dynamic Light Scattering, Hydrodynamic Size,
ملخص المقالة :
Particle size and surface charge are key parameters of nanoparticles (NPs), especially therapeutic NPs, to influence the kinetics in vivo and interaction with the cellular and biological membranes and decide the efficacy for biological systems. Characterization of nanomaterials including both nanoparticles and micelles has therefore been a major issue in nanomedicine research to develop well-defined nano-formulations with focus on therapeutic goals. Dynamic light scattering (DLS) and zeta potential (ZP) measurements are widely accepted techniques for proper determination of the particle size and surface charge of therapeutic NPs. These have so far been easy, simple, and reproducible tools. However, there are challenges to interpret and systematically analyze data effectively due to lack of adequate understanding of the principles involved and impeccable background for operation of the system along with competence of sample preparation and characterization and so on. This review addresses the issues with focus on the fundamental principles involved in the techniques of DLS and ZP. The ultimate goal has been development of knowledge–base with a view to better analyzing and interpreting results for evaluation of hydrodynamic size, diffusion, inter particular interactions and stability of colloidal systems based on surface charge of therapeutic NPs.
Dynamic light scattering and zeta potential measurements: effective techniques to characterize therapeutic nanoparticles
ABSTRACT
Particle size and surface charge are key parameters of nanoparticles (NPs), especially therapeutic NPs, to influence the kinetics in vivo and interaction with the cellular and biological membranes and decide the efficacy for biological systems. Characterization of nanomaterials including both nanoparticles and micelles has therefore been a major issue in nanomedicine research to develop well-defined nano-formulations with focus on therapeutic goals. Dynamic light scattering (DLS) and zeta potential (ZP) measurements are widely accepted techniques for proper determination of the particle size and surface charge of therapeutic NPs. These have so far been easy, simple, and reproducible tools. However, there are challenges to interpret and systematically analyze data effectively due to lack of adequate understanding of the principles involved and impeccable background for operation of the system along with competence of sample preparation and characterization and so on. This review addresses the issues with focus on the fundamental principles involved in the techniques of DLS and ZP. The ultimate goal has been development of knowledge–base with a view to better analyzing and interpreting results for evaluation of hydrodynamic size, diffusion, inter particular interactions and stability of colloidal systems based on surface charge of therapeutic NPs.
KEY WORDS: Dynamic light scattering, Zeta potential, Hydrodynamic size, Diffusion, Surface charge, Nanomedicine
INTRODUCTION
Nanomaterials, in particular, nanoparticles (NPs) and micelles of nanodimension have received an upsurge of interest in biotechnology and medicine. This necessitates appropriate tools with special characteristics such as accessibility, fastness, and effective resolution to manipulate nanomaterials into biological environments and to characterize nanometer-sized particles within colloidal systems [1-2]. Analysis of quality control of nanomaterials has been a major issue for their application in different industrial fields such as nanomedicine. However, standard tools in this regard have been very limited and proper understanding is still an elusive goal [3]. For interpretation of results it is imperative that the data should be reliable and originated from apposite techniques and the nanomaterials need to be characterized sufficiently especially within colloidal systems.
On the other hand, safety is a key challenge for characterization and application of nanomaterials in biological media. It is widely believed that the safety concern is related to physiochemical properties of NPs including size, surface charge, and shape of particles, ligand-based surface functionalization, impurity [4-8] and so on. The most commonly used physicochemical properties to reveal the outstanding differences of nanomaterials with their counterpart bulk materials have been conductivity, fluorescence, and magnetism [9-12].
Living systems, in general, experience biological effects from NPs and micelles which include cellular uptake, toxicity, and dissolution [13-15] due to the profound influence of particle size and surface charge of the nanosturctures. Studies reported so far highlight the importance and influence of these two parameters in different fields especially biomedical sciences. These include release profile from the designed nanomaterials to deliver drugs across the blood–brain barrier (BBB) [16], potential candidature of bioactive glass NPs for bone tissue engineering [17], effective mucus diffusion and permeation combined with higher cellular uptake based on self-emulsifying drug delivery systems containing phosphorylated polysaccharides (when droplets reach absorptive epithelium membrane) [18], and a promising strategy for future gene delivery systems [19]. These have been responsible for phospholipid- Al2O3 particle interactions [20] and dictate zeta potential measurement for air bubbles in protein solution [21], electrostatic interactions governing the kinetics of the adsorption of full-length recombinant human amelgenin (rH174) onto hydroxyapatite (HAP) [22], and zeta potential (ZP) measurement of nanomaterials to study their colloidal stability [23].
Dynamic light scattering (DLS) also otherwise known as photon correlation spectroscopy (PCS) or quasi-elastic light scattering (Fig. 1) and ZP measurements (Fig. 2) are well-known tools and techniques to study the hydrodynamic size and surface charge of nanomaterials within colloidal systems with different potential applications especially pharmaceutical applications [21-22].
Fig.1. Schematic of DLS tool.
Fig. 2. Schematic of zeta potential tool.
The sizes of NPs in colloidal systems are obtained from measurements of scattering intensity of NPs in Brownian motion when illuminated by a monochromatic beam of light [24]. For charged NPs in colloidal systems factors such as interactions between particles, molecules and ions create adsorbed layers on NPs [25]. The surfaces of the dispersed particles are altered depending on the adsorbed layer [25]. The DLS and ZP techniques utilize these properties of colloid dispersions in order to determine the hydrodynamic size and surface charge of NPs [26].
The quality of reported data related to size and charge of therapeutically NPs by DLS and ZP has so far not be always excellent in nanomedicine research due to lack of caution and proper training. Investigation of these parameters are crucial during development of nanosystems related to medicine and biomedical sciences specially nanoparticulate-based drug delivery systems regarding the fact that biological matrices are well-known to alter these two features of NPs with different mechanisms (e.g., protein adsorption causing the characteristic corona) [19,20].
In this review, an effort has been made to offer a simple account on measurements of DLS and ZP and discuss factors influencing the measurements and quality of data. The kinetics in vivo as well as interaction with the cellular and biological membranes related to therapeutic NPs, which are affected from their size and surface charge [24-25] have also been revealed.
DYNAMIC LIGHT SCATTERING
Scattering of light by nanoparticles dispersed in a colloidal system
NPs within colloidal systems scatter incident light proportional to the 6th power of their radius [30-32]. Scattering light by particles with λ/10 in size (λ denotes the wavelength of the incident light) is elastic and known as Rayleigh scattering [30-32]. On the other hand, scattering light by particles greater than λ/10 in size will be Mie scattering (inelastic scattering) where the scattered light is angle-dependent [30-32] where the scattered light is the most intense towards the direction of the incident light [31]. Hydrated shells wrapped within a cloak of molecules (which are not the ingredients of the NPs itself) are formed on surface of dispersed NPs in colloidal system (corona) [30, 33-34]. The corona is formed by two shells: hard and soft [30, 33-34]. The hard corona refers to the inner stable layer tightly bound to the NP surface and the soft corona is the layer on top of the hard corona with a composition different that of hard corona [30, 33-34]. Different compositions related to hard and soft corona in addition to structure of NP are believed themselves to have a key role in scattering light by particle surface. In this case, particles are different in surface chemistry and composition than those originality synthesized [30, 33-34].
Casals et al [27] used DLS to confirm the formation of the protein corona after exposure of metallic Au NPs (4 to 40 nm) as well as to monitor the time evolution of the inorganic NP−protein corona formation and to characterize the stability of the NPs and their surface state at every stage of the experiment. Liu et al [28] also used DLS for charaterization of gold nanorods and investigation of the adsorption of different proteins including bovine serum albumin (BSA), human serum albumin (HAS), immunoglobulin G (IgG), and immunoglobulin A (IgA) with gold nanorods with the same diameter but different aspect ratios. Protein adsorption to gold nanorods has been strongly dependent on the aspect ratio of the nanorods, and varied significantly from protein to protein. DLS has been a valuable tool for nanorod characterization and understanding the gold nanorod–protein interactions. Waghmare et al [29] employed DLS to monitor adsorption of Bovine serum albumin (BSA) protein onto silver NPs. They reported increase in adsorption with increase in average hydrodynamic radius of BSA-Ag NP corona from 24 to 35 nm. This can guide drug design for tumor-targeted therapy.
Understanding the dynamics of the growth of protein corona onto NP-surfaces by DLS is important from the perspective of how the NPs behave in vivo [49]. DLS technique can hence, be an efficient tool along with other techniques such as isothermal titration calorimetry (ITC), Fourier-transform infrared spectroscopy (FT-IR) and fluorescence spectroscopy in different research especially in nanomedicine.
Autocorrelation function and dynamic parameters for characterization of nanoparticles by dynamic light scattering
DLS is an effective tool to characterize dynamic parameters of NPs including the diffusion coefficient and particle size within a colloidal system. The time-dependent scattered light intensity from a nano-colloidal solution is a fluctuating quantity that depends on the size, Brownian motion and diffusive behavior of NPs in solution and viscosity of continuous phase. These fluctuations can be characterized according to the normalized autocorrelation function, g1(τ), of the scattered electrical field for a given delay time, τ, which contains information about the structure and dynamics of the scattered particles [35-42, 63-65].
Where, E* is the complex conjugate of E. Experimentally, the intensity autocorrelation function, g2(q, τ), is determined as following [35-42 ,63-65]:
The normalized autocorrelation function, g2(q,τ), is converted to the autocorrelation function of the scattered electrical field, g1(q,τ) by the Siegret relationship [35-42, 63-65].
g2 q, |A exp (Γ )|2 | (3) |
Here, A is an instrumental constant. For a colloidal system containing monodisperse colloidal particles like micelles and other nanoparticles, the function of g1(q, τ) is represented by a single exponential decay curve [35-42, 63-65].
g1 q, A exp (Γ ) (4)
It is important to note that there is a digital correlator within the DLS tool (Fig.1) that measures the degree of similarity between two signals over a time period. If the intensity signal of a particular part of the speckle pattern at one point in time is compared to the intensity signal a very short time later, the resulting two signals are very similar and they strongly correlate with each other. On the other hand, correlation between two signals decreases with time due to decrease in similarity of two signals by Brownian motion (Fig. 3).
The decay rate, Γ, is converted to diffusion coefficient using [35-42, 63-65]:
D = Γ/q2 (5)
where q is the scattering vector [59-63]. Finally, the diffusion coefficient of nanoparticles or micelles can be characterized as the hydrodynamic radius (Rh) according to the stokes-Einstein relation [35-42, 63-65]:
Rh= 
(6)
Where KB is Boltzmann’s constant, T is the temperature in Kelvin, and η is the viscosity of the solvent.
An increase in particles sizes results in a slower exponential relax with a smaller relax constant, as the fluctuations in light intensity change more slowly; whereas for smaller particles, a rapidly relaxing exponential function is obtained, with a large relax constant. Therefore, the inverse correlation time is inversely proportional to the size of NPs (Fig.3) [35-42].
Fig. 3. Schematic related to relationship particle size with the scattered light intensity and autocorrelation function.
The hydrodynamic radius is the radius of the hypothetical hard sphere that diffuses with the same speed as the particle is analysed under DLS [35-42]. It is well-known that RH is a mathematical measurement since hard spheres rarely exist in a colloidal system. In other words, the dispersed particles within a colloidal system are hydrated/solvated (formation of corona) form of corona and are often not spherical.
The rotational correlation time (τr) of spherical droplets are calculated according to hydrodynamic model of the Stokes-Einstein-Debye (SED) [35-42].
Hear, rh is hydrodynamic diameter of water nanodroplets.
Rahdar et al [41] focused on dynamics of water nanodroplets containing xanthen gum (XG) polysaccharide by using DLS technique; they synthesized water nanodroplets containing XG with hydrodynamic diameter in the range of 5-35 nm at the different XG concentrations by water-in-oil aerosol T (AOT) microemulsion system as a function of mass fraction of droplet (MFD) at a the constant water content (W = [H2O]/[AOT] = constant).
It is worth mentioning that the nanometer-sized water droplets within water-in-oil surfactant microemulsion are formed based on specific ratios of the water, surfactant, and oil. The structure, size, and property of water nanodroplets in the microemulsion are affected by two parameters: i) the water-to-surfactant molar ratio, popularly is shown as the W value, W= [water or polar solvent]/[surfactant]) [35-42] and ii) the droplet-to-total components mass ratio, generally is represented as the MFD value (or volume fraction of nanodroplet), MFD = Mnanodroplet/Mtotal [35-42].
In the work by Rahdar et al [41], dynamical parameters of the diffusion and size distribution characterization of the nano-sized water droplets containing polysaccharide of the XG were derived from the autocorrelation function of water nanodroplets by DLS technique. The autocorrelation function was plotted against decay time for nanodroplets for different XG concentrations at MFD values of 0.01, 0.04, and 0.1. Fig. 3 represents a typical autocorrelation function versus decay time plot for nanodroplet containing XG.
Fig.3. The autocorrelation function of nanodroplets versus time at Xanthan gum polymer of 0.0000625 at 25 ºC (reproduced from Ref. 41 with permission).
To obtain decay rate of nanodroplets (Fig. 4), the autocorrelation function of nanodroplets was fitted with a single exponential function according the relation in eq.(4) [35-42].
Fig.4. The inverse correlation time versus droplet mass fraction of micelles containing xanthen gum at MFD of 0.0000625 (reproduced from Ref. 41 with permission).
To understand the inter-particular interactions type within colloidal systems, it is necessary to obtain the collective diffusion of NPs in systems. Therefore, the collective diffusion of water nanodroplets was calculated according to eq. (5) [35-42]. The collective diffusion coefficient of water nanodroplets versus MFD at different concentrations of XG polysaccharide are shown in Fig.5.
Fig.5. Diffusion Coefficient of water nanodroplets containing different xanthen gum concentrations as a function of MFD (up triangle): 0.0000625 (circle): 0.0000157 at 27oC (reproduced from Ref. 41 with permission).
As is apparent from Fig.5, the collective diffusion as a function of MFD has negative slope for water nanodroplet sample containing XG at 0.0000157 and positive slope for sample containing XG at MFD of 0.0000625. In other words, the inter-nanodroplets interaction changed from attractive to repulsive as concentration of XG biopolymer increased as a function of MFD due to adsorption of XG polysaccharide at the interface of AOT nanodroplets and resulting in repulsive interaction of the droplet-droplet thus increasing concentration of XG biopolymer within water nanodroplets [41]. According to study by Rahdar et al [41], change in slope of the curve for diffusion with MFD is interpreted as change in interdroplet interaction within colloidal system.
The diffusion is converted to the hydrodynamic radius of water nanodroplets by using the Stokes-Einstein according to eq. (6). The hydrodynamic diameter versus MFD at different concentrations of XG is shown in Fig. 6.
Fig.6. Hydrodynamic diameter of water nanodroplets containing different xanthen gum concentrations using Stokes-Einstein Relation versus MFD, (up triangle): 0.0000625 (circle): 0.0000157 (reproduced from Ref. 41 with permission).
It is clear from data in Fig. 6 that with increasing the polysaccharide concentration of XG within the water nanodroplets, hydrodynamic diameter of water nanodroplets, in general, decreases.
In a recent study Rahdar et al [42] focused on dynamics of water nanodroplets containing D-(+)-Glucose by using DLS technique. They calculated the collective diffusion coefficient of nanodroplets for different D-(+)-Glucose concentrations. For different D-(+)-Glucose concentrations within the water-in-oil microemulsions, a single relaxation curve was observed for the water droplets. DLS of water nanodroplets indicated that the diffusion coefficient of water nanodroplets increased and their size decreased as concentration of D-(+)-glucose within water nanodroplets increased. The interaction between droplets changed from attractive to repulsive as concentration of D-(+)- glucose within droplets increased [42]. Rahdar et al [35] also used DLS technique to report change at inter-droplet interactions from attractive to repulsive as a function of MFD by decreasing concentration of Rhodamine B within water-in-oil AOT microemulsion. Therefore, DLS is an effective tool to discuss inter-particular interaction within colloidal systems.
Rudyak et al [61] studied the diffusion coefficient of NPs within a dense molecular medium. By using molecular dynamics method they reported that the autocorrelation function of the NPs is well described by a superposition of two exponents with different characteristic relaxation times. It was found that the autocorrelation function of NPs relies on the particle radius and the solvent density.
Diffusivity, correlation functions and power spectral density of velocity fluctuations in monolayer grapheme have been investigated in detail by Rengel and Martin [62]. Autocorrelation function of samples depended on microscopic nature of fluctuations. They relied on different conditions for parallel and perpendicular directions with regard to the applied field and the microscopic wave vector distribution of carriers. A monotonic decay was observed for the correlation function for perpendicular fluctuations and larger correlation times, while a negative peak in its correlation function was marked for the fluctuations in the parallel direction.
The literature is very rich for characterization of sizes of different NPs such as, FePt, NiO, and iron oxide. The DLS technique has proved to be powerful for evaluating zize of such NPs through plentiful reports [65].
Size distribution from dynamic light scattering measurements
The DLS tool generates useful data for characterization of nanomaterials including therapeutic NPs which include the correlation data, intensity-, volume-, number-, and z-average size, and polydispersity index (PDI) and so on. Correlation data have been discussed in the preceding section.
The intensity weighted distribution shows how particles with different sizes within colloidal system are detected from a fit to the autocorrelation function of the measured light scattering. The size related to intensity is very sensitive to very small numbers of aggregates within colloidal system. Since scattered light intensity by a particle is proportional to the 6th power of its radius, even few particles of larger size scatter more light than many smaller particles. The size distribution by the number and volume is related to number of particles with a certain size, and volume occupied by the particles. The z-average is an average size from intensity, volume, and number originating from the distribution fit.
The PDI provides an indication of the width of the overall distribution denoting polydispersity or monodispersity of particles within colloidal system. For Gaussian distribution, the PDI is equal to the (width/mean)2.
DLS technique has been successfully applied by Nobbmann et al [68] to characterized size of TiO2 NPs. The size distributions could be well distinguished from the difference between intensity (85.6 nm), volume, and number (51.9 nm) distributions. The technique has also been applied to gold NPs by Khlebtsov et al [69] and the different sizes for gold NPs were found to show good reflection in the intensity, volume, and number distributions.
Decisive factors for characterization of nanoparticles by dynamic light scattering
Sample preparation is critical in DLS analyses. The samples are prepared either in different solvents such as water, methanol, ethanol, toluene or diluents ones. Some solvents for instance toluene can scatter light while some like DMSO exhibits significant changes in viscosity with variation in temperature [30, 43-45]. The specimens for DLS measurements should be clear and homogeneous. Any precipitation proves the existence of bigger particles which can be due to poor dispersion or inadequate sonication. Due to the lack of ions, use of deionized (DI) water is usually not recommended since it fails to cover the particles from long-range interactions. Therefore, the size in DI water is always larger than their actual size. Dilute saline water gives better results as the ions shield the particles from long-range interactions. Large suspended particles of low density on top of the solvent layer may also give erroneous results. For powder formulations stirring quickly can dissolve the NPs to gain a stable and homogeneous dispersion [30, 43-45].
Increasing concentration of NPs results in multi-scattering in which the scattered light from one particle interacts with others before arriving at the detector. As a result, the obtained size is misleadingly smaller. Agglomeration occurs at high concentrations [30, 46-48]. On the other hand, use of diluted samples may avoid sufficient scattered light for the investigation. Therefore choice of ideal sample concentration is crucial [30,46-48].
It is troublesome to get high quality results from dispersions with aggregated NPs. Immoderate scattering also covers the low intensity light scattered from tiny particles. Subsequently, broadened peaks rise whereas the exactness of the information is diminished. Therefore, DLS is reliable just at dilute concentrations. To encounter these consequences, diverse surfactants are usually used to create stable dispersions [49].
NPs can have different shapes besides spheres including nanotubes and nanostars. The RH or rH determined by DLS is radius of a hypothetical firm sphere moving at the identical pace to that of a spherical NP in the dispersion [50-51]. Stokes-Einstein equation has been modified to deal with cylindrical structures [50-51].
Comparison of particle size based on conventional techniques with that of dynamic light scattering
It is conceivable to determine the size distribution of NPs from transmission electron microscopy (TEM) photographs with data sets on their mean size [89, 90]. However, such data from TEM images usually do not conform well with the information received from DLS. This can be attributed to the fact that DLS is an intensity-based method while TEM could be a number-based one which makes them basically distinctive [52-54]. Whereas the samples for DLS are solvated, TEM works on dry samples. DLS measures the RH of the dispersed particles while TEM anticipates the surface area (Fig.7). Subsequently, the measured particle size by DLS is usually larger than that of the TEM. An advantage of DLS is its capability to measure a large number of particles compared to TEM. In this manner, DLS gives more vigorous information on size distribution [52-54].
Fig.7. Schematic of hydrodynamic size.
Since DLS measurement is based on intensity, quite reasonably it places higher emphasis on the particles of larger sizes. When the quality of data is sufficiently good, it can be transformed to a number based distribution. In contrast, the basis of measurements in TEM is number and therefore the size distribution shows greater emphasis from the smallest components in the system [25-42]. The hydrodynamic size estimated from DLS is in fact the size of the NP plus the solvated liquid layer around the particle. In sharp contrast, the TEM measurements give the actual size of the NPs. More importantly, the size measured by DLS is merely an estimated one, not the actual size of NPs as determined by TEM [25-42].
DLS is superior to other techniques for providing information regarding particle sizes of NPs for a number of advantageous features. First of all, time of measurement in DLS is short and the entire process does not require rigorous experimentation and expertise. The technique is non-invasive and non-destructive giving the opportunity to use the sample for other measurements. This feature is greatly appreciated for recycled use of NPs with an expensive surface functional group, such as an enzyme or molecular ligands. DLS is tremendously sensitive towards the presence of small aggregates due to the proportionality of the scattering intensity to the sixth power of the particle radius. This allows efficient avoidance of incorrect measurements even with the occurrence of limited events of aggregation. DLS has thus been referred to one of the very powerful techniques in monitoring the colloidal stability of NPs in suspension and for therapeutic NPs the features have been very unique.
AFM has also been a powerful device to portrait NPs. AFM offers exact data on particle size and shape; it is capable to diagnose particles with diverse sizes in a blend. The number of particles analyzed by AFM however, is much smaller; consequently, DLS offers an improved size distribution. On the other hand, characterization of NPs by AFM provides a rapid and accurate. Characterization of NPSs by AFM has definite advantages over DLS for non-monodispersed solutions. [55].
Sedimentation methods have also received demands to determine the size of NPs [30,56-57]. In summary, these techniques utilize high centrifugal energy to deposit NPs in fraction based on density. The size of the NPs is evaluated by observing the deposition of the particles on a rotating disc either by x-ray absorbance or monochromatic light. The mathematical operator for these techniques is discussed widely in the literature [30,56-57].
ZETA POTENTIAL AND PRINCIPLE
The zeta potential is a function of surface charge in colloidal dispersions. It is an effective tool to measure magnitude of the electrostatic interaction between particles within colloidal systems of nanodimension. In other words, the ZP is commonly used to predict and control dispersion stability. The ZP is a scientific term for electrokinetic potential in colloidal dispersions [16-21,23,26].
In process related to study stability particles in colloidal dispersion by the ZP, a controlled electric field is applied via electrodes immersed in a sample suspension and this leads to moving the charged particles towards the electrode of opposite polarity. The ZP reflects the potential difference between electric double layer (EDL) of electrophoretically mobile particles and the layer of dispersant around them at slipping plane [16-21,23,26].
It is important to mention that when a charged particle is dispersed in a liquid, an adsorbed double layer, known as EDL [30, 58], is created on the particle surface. The inner layer includes the molecules/ions with opposite charge to that of the particle known as Stern layer. Beyond Stern layer the electrostatic effects decreases due to the surface charge on the particles according to Debye law [30, 58].
Electrkinetic phenomena for zeta potential measurement
A group of phenomena, generally referred to as electrokinetic effects, can be used as the basis for determination of ZP. Four related key phenomena are: electrophoresis, electro-osmosis, streaming potential and sedimentation potential [21,23,30]. As a whole, electrophoresis is the movement of charged particles which are suspended in a liquid under the influence of an applied electric field. The electrophoretic velocity is proportional to the electric field, with the proportionality constant called the electrophoretic mobility. Zeta potential is directly related to electrophoretic mobility [21, 26, 30].
Zeta potential can be measured by means of electrophoresis. The electrophoretic mobility (me) is calculated according to me= V/E, where V is particle velocity (mm/s) and E is the electric field strength (Volt/cm). The zeta potential is then calculated from the me by Henry’s equation [21,23,30]:
me= 2ere0zf (ka)/3h (7)
Here, er is dielectric constant, e0 is permittivity under vacuum, z is zeta potential, f(ka) is Henry’s function, and h is viscosity.
The Debye length can be calculated from the following equation:
K-1= ((ere0kBT)/(2000 e2 IN))0.5 (8)
Where, e = electronic charge in Coulombs (= 1.6022 × 10-19 C), N = Avogadro number, and I = ionic concentration (in mol L-1).
When value of f(ka) is considered 1.5, it means that the EDL is comparable with the particle radius, (particles are larger than 0.2 microns dispersed in electrolytes containing more than 10-3 molar salt). In this situation, Henry’s equation modifies into the Helmholtz – Smoluchowski (HS) equation (Fig.8) [21,23,30]:
me= ere0z/h (9)
When value of f(ka) is considered 1, it means that the EDL is much bigger than the particle itself due to smaller (£100) particle dispersed in electrolytes containing more than 10-5 molar salt. In this situation, the Henry’s equation can be modified as Hückel equation (Fig.8) [21,23,30]:
me= 2ere0z/3h (10)
The Hückel equation is usually used in ceramic industry and it is not a useful equation for pharmaceutical preparation [21,23,30].
Fig.8. Huckel and Smoluchowski approximations.
Electrophoretic light scattering and electroacoustic phenomenon in zeta potential measurements
In recent years, the technique of electrophoretic light scattering (ELS) has been applied to measure electrophoretic mobility and then calculating ZP. In an ELS instrument, a laser beam passes through the electrophoresis cell, irradiates the dispersed particles in the system, and is scattered by the particles. A part of the laser beam is diverted before it reaches the cell. This beam is combined with the reference beam to determine the sign of the charge on the particle, and then calculation of ZP [21,23,30].
Electroacoustic effects are result of coupling between acoustic and electric fields. In this technique the particles in a sample oscillator under the electric field and the oscillations are analyzed on magnitude and phase angle to measure the particle size and ZP. This technique is less popular in drug delivery research.
Decisive factors for characterization nanoparticles by zeta-potential measurements
The most influential factor that affects ZP especially in aqueous dispersions is pH. The change in magnitude with acidic and basic pH causes the change in ZP. The isoelectric point is the pH at which the ZP is zero. At this pH the repulsive forces are zero, and aggregation occurs [21, 23, 30]. On the other hand, as ionic strength of colloidal system increases the EDL compresses while the ZP decreases in magnitude and vice versa [21, 23, 30].
The particle concentration can have a significant impact on ZP. The effect depends on relative valence of ions and on their concentration. All together, it can be stated that increasing concentration may decrease ZP with lesser stability of the dispersion [21,23,30].
The most popular application of ZP data is related to colloid stability. According to literature related to drug delivery issues, values of ZPs are classified to ±0– 10 mV (highly unstable), ±10–20 mV (relatively stable), ±20–30 mV (moderately stable), and ˃±30mV (highly stable) [30,59-60].
Literature review reveals that although values of ZP are indications to study colloid stability, but they are not enough. [30,59-60]. The ZP provides insight on the electrostatic repulsive forces, but it doesn’t provide information on the attractive van der Waals forces. There are theories focused on the understanding of attractive forces existing in nature discussion of which is beyond the scope of current review.
The attractive van der Waals forces are related to the Hamaker constant [30,59-60] which corresponds to the difference between the refractive index (RI) of the particle and the dispersant, indirectly. Therefore, if the Hamaker constant is low the van der Waals attractive force also becomes weak and then mild electrostatic repulsion reflected by low ZP (10–15 mV) may be enough to ensure colloid stability. On the other hand, steric interactions can also help to colloid stability. For example, some of water-in-oil emulsions are highly stable in spite of low ZP [30, 59-60].
A change in the surface charge of NPs brings about change in the ZP changes; however, the ZP is not a direct measure of surface charge. The ZP is related to surface chemistry; anything that changes surface charge will cause some change in the ZP (e.g., pH, which is relevant for nanoformulations). Even a small percent of a component which adsorbs at the surface of the particle will largely determine the surface charge density influencing the ZP and the stability [16-21,23,30].
Influence of ionic strength on the colloidal stability and interfacial assembly of hydrophobic ethyl cellulose NPs was investigated by Bizmark et al [66]. The stability of ethyl cellulose (EC) nanocolloids was reported as a function of the ionic strength of the suspension. The predictions of an extended DLVO theory were applied for determination of the critical coagulation concentration for NaCl by using particle size measurements. The barrier-free irreversible adsorption of EC NPs at the air/water interface was found to occur at neutral pH and at any ionic strength in the range of 0 to 0.1 M. The parameters of the adsorption energy, contact angle, and steady-state surface tension which characterize the assembly of EC NPs at the air−water interface have been insensitive to the ionic strength. Badawy et al [67] reported the impact of environmental conditions such as pH, ionic strength, and electrolyte type on the surface charge and aggregation of silver NPs in suspensions. The ionic strength, pH and electrolyte type showed no impact on the aggregation of the sterically stabilized poly(N-vinylpyrrolidone) (PVP)- coated Ag NPs, while the surface charge and aggregation of the branched polyethylenimine-coated AgNPs varied according to the solution pH.
CONCLUSION
In summary, the review highlighted importance and key role of DLS and ZP measurement techniques to characterize particle size and surface charge related to nanomaterials, in particular therapeutic NPs. Regarding characterization of particle size and surface charge by DLS and ZP, there are still challenges for outstanding analysis and interpretation of data due to lack of adequate understanding of the physical principles involved for the measurement and the operating systems and appropriate sample preparation and so on. The current review has been an attempt to address the physical principles governing the techniques of DLS and ZP and is likely to help to better analyze and interpret data of therapeutic nanoparticles for biotechnological applications and nanomedicines.
CONFLICT OF INTEREST
The author would like to declare no conflict of interest.
REFRENCES
[1] Treuel L, Eslahian K A, Docter D, Lang T, Zellner R, Nienhaus K, Maskos M. Physicochemical characterization of nanoparticles and their behavior in the biological environment. Physical Chemistry Chemical Physics. 2014; 16(29), 15053-15067.
[2] Akbarzadeh A, Samiei M, Davaran S. Magnetic nanoparticles: preparation, physical properties, and applications in biomedicine. Nanoscale Research Letters. 2012, 7(1), 144.
[3] Varenne, F, Rustique, E, Botton, J, Coty, J B, Lanusse, G, Lahcen, M A, Negri L. Towards quality assessed characterization of nanomaterial: Transfer of validated protocols for size measurement by dynamic light scattering and evaluation of zeta potential by electrophoretic light scattering. International Journal of Pharmaceutics. 2012, 528(1-2), 299-311.
[4] Bhattacharjee, S, Ershov, D, Fytianos, K, van der Gucht, J, Alink, G M, Rietjens, I M, Zuilhof, H. Cytotoxicity and cellular uptake of tri-block copolymer nanoparticles with different size and surface characteristics. Particle and Fibre Toxicology, 2012. 9(1), 11.
[5] Bhattacharjee, S, de Haan, L H, Evers, N M, Jiang, X, Marcelis, A T, Zuilhof, H, Alink, G M. Role of surface charge and oxidative stress in cytotoxicity of organic monolayer-coated silicon nanoparticles towards macrophage NR8383 cells. Particle and Fibre Toxicology. 2010, 7(1), 25.
[6] Favi, P M, Gao, M, Johana Sepúlveda Arango, L, Ospina, S P, Morales, M, Pavon, J J, Webster, T J. Shape and surface effects on the cytotoxicity of nanoparticles: gold nanospheres versus gold nanostars. Journal of Biomedical Materials Research Part A, 2015. 103(11), 3449-3462.
[7] Jokerst, J V, Lobovkina, T, Zare, R N, Gambhir, S S. Nanoparticle PEGylation for imaging and therapy. Nanomedicine, 2011. 6(4), 715-728.
[8] Kong, B, Seog, J H, Graham, L M, Lee, S B. Experimental considerations on the cytotoxicity of nanoparticles. Nanomedicine, 2011; 6(5), 929-941.
[9] Alshehri, A H, Jakubowska, M, Młożniak, A, Horaczek, M, Rudka, D, Free, C, Carey, J D. Enhanced electrical conductivity of silver nanoparticles for high frequency electronic applications. ACS Applied Materials Interfaces, 2012; 4(12), 7007-7010.
[10] Shi, D, Sadat, M E, Dunn, A W, Mast, D B. Photo-fluorescent and magnetic properties of iron oxide nanoparticles for biomedical applications. Nanoscale, 2015; 7(18), 8209-8232.
[11] Nealon, G L, Donnio, B, Greget, R, Kappler, J P, Terazzi, E, Gallani, J L. Magnetism in gold nanoparticles. Nanoscale, 2012; 4(17), 5244-5258.
[12] Chen, G, Roy, I, Yang, C, Prasad, P N Nanochemistry and nanomedicine for nanoparticle-based diagnostics and therapy. Chemical Reviews, 2016; 116(5), 2826-2885.
[13] Fröhlich, E. The role of surface charge in cellular uptake and cytotoxicity of medical nanoparticles. International Journal of Nanomedicine, 2012; 7, 5577.
[14] Bhattacharjee, S, Rietjens, I M, Singh, M P, Atkins, T M, Purkait, T K, Xu, Z, Alink, G M. Cytotoxicity of surface-functionalized silicon and germanium nanoparticles: the dominant role of surface charges. Nanoscale, 2013; 5(11), 4870-4883.
[15] Axson, J L, Stark, D I, Bondy, A L, Capracotta, S S, Maynard, A D, Philbert, M A, Ault, A P. Rapid kinetics of size and pH-dependent dissolution and aggregation of silver nanoparticles in simulated gastric fluid. The Journal of Physical Chemistry C, 2015; 119(35), 20632-20641.
[16] Voigt, N, Henrich-Noack, P, Kockentiedt, S, Hintz, W, Tomas, J, Sabel, B A. Surfactants, not size or zeta-potential influence blood–brain barrier passage of polymeric nanoparticles. European Journal of Pharmaceutics and Biopharmaceutics, 2014; 87(1), 19-29.
[17] Doostmohammadi, A, Monshi, A, Salehi, R, Fathi, M H, Golniya, Z, Daniels, A U. Bioactive glass nanoparticles with negative zeta potential. Ceramics International, 2011; 37(7), 2311-2316
[18] Griesser, J, Burtscher, S, Köllner, S, Nardin, I, Prüfert, F, Bernkop-Schnürch, A. Zeta potential changing self-emulsifying drug delivery systems containing phosphorylated polysaccharides. European Journal of Pharmaceutics and Biopharmaceutics, 2018; 119, 264-270.
[19] Bonengel, S, Prüfert, F, Jelkmann, M, Bernkop-Schnürch, A. Zeta potential changing phosphorylated nanocomplexes for pDNA delivery. International Journal of Pharmaceutics, 2016; 504(1-2), 117-124.
[20] Wiącek, A E. Investigations of DPPC effect on Al2O3 particles in the presence of (phospho) lipases by the zeta potential and effective diameter measurements. Applied Surface Science, 2011; 257(9), 4495-4504.
[21] Phianmongkhol, A, Varley, J. ζ potential measurement for air bubbles in protein solutions. Journal of Colloid and Interface Science, 2003; 260(2), 332-338.
[22] Uskoković, V, Odsinada, R, Djordjevic, S, Habelitz, S. Dynamic light scattering and zeta potential of colloidal mixtures of amelogenin and hydroxyapatite in calcium and phosphate rich ionic milieus. Archives of Oral Biology, 2011; 56(6), 521-532.
[23] Varenne, F, Botton, J, Merlet, C, Vachon, J J, Geiger, S, Infante, I C, Vauthier, C. Standardization and validation of a protocol of zeta potential measurements by electrophoretic light scattering for nanomaterial characterization. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2015; 486, 218-231
[24] Clogston, J D, Crist, R M, McNeil, S E. Physicochemical characterization of polymer nanoparticles: Challenges and Present Limitations in Polymer Nanoparticles for Nanomedicines. Springer, Cham, 2016; 187-203.
[25] Verma, A, Stellacci, F. Effect of surface properties on nanoparticle–cell interactions. Small, 2010; 6(1), 12-21.
[26] Ziegler, J, Wachtel, H. Comparison of cascade impaction and laser diffraction for particle size distribution measurements. Journal of aerosol medicine, 2005; 18(3), 311-324.
[27] Casals, E, Pfaller, T, Duschl, A, Oostingh, G J, Puntes, V. Time evolution of the nanoparticle protein corona. ACS Nano, 2010; 4(7), 3623-3632.
[28] Liu, H, Pierre-Pierre, N, Huo, Q. Dynamic light scattering for gold nanorod size characterization and study of nanorod–protein interactions. Gold Bulletin, 2012; 45(4), 187-195.
[29] Waghmare, M, Khade, B, Chaudhari, P, Dongre, P. Multiple layer formation of bovine serum albumin on silver nanoparticles revealed by dynamic light scattering and spectroscopic techniques. Journal of Nanoparticle Research, 2018, 20(7), 185.
[30] Bhattacharjee, S. DLS and zeta potential–What they are and what they are not? Journal of Controlled Release, 2016; 235, 337-351.
[31] Rademeyer, P, Carugo, D, Lee, J Y, Stride, EMicrofluidic system for high throughput characterisation of echogenic particles. Lab on a Chip, 2015; 15(2), 417-428.
[32] Fan, X, Zheng, W, Singh, D J. Light scattering and surface plasmons on small spherical particles Light. Science Applications, 2014; 3(6), e179
[33] Fleischer, C, Payne, C K. Nanoparticle–cell interactions: molecular structure of the protein corona and cellular outcomes. Accounts of Chemical Research, 2017; 47(8), 2651-2659.
[34] Milani, S, Baldelli Bombelli, F, Pitek, A S, Dawson, K A, Rädler, J. Reversible versus irreversible binding of transferrin to polystyrene nanoparticles: soft and hard corona. ACS Nano, 2012; 6(3), 2532-2541.
[35] Rahdar, A, Almasi-Kashi, M. Dynamic and spectroscopic studies of nano-micelles comprising dye in water/dioctyl sodium sulfosuccinate/decane droplet microemulsion at constant water content. Journal of Molecular Structure, 2017; 1128, 257-262
[36] Rahdar, A, Almasi-Kashi, M. Photophysics of Rhodamine B in the nanosized water droplets: a concentration dependence study. Journal of Molecular Liquids, 2016; 220, 395-403.
[37] Rahdar, A, Almasi-Kashi, M, Mohamed, N. Light scattering and optic studies of Rhodamine B-comprising cylindrical-like AOT reversed micelles. Journal of Molecular Liquids, 2016; 223, 1264-1269.
[38] Rahdar, A, Almasi-Kashi, M, Khan, A M, Aliahmad, M, Salimi, A, Guettari, M, Kohne, H E G. Effect of ion exchange in NaAOT surfactant on droplet size and location of dye within Rhodamine B (RhB)-containing microemulsion at low dye concentration. Journal of Molecular Liquids, 2018; 252, 506-513.
[39] Rahdar, A, Almasi-Kashi, M, Aliahmad, M. Effect of chain length of oil on location of dye within AOT nanometer-sized droplet microemulsions at constant water content. Journal of Molecular Liquids, 2017; 233, 398-402.
[40] Rahdar, A, Najafi-Ashtiani, H, Sanchooli, E. Fluorescence and dynamics studies of dye-biomolecule interaction in the nano-colloidal systems. Journal of Molecular Structure, 2019; 1175, 821-827.
[41] Rahdar, A, Almasi-Kashi, M. Dynamic light scattering of nano-gels of xanthan gum biopolymer in colloidal dispersion. Journal of Advanced Research, 2016; 7(5), 635-641.
[42] Rahdar, A, Almasi-Kashi, M. Entrapment–D-(+)-Glucose Water Nanodroplet: Synthesis and Dynamic Light Scattering. Journal of Nanostructures, 2018; 8(2), 202-208.
[43] Finnigan, J A, Jacobs, D J. Light scattering from benzene, toluene, carbon disulphide and carbon tetrachloride. Chemical Physics Letters, 6(3), 1970; 141-143.
[44] Noday, D A, Steif, P S, Rabin, Y. Viscosity of cryoprotective agents near glass transition: a new device, technique, and data on DMSO, DP6, and VS55. Experimental Mechanics, 2009; 49(5), 663-672.
[45] Chanamai, R, McClements, D J. Creaming stability of flocculated monodisperse oil-in-water emulsions. Journal of Colloid and Interface Science, 2000; 225(1), 214-218.
[46] JPanchal, J, Kotarek, J, Marszal, E, Topp, E M. Analyzing subvisible particles in protein drug products: a comparison of dynamic light scattering (DLS) and resonant mass measurement (RMM). The AAPS journal, 2014; 16(3), 440-451.
[47] Zheng, T, Cherubin, P, Cilenti, L, Teter, K, Huo, Q. A simple and fast method to study the hydrodynamic size difference of protein disulfide isomerase in oxidized and reduced form using gold nanoparticles and dynamic light scattering. Analyst, 2016; 141(3), 934-938.
[48] Meng, Z, Hashmi, S M, Elimelech, M. Aggregation rate and fractal dimension of fullerene nanoparticles via simultaneous multiangle static and dynamic light scattering measurement. Journal of Colloid and Interface Science, 2013; 392, 27-33.
[49] Liu, H H, Surawanvijit, S, Rallo, R, Orkoulas, G, Cohen, Y. Analysis of nanoparticle agglomeration in aqueous suspensions via constant-number Monte Carlo simulation. Environmental Science Technology, 2011; 45(21), 9284-9292.
[50] Zhou, C, Qi, W, Lewis, E N, Carpenter, J F. Concomitant Raman spectroscopy and dynamic light scattering for characterization of therapeutic proteins at high concentrations. Analytical Biochemistry, 2015; 472, 7-20.
[51] Niu, W, Chua, Y A, Zhang, W, Huang, H, Lu, X. Highly symmetric gold nanostars: crystallographic control and surface-enhanced Raman scattering property. Journal of the American Chemical Society, 2015; 137(33), 10460-10463.
[52] Grouchko, M, Roitman, P, Zhu, X, Popov, I, Kamyshny, A, Su, H, Magdassi, S. Merging of metal nanoparticles driven by selective wettability of silver nanostructures. Nature Communications, 2014; 5, 2994.
[53] Zhao, X, Zhu, S, Song, Y, Zhang, J, Yang, B. Thermal responsive fluorescent nanocomposites based on carbon dots. RSC Advances, 2015; 5(20), 15187-15193.
[54] Kim, K H, Xing, H, Zuo, J M, Zhang, P, Wang, H. TEM based high resolution and low-dose scanning electron nanodiffraction technique for nanostructure imaging and analysis. Micron, 2015; 71, 39-45.
[55] Mansfield, E D, Sillence, K, Hole, P, Williams, A C, Khutoryanskiy, V. POZylation: a new approach to enhance nanoparticle diffusion through mucosal barriers. Nanoscale, 2015; 7(32), 13671-13679.
[56] Fielding, L A, Mykhaylyk, O O, Armes, S P, Fowler, P W, Mittal, V, Fitzpatrick, S. Correcting for a density distribution: particle size analysis of core–shell nanocomposite particles using disk centrifuge photosedimentometry. Langmuir, 2012; 28(5), 2536-2544.
[57] Krpetić, Z, Davidson, A M, Volk, M, Lévy, R, Brust, M, Cooper, D L. High-resolution sizing of monolayer-protected gold clusters by differential centrifugal sedimentation. ACS Nano, 2013; 7(10), 8881-8890.
[58] Ruiz-Cabello, F J M, Trefalt, G, Maroni, P, Borkovec, M. Electric double-layer potentials and surface regulation properties measured by colloidal-probe atomic force microscopy. Physical Review E, 2014; 90(1), 012301.
[59] Missana, T, Adell, A, On the applicability of DLVO theory to the prediction of clay
colloids stability, Journal of Colloid Interface Science 2000; 230 , 150–156.
[60] Leite, F L, Bueno, C C, Da Róz, A L, Ziemath, E C, Oliveira, O N, Theoretical models for
surface forces and adhesion and theirmeasurement ssing atomic forcemicroscopy,
International Journal of Molecular Science 2012; 13, 12773–12856.
[61] Rudyak VY, Kharlamov GV, Belkin AA. The velocity autocorrelation function of nanoparticles in a hard-sphere molecular system. Technical Physics Letters. 2000 ; 26, 553-6.
[62] Rengel R, Martín MJ. Diffusion coefficient, correlation function, and power spectral density of velocity fluctuations in monolayer graphene. Journal of Applied Physics. 2013;114,143702.
[63] Xu R. Progress in nanoparticles characterization: Sizing and zeta potential measurement. Particuology. 2008;6(2):112-5.
[64] Ziębacz N, Wieczorek SA, Kalwarczyk T, Fiałkowski M, Hołyst R. Crossover regime for the diffusion of nanoparticles in polyethylene glycol solutions: influence of the depletion layer. Soft Matter. 2011;7(16):7181-6.
[65] Lim J, Yeap SP, Che HX, Low SC. Characterization of magnetic nanoparticle by dynamic light scattering. Nanoscale Research Letters. 2013;8(1):381.
[66] Bizmark N, Ioannidis MA. Effects of ionic strength on the colloidal stability and interfacial assembly of hydrophobic ethyl cellulose nanoparticles. Langmuir. 2015;31(34):9282-9.
[67] Badawy AM, Luxton TP, Silva RG, Scheckel KG, Suidan MT, Tolaymat TM. Impact of environmental conditions (pH, ionic strength, and electrolyte type) on the surface charge and aggregation of silver nanoparticles suspensions. Environmental Science & Technology. 2010;44(4):1260-6.
[68] Nobbmann U, Morfesis A. Light scattering and nanoparticles. Materials Today. 2009;12(5):52-4.
[69] Khlebtsov BN, Khlebtsov NG. On the measurement of gold nanoparticle sizes by the dynamic light scattering method. Colloid Journal. 2011;73(1):118-27.
