Dynamic light scattering: A useful technique to characterize nanoparticles

Journal of Nanoanalysis

داوران ، دوره 6، شماره 2، شهریور 2019، صفحه 80-89   

شناسه دیجیتال (DOI): 10.22034/jna.2019.667079

نویسندگان

Abbas Rahdar^{* 1}؛ Nooshin Amini²؛ Faezeh Askari¹؛ Md. Abu Bin Hasan Susan³

¹Department of Physics, University of Zabol, Zabol, Iran

²Faculty of Pharmaceutical Chemistry, Pharmaceutical Sciences Branch, Islamic Azad University (IAUPS), Tehran, Iran

³Department of Chemistry, University of Dhaka, Dhaka 1000, Bangladesh

چکیده

Biologically, two parameters of size and surface charge of the nanoparticles, especially therapeutic nanoparticles influence their kinetics in vivo as well as their interaction with the cellular and biological membranes and resulting their efficacy. So effective characterization of nanomaterials including nanometer-sized particles and micelles is a key issue to develop the well-deserved and well-defined Nano-formulations focus on the therapeutic goals in nanomedicine research. Determining the particle size and surface charge of nanoparticles are essential to characterize therapeutic nanoparticles properly. Measurements related to techniques of dynamic light scattering (DLS) and zeta potential (ZP) are known as easy, simple, and reproducible tools to obtain the size and surface charge of nanoparticles. Regarding characterization of particle size and surface charge by the DLS and ZP there is challenges for researchers to interpret and analyze the exported data effectively due to lack of adequate understanding focus on physical principles governing on the operating system of these techniques and how preparing samples for characterization and so on. With this in mind, this review tries to address this issue focus on the fundamental principles governing on techniques of DLS and ZP to better analyzing and interpreting the reported results such as hydrodynamic size, diffusion, inter particular interactions as well as study of the colloidal system stability based on surface charge of nanoparticles.

کلیدواژه‌ها

Diffusion؛ Dynamic Light Scattering؛ Hydrodynamic Size؛ Nanomedicine؛ Surface Charge؛ Zeta Potential

سایر فایل های مرتبط با مقاله

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داوران

INTRODUCTION

Increasing application of nanomaterials including nanoparticles and Nano micelles in biotechnology and medicine requires tools with special characteristics such as accessibility, fast, and effective resolution to manipulate nanomaterials into biological environments as well as to characterize physicochemical properties of nanometer-sized particles within Nano-colloidal systems [1-2]. Literature review highlight that quality control analysis of nanometer-sized materials including is a key issue to follow their application and development in different industrial fields such as nanomedicine. With this in mind, in this case the lack of standard tools for effective characterization of nanomaterials is a key challenge [3]. 

So to achieve reliable data with high translator and interpretational output, it is required for sufficient characterization of the nanomaterials especially within colloidal systems.

On the other hand, safety is a key challenge in field of application and characterization of nanomaterials into biological media. It is widely believed that the safety concerns is related to physiochemical properties of nanoparticles including particle size, surface charge , particle shape, ligand-based surface functionalization, impurity [4-8] and so on. Existing of the useful tools and techniques to characterize effective the nanomaterials reveal the outstanding differences in the physicochemical properties related to the nanomaterials such as conductivity, fluorescence, magnetism [9-12] than those of bulk materials.

Literature review indicate a range of biological effects of nanoparticles and Nano micelles within living systems such as cellular uptake, toxicity, dissolution [13-15] are affected from two key parameters of particle size and surface charge related to nanosturctures. The conducted studies has highlighted the importance and influence of these two parameters in scientific different fields specially biomedical sciences such as in release profile from the designed nanomaterials to deliver drugs across the blood–brain barrier (BBB) [16], potential candidature of bioactive glass nanoparticles 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], a promising strategy for future gene delivery systems [19], Responsible for phospholipid- Al₂O₃ particle interactions [20], ζ potential measurement for air bubbles in protein solution [21], electrostatic interactions governing on the kinetics of the adsorption of rH174 (the full-length recombinant human amelgenin) onto HAP (hydroxyapatite) [22], zeta potential measurement of nanomaterials to study their colloidal stability [23].

Dynamic light scattering (DLS) that known as photon correlation spectroscopy (PCS) or quasi-elastic light scattering (Fig.1) and zeta potential (Fig.2) are well-known as tools and techniques to study the hydrodynamic size and surface charge of nanomaterials within colloidal systems with different potential applications especially pharmaceutical application [21-22].

The size of NPs in colloidal systems is obtained based on measurement of scattering intensity of nanoparticles in Brownian motion when illuminate by a monochromatic beam of light [24]. For charging NPs in colloidal system factors such as the interactions between particles, molecules and ions lead to the creation of 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].

As mentioned earlier, the conducted studies indicate the kinetics in vivo as well as interaction with the cellular and biological membranes related to therapeutic nanoparticles are affected from their size and surface charge [24-25].

The conducted studies also reveal that factors such as frequent use with lack of caution and proper training has been caused that the quality of the reported data related to size and charge of NPs by DLS and ZP is not be always excellent in nanomedicine research.

So, it is required to investigate about these two parameters 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 mechanism (e.g., protein adsorption causing the characteristic corona) [19,20].

With this in mind, in this review, an effort is made to offer a simple account on these two techniques and discussion on who and why different factors influence the measurements and quality of data related to DLS and ZP.

DYNAMIC LIGHT SCATTERING (DLS)

Scattering of light by nanoparticles and nanoparticles dispersed in a colloidal system

Nanoparticles 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 to the wavelength of the incident light) is elastic that known as Rayleigh scattering (Fig.3) [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 most intense towards the direction of the incident light [31]. A hydrated shell wrapped within a cloak of molecules (which are not the ingredients of the nanoparticles itself) are formed on surface of dispersed nanoparticles in colloidal system (corona) [30, 33-34]. The corona is formed by two shells, including hard and soft [30, 33-34]. The hard corona refers to the inner stable layer tightly bound to the nanoparticle 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]. It is widely believed that different compositions related to hard and soft corona in addition to the structure of nanoparticle itself have a key role in scattering light from particle surface. In this case, in characterization of nanoparticles by dynamic light scattering technique, particles are different in surface chemistry and composition than those originality synthesized [30, 33-34].

In a conducted study by Casals et al [27], dynamic light scattering (DLS) was used to confirm the formation of the protein corona after exposure to metallic Au nanoparticles (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 nanoparticles and their surface state at every stage of the experiment.

In an another study by Liu et al [28], they reported the use of dynamic light scattering (DLS) for characterization of gold nanorods and to investigate 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. They reported that protein adsorption to gold nanorods is strongly dependent on the aspect ratio of the nanorods, and varies significantly from protein to protein. This study demonstrated that dynamic light scattering is a valuable tool for nanorod characterization and understanding the gold nanorod–protein interactions.

In a recent study by Waghmare et al [29], they employed DLS to monitor adsorption of BSA protein onto silver nanoparticles. They reported increase in adsorption with enhancing in average hydrodynamic radius of BSA-Ag NP corona from 24 to 35 nm. The reported results related to conducted study can be effective in drug design development for tumor-targeted therapy.

With this in mind, understanding the dynamics of the growth of protein corona onto NP-surfaces by DLS is important from the perspective of how the nanoparticles behave in vivo [49]. Hence, DLS technique can 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 (ACF), Diffusion (D), Inter-particular interactions, hydrodynamic radius (R_h), the rotational correlation time (τ_r) of nanoparticles

DLS is an effective tool to characterize dynamic parameters of nanoparticles 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 nanoparticles 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].

 (1)

Where, E* is the complex conjugated of E. Experimentally, the intensity autocorrelation function, g²(q, τ), is determined as following [35-42]:

 (2)

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].

g² (q, τ) = 1+ |A exp (-Γt )|² (3)

Here, A is an instrumental constant. In a colloidal system containing monodisperse micelles, the function of g1(q, τ) is represented by a single exponential decay curve [35-42].

g¹ (q, τ) = A exp (-Γt ) (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 was compared to the intensity signal a very short time later and resulting the two signals were very similar, they strongly correlate each other. On the other hand, correlation between two signals decreases with time due to decrease in similarity of two signals as time increases affected by Brownian motion of nanoparticles (Fig.3).

The decay rate, Γ, is converted to diffusion coefficient using [35-42]:

D=Γ/q² (5)

Where q is the scattering vector [59-63]. Finally, the diffusion coefficient of nanomicelles can be characterized as the hydrodynamic size (R_h) according to the stokes-Einstein relation [35-42]:

R_h=  (6)

Where K is Boltzmann’s constant, T is the temperature in K, and η is the viscosity of 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 nanoparticl [35-42].

It is widely believed that the hydrodynamic radius is the radius of a the hypothetical hard sphere that diffuses with the same speed as the particles is analysed under dynamic light scattering [35-42]. It is well-known that R_H is a mathematical measurement. Because hard spheres rarely exist in a colloidal system. In other word, the dispersed particles within a colloidal system are hydrated/solvated (formation of corona) that form of corona is often not spherical.

The rotational correlation time (τ_r) of spherical droplets is calculated according to model of the Stokes-Einstein-Debye (SED) hydrodynamic [35-42].

 (7)

Hear, r_h is hydrodynamic diameter of water nanodroplets ,k is Boltzmann’s constant, T is the temperature in K, and η is the viscosity of solvent.

In a conducted study by Rahdar et al [41] focus on dynamics of water nanodroplets containing Xanthen Gum (XG) polysaccharide by using dynamic light scattering (DLS) technique, they synthesized the water nano-droplets containing XG with hydrodynamic diameter in the range of 5-35 nm at the different XG concentrations by water-in-oil AOT microemulsion system as a function of mass fraction of droplet (MFD) at a the constant water content (W=[H2O]/[AOT]=Const.).

It is important to mention that the nanometer-sized water droplets within water-in-oil surfactant microemulsion are formed based on specific ratios of the water, surfactant, and oil. It is widely believed that the structure, size, and property of water nano-droplets in the microemulsion are affected by two parameters i) the water-to-surfactant molar ratio, popularly is showed 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 mass fraction of nano-droplet (MFD) value (or volume fraction of nano-droplet) , MFD=Mnano-droplet/Mtotal [35-42].

In that work, to study the dynamical parameters of the diffusion and size distribution characterization of the nano-sized water droplets containing polysaccharide of the Xanthen Gum, the autocorrelation function of water nanodroplets was obtained by dynamic light scattering (DLS) technique. They reported plot of the autocorrelation function versus decay time for nanodroplet for the different XG concentrations at the values different of mass fraction of water nanodroplet (MFD) of 0.01, 0.04, and 0.1.

Typically, the autocorrelation function versus decay time for nanodroplet containing XG at concentration of 0.0000625 is shown in Fig.3.

To obtain decay rate of nanodroplets (Fig.4), the autocorrelation function of nanodroplets was fitted with a single exponential function according the relation (4) [35-42].

To understand the inter-particular interaction type within colloidal systems, it is necessary to obtain the collective diffusion of nanoparticles in systems.

Then the collective diffusion of water nanodroplets was calculated according the relation (5) [35-42]. The collective diffusion coefficient of water nanodroplets versus mass fraction of nano-droplet (MFD) at the different concentrations of XG polysaccharide are shown in Fig.5.

As it can be seen from Fig.5, the collective diffusion as a function of droplet mass fraction have negative slope for water nanodroplet sample containing XG at 0.0000157concentration and positive slope for sample containing XG at concentration of 0.0000625. On the other hand, the inter-nanodroplets interaction changed from attractive to repulsive as concentration of Xanthen Gum biopolymer increased as a function of MFD due to adsorbing XG polysaccharide at interfacial of AOT nanodroplets and resulting 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 curve slope of Diffusion with MFD is interpreted as change in interdroplet interaction type within colloidal system.

The diffusion is converted to the hydrodynamic radius of water nanodroplets by using the Stokes-Einstein according to relation (6). Then the hydrodynamic diameter versus mass fraction of nano-droplet (MFD) at the different concentrations of XG are shown in Fig.6.

It is clear from data in Fig.6 that with increasing the polysaccharide concentration of XG within the water nanodroplets, hydrodynamic diameter of water nano-droplets decreases generally.

In another study by Rahdar et al [42], focus on dynamics of water nanodroplets containing D-(+)-Glucose by using dynamic light scattering (DLS) technique, they calculated the collective diffusion coefficient of nano droplets for different D-(+)-Glucose concentrations. In the conducted study, for the different D-(+)-Glucose concentrations within the water-in-oil micro emulsion, a single relaxation curve was observed for the water droplets. Study of dynamic light scattering of water nano droplets indicated that the diffusion coefficient of water nano- droplets increased and their size decreased as concentration of D-(+)-Glucose within water nanodroplets increased. Results showed that the interaction between droplets changed from attractive to repulsive as concentration of D-(+)- Glucose within droplets increased [42]. So, DLS is an effective tool to discuss inter-particular interaction within colloidal systems.

Size by intensity, volume, and number and z-average obtained by dynamic light scattering

The DLS tool produces the different data including the correlation data, size by intensity, volume, and number, z-average, and PDI and so on.

About correlation discussed earlier. 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. Because scattered light intensity by a particle is proportional to the 6th power of its radius. So, a few particles of larger size scatter more light than many smaller particles in size.

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 polydispersity index (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.

Effect of parameters of sample preparation, sample concentration, aggregation, shape of nanoparticles on 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 example toluene can scatter light while some like DMSO exhibits significant changes in viscosity with variations 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, using DI (deionized) water is usually not suggested because it fails to cover the particles from long distance interactions. Therefore, the size of them in DI water is always larger than their actual size. As a result, using dilute saline water gives better results as the ions shield the particles from long-distance interactions. Another item that can supply the DLS useless are large particles of low density that may float on top of the solvent layer. For powder formulations stirring quickly can dissolve the NPs to gain a stable and homogeneous dispersion [30, 43-45].

Increasing concentration of NPs result in multi-scattering in which the scattered light from one particle interacts with others before arriving to the detector. As a result, the obtained size is falsely smaller. Agglomeration occurs in high concentrations except surfactants are utilized [30, 46-48]. On the opposite, utilizing diluted samples may not produce sufficient scattered light for the investigation. In this manner, finding the 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 that’s scattered from tiny particles. Subsequently, broadened peaks rise whereas the exactness of the information is diminished. Therefore, DLS has been trusty just at dilute concentrations. To encounter these consequences, diverse surfactants are usually used to create lasting dispersions [49].

Due to the fact that NPs have different types of forms of shape besides spherical inclusive of nanotubes and nanostars; DLS presents a R_H which by determining its radius of a hypothetical firm sphere moving at the identical pace to that of the aspherical NPs in the dispersion[50-51]. Scientists also have modified the Stokes-Einstein equation to fit the data obtained from cylindrical structures [50-51].

Particle size based on TEM (transmission electron microscopy), AFM (atomic force microscopy), sedimentation (X-ray disc centrifuge and DCS/differential centrifugal sedimentation), and DLS

It is conceivable to determine the size distribution of NPs from TEM photographs with datasets on their mean size [89, 90]. However, such data from TEM images usually do not confirm well with the information received from DLS. This can be because 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 anticipated the surface area (Fig.7). Subsequently, the measure gotten by DLS is normally 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 a more vigorous information on size distribution [52-54].

AFM has developed as a powerful device to picture NPs. AFM offers exact data on particle size and shape; moreover, it’s capable to diagnose particles with diverse sizes in a blend. But, the number of particles analyzed by AFM is much smaller, consequently, DLS offers an improved size distribution. On the other hand, characterization of nanoparticles by AFM provides a rapid and accurate and analysis for nanoparticle characterization by AFM has advantages over DLS for non-monodispersed solutions. [55].

Sedimentation methods have achieved demand to determine the size of NP [30,56-57]. In summary, these techniques utilize high centrifugal energy to deposit NPs infractions based on density. The sizing of the NPs is done 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 PRINCIPLES OF ZETA POTENTIAL

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 Nano colloidal system. In other word, the zeta potential is commonly used to predict and control dispersion stability. The zeta potential is a scientific term for electrokinetic potential in colloidal dispersions [16-21, 23 and 26].

In process related to study stability particles in colloidal dispersion by the zeta potential, 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. In the other word, the zeta potential reflects the potential difference between EDL (Electric Double Layer) of electrophoretically mobile particles and the layer of dispersant around them at slipping plane [16-21, 23 and 26].

It is important to mention that when a charged particle is dispersed in a liquid, an adsorbed double layer that is known as EDL [30, 58] creates on particle surface. The inner layer includes the molecules/ions with opposite charge to that of the particle that is called Stern layer. Beyond Stern layer the electrostatic effects decrease due to the surface charge on the particles according to Debye’s law [30, 58].

Electrophoretic mobility, Henry’s equation, Helmholtz – Smoluchowski (HS) equation, Hückel equation in zeta potential measurement

A group of phenomena, generally referred to as Electrokinetic Effects, can be used as the basis for determination of Zeta Potential. Four related phenomena, mainly consists of Electrophoresis, Electro-Osmosis, Streaming Potential and Sedimentation Potential [21, 23 and 30].

Totally it can said that Electrophoresis is the movement of charged particles which are suspended in a liquid under the influence of an applied electric field. Zeta potential has a direct relation with electrophoretic mobility [21, 26, and 30].

Totally Electrophoresis is the movement of charged particles 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 proportional to the electrophoretic mobility [21, 26, and 30].

Zeta potential can be measured by means of electrophoresis, movement of charge surface stationary liquid applied electric field. The electrophoretic mobility (m_e) is calculated according to m_e= V/E, where V is particle velocity (mm/s) and E is electric field strength (Volt/cm).

The zeta potential is then calculated from the obtained m_e by Henry’s equation [21, 23 and 30]:

m_e= 2e_re₀zf (ka)/3h (8)

Here, e_r is Dielectric constant, e₀ is permittivity of vacuum, z is Zeta Potential, f (ka) is Henry’s function, and h is viscosity.

When value of f (ka) is considered 1.5, it means that the EDL is in comparison 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 [21, 23 and 30]:

m_e= e_re₀z/h (8)

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 [21, 23, and 30]:

m_e= 2e_re₀z/3h (9)

The Hückel equation is usually used in ceramic industry and it is not a useful equation for pharmaceutical preparation [21, 23 and 30].

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 Zeta Potential. 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 Zeta Potential [21, 23 and 30].

Electroacoustic effects are result of coupling between acoustic and electric fields. Whit this technique the particles in a sample oscillator under the electric field and the oscillation are analyzed on magnitude and phase angle to measure the particle size and Zeta Potential. This technique is less popular in drug delivery research.

Effect of parameters of pH and ionic strength, sample concentration, colloid stability, and surface charge of NPs on characterization nanoparticles by zeta-potential

The most essential factor that affects zeta potential, especially in aqueous dispersions is pH. The change in magnitude with acidic and basic pH causes the change in zeta potential. The isoelectric point (I.E.P.), is the pH at which the zeta potential is zero is called. At this pH the repulsive forces are zero, and aggregation occurs [21, 23 and 30]. On the other hand, as ionic strength of colloidal system increases the EDL compresses while the zeta potential decreases in magnitude and vice versa [21, 23 and 30].

The particle concentration can have a significant impact on zeta potential. The effect depends on relative valence of ions and on their concentration. Totally, It can be said that increasing concentration may decrease zeta potential with the lesser stability of the dispersion [21, 23 and 30].

The most popular application of zeta potential data is related to colloid stability. According to literature related to drug delivery issues, values of zeta potential are classified to ±0– 10mV (highly unstable), ±10–20mV (relatively stable), ±20–30 mV (moderately stable), and ˃±30mV (highly stable) [30,59-60].

Literature review reveals that although values of zeta potential are indications to study colloid stability, but they are not enough. [30, 59-60]. Because the zeta potential provides insight on the electrostatic repulsive forces, but it doesn’t provide information focus on the attractive Van der Waals forces. There is theories focus on understanding attractive forces existing in nature witch discussions related to those subjects are beyond the scope of current review.

It is significant to mention that 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 forces also become weak and then mild electrostatic repulsion reflected by low ZP (10–15 mV) may be enough to confirm 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 despite having low zeta potential [30, 59-60].

By changing the surface charge of nanoparticles, the zeta potential changes, while we know that zeta potential is not a direct measure of surface charge. The zeta potential is determined by surface chemistry. Anything that changes to surface charge will cause some change in the zeta potential (e.g., pH which is relevant for nanoformulations). Even a small percent of a component which absorbed at the surface of the particle, will largely determine the surface charge density and resulting resulting zeta potential and the stability [16-21,23,30].

CONCLUSION

In summary, the conducted studies focus on dynamic light scattering (DLS) and zeta potential (ZP) have highlighted the importance and key role of these techniques to characterize particle size and surface charge related to therapeutic nanoparticles in nanomedicine applications. Regarding characterization of particle size and surface charge by DLS and ZP, there are challenges for researchers to analysis and interpretation of exported data excellently due to lack of adequate focus on understanding of the physical principles behind on the operating system of these tools and sample preparation and so on.

With this in mind, in the current review an attempt has been made to address the physical principles governing on techniques of DLS and ZP to help to better analyzing and interpreting the reported data.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interests regarding the publication of this manuscript.

 

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