Particle Size Distribution Information Research Articles

Particle size distribution measurement based on the angular scattering efficiency factor spectra inversion-simulation and experiment.

The quantification of the particle size distribution (PSD) within a particle system is significant to various domains, including atmospheric and environmental sciences, material science, civil engineering, and human health. The scattering spectrum reflects the PSD information of the particle system. Researchers have developed high-precision and high-resolution PSD measurements for monodisperse particle systems through scattering spectroscopy. However, for polydisperse particle systems, current methods based on light scattering spectrum and Fourier transform analysis can only obtain the information of the particle component, but cannot provide the relative content information of each component. In this paper, a PSD inversion method based on the angular scattering efficiency factors (ASEF) spectrum is proposed. By establishing a light energy coefficient distribution matrix, and then measuring the scattering spectrum of the particle system, PSD can be measured in conjunction with inversion algorithms. The simulations and experiments conducted in this paper substantiate the validity of the proposed method. Unlike the forward diffraction approach that measures the spatial distribution of scattered light I(θ) for inversion, our method uses the multi-wavelength distribution information of scattered light β(λ). Moreover, the influences of noise, scattering angle, wavelength, particle size range, and size discretization interval on PSD inversion are studied. The method of condition number analysis is proposed to identify the appropriate scattering angle, particle size measurement range, and size discretization interval, and it can reduce the root mean square error(RMSE) of PSD inversion. Furthermore, the method of wavelength sensitivity analysis is proposed to select the spectral band with higher sensitivity to particle size changes, thereby improving the computational speed and avoiding the problem of diminished accuracy caused by the reduction of the number of wavelengths used.

Particle size distribution recovery from non-Gaussian intensity autocorrelation functions obtained from dynamic light scattering at ultra-low particle concentrations

Dynamic light scattering (DLS) from ultra-low concentration suspensions (the average number of particles in the scattering volume is less than ~100) gives rise to autocorrelation functions (ACFs) containing a non-Gaussian term due to particle number fluctuations. This term is difficult to characterize and account for and makes recovery of particle size distribution (PSD) information unreliable. We show that an initial analysis of the intensity ACF to determine parameters describing the amplitude and relaxation rate of the non-Gaussian term and then using these parameters to create a better theoretical non-Gaussian ACF model allows a more accurate recovery of the PSD. The modified model is consistent with the measured ACF data, and a reconstructed kernel matrix matching the measured data is obtained. When compared with the usual kernel function reconstruction (KFR) method, the proposed method gives significantly improved PSD recovery accuracy with experimental data. Furthermore, the PSDs obtained have no obvious differences to those obtained from measurements at normal particle concentrations. • Fitting the measured ACF data with a non-Gaussian ACF model of lower concentration. • Reducing focused beam waist value to obtain non-Gaussian model at lower concentration. • Providing a solution for nanoparticle size measurement at ultra-low concentration.

Asymmetric-flow field-flow fractionation for measuring particle size, drug loading and (in)stability of nanopharmaceuticals. The joint view of European Union Nanomedicine Characterization Laboratory and National Cancer Institute - Nanotechnology Characterization Laboratory

Asymmetric-flow field-flow fractionation (AF4) has been recognized as an invaluable tool for the characterisation of particle size, polydispersity, drug loading and stability of nanopharmaceuticals. However, the application of robust and high quality standard operating procedures (SOPs) is critical for accurate measurements, especially as these complex drug nanoformulations are most often inherently polydisperse. In this review we describe a unique international collaboration that lead to the development of a robust SOP for the measurement of physical-chemical properties of nanopharmaceuticals by multi-detector AF4 (MD-AF4) involving two state of the art infrastructures in the field of nanomedicine, the European Union Nanomedicine Characterization Laboratory (EUNCL) and the National Cancer Institute-Nanotechnology Characterisation Laboratory (NCI-NCL). We present examples of how MD-AF4 has been used for the analysis of key quality attributes, such as particle size, shape, drug loading and stability of complex nanomedicine formulations. The results highlight that MD-AF4 is a very versatile analytical technique to obtain critical information on a material particle size distribution, polydispersity and qualitative information on drug loading. The ability to conduct analysis in complex physiological matrices is an additional very important advantage of MD-AF4 over many other analytical techniques used in the field for stability studies. Overall, the joint NCI-NCL/EUNCL experience demonstrates the ability to implement a powerful and highly complex analytical technique such as MD-AF4 to the demanding quality standards set by the regulatory authorities for the pre-clinical safety characterization of nanomedicines.

Characteristics of Snow Particle Size Distribution in the PyeongChang Region of South Korea

Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel2 disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow (ρ), snowfall rate (S), and ice water content (IWC) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors (Dm, log10Nw, and μ) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The Dm decreases and log10Nw and μ increase as the density increases. The Dm and log10Nw increase and μ decreases with the increase of snowfall rate. The power-law relationship between ρ and Dm is obtained and the relationship between S and IWC is also derived.

Using pore size distribution and porosity to estimate particle size distribution by nuclear magnetic resonance

Soil particle size distribution (PSD) is critical for assessing the potential for internal instability of sand–gravel soils, mainly because particle migration in sand–gravel soils owing to seepage forces affects almost all soil hydraulic properties during internal erosion processes. Therefore, a reliable PSD analysis is essential for revealing the mechanism of particle migration and internal instability of sand–gravel soils. At present, however, PSD is commonly calculated by manual interpretation methods and cannot be continuously and nondestructively analyzed during internal erosion processes. Those methods are lacking, however, in that it results in a failure to continuously determine the internal erosion condition. In this paper, a new approach with the ability to continuously predict the PSD changes in sand–gravel mixtures is introduced based on one-dimensional laboratory suffusion tests in a non-destructive nuclear magnetic resonance (NMR) system over a range of suffusion times. The results show that pore expansion and particle migration are an interactive process that controls suffusion development in sand–gravel mixtures. The proposed method is verified through experimental data measured by sieve analysis, and has potential to retrieve PSD information at different suffusion times.

Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China

Water uptake by aerosol particles alters its light-scattering characteristics significantly. However, the hygroscopicities of different aerosol particles are not the same due to their different chemical and physical properties. Such differences are explored by making use of extensive measurements concerning aerosol optical and microphysical properties made during a field experiment from December 2018 to March 2019 in Beijing. The aerosol hygroscopic growth was captured by the aerosol optical characteristics obtained from micropulse lidar, aerosol chemical composition, and aerosol particle size distribution information from ground monitoring, together with conventional meteorological measurements. Aerosol hygroscopicity behaves rather distinctly for mineral dust coarse-mode aerosol (Case I) and non-dust fine-mode aerosol (Case II) in terms of the hygroscopic enhancement factor, f β ( R H , λ 532 ) , calculated for the same humidity range. The two types of aerosols were identified by applying the polarization lidar photometer networking method (POLIPHON). The hygroscopicity for non-dust aerosol was much higher than that for dust conditions with the f β ( R H , λ 532 ) being around 1.4 and 3.1, respectively, at the relative humidity of 86% for the two cases identified in this study. To study the effect of dust particles on the hygroscopicity of the overall atmospheric aerosol, the two types of aerosols were identified and separated by applying the polarization lidar photometer networking method in Case I. The hygroscopic enhancement factor of separated non-dust fine-mode particles in Case I had been significantly strengthened, getting closer to that of the total aerosol in Case II. These results were verified by the hygroscopicity parameter, κ (Case I non-dust particles: 0.357 ± 0.024; Case II total: 0.344 ± 0.026), based on the chemical components obtained by an aerosol chemical speciation instrument, both of which showed strong hygroscopicity. It was found that non-dust fine-mode aerosol contributes more during hygroscopic growth and that non-hygroscopic mineral dust aerosol may reduce the total hygroscopicity per unit volume in Beijing.

Splintex 2.0: A physically-based model to estimate water retention and hydraulic conductivity parameters from soil physical data

Soil water retention curve (SWRC) and hydraulic conductivity curve (SHCC) are functions that contribute to the understanding and modeling of hydraulic processes in the vadose zone of the soil. However, their measurement is often difficult and expensive becoming impractical the large-scale monitoring. Pedotransfer functions (PTFs) are, therefore, an alternative to estimate SWRC and SHCC data. Most PTFs are usually calibrated with data from local soils and may be uncertain when applied to soils with different morphological properties. On the other hand, PTFs with a physico-empirical basis have as advantage their wide application. Splintex 1.0 is a physico-empirical model developed in BASIC language that is based on the particle size distribution and other basic soil information. This model estimates the parameters of the van Genuchten-Mualem equation that compose the SWRC without requiring prior calibration and the saturated hydraulic conductivity (Ks) using a texture-PTF. A second version with a user-friendly computational interface is presented to improve the estimates of the SWRC parameters and to introduce the estimation of the SHCC parameters with different PTFs, which are based on two physically-based models that can be applied universally. Computational procedures and equations of Splintex 2.0 were written in C++ language and the performance of both model versions was tested for different soil texture classes. The performance analysis was carried out using the Pearson correlation coefficient and the mean absolute and root mean square errors. Splintex 2.0 yielded good performance in the quantification of water retention data, showing its application to any soil class. As an advantage, the conductivity data can be estimated without the need of Ks and SWRC parameters.

Multi-angle dynamic light scattering analysis based on successive updating of the angular weighting

The angular weighting coefficient is key to accurate particle size distribution (PSD) measurement using multiangle dynamic light scattering (MDLS). However, determining the weighting coefficient is affected by the noise in the measured MDLS data. In this paper, a novel successive updating of the angular weighting (AWSU) method is proposed. By using information character weighting, the angular weighting coefficient and the character weighting matrix are updated once with each additional angle. The effect of autocorrelation function (ACF) denoising and information extraction is improved by using information character weighting step-by-step. The results for broad unimodal and closely-spaced bimodal PSDs demonstrate the effectiveness of this method. Compared with another routinely used inversion method, it is found that the increase in PSD information retrieved from MDLS depends not only on increasing the number of angles, but also on the calculation of the angular weightings. An accurate weighting algorithm can reduce the number of scattering angles required. Using the AWSU method for PSD recovery with 4 scattering angles can give better results than the usual method with 6 scattering angles. It is also found that the AWSU method not only improves the accuracy of the weightings, but it also contributes directly to the PSD recovery by reconstructing the ACF with the PSD obtained by the weighted inversion. The combination of these two contributions not only reduces the number of scattering angles needed, but also keeps the PSD recovery results from getting worse because of excessive scattering angles.

Stratospheric aerosol characteristics from space-borne observations: extinction coefficient and Ångström exponent

Abstract. Stratospheric aerosols are of a great importance to the scientific community, predominantly because of their role in climate, but also because accurate knowledge of aerosol characteristics is relevant for trace gas retrievals from remote-sensing instruments. There are several data sets published which provide aerosol extinction coefficients in the stratosphere. However, for the instruments measuring in the limb-viewing geometry, the use of this parameter is associated with uncertainties resulting from the need to assume an aerosol particle size distribution (PSD) within the retrieval process. These uncertainties can be mitigated if PSD information is retrieved. While occultation instruments provide more accurate information on the aerosol extinction coefficient, in this study, it was shown that limb instruments are more sensitive to the smaller particles in the visible–near-infrared spectral range. However, the sensitivity of occultation instruments improves if the UV part of the wavelength spectrum is considered. A data set containing PSD information was recently retrieved from SCIAMACHY limb measurements and provides two parameters of the unimodal lognormal PSD for the SCIAMACHY operational period (2002–2012). In this study, the data set is expanded by aerosol extinction coefficients and Ångström exponents calculated from the retrieved PSD parameters. Parameter errors for the recalculated Ångström exponents and aerosol extinction coefficients are assessed using synthetic retrievals. For the extinction coefficient the resulting parameter error is within ±25 %, and for the Ångström exponent, it is better than 10 %. The SCIAMACHY aerosol extinction coefficients recalculated from PSD parameters are compared to those from SAGE II. The differences between the instruments vary from 0 % to 25 % depending on the wavelength. Ångström exponent comparison with SAGE II shows differences between 10 % at 31 km and 40 % at 18 km. Comparisons with SAGE II, however, suffer from the low number of collocated profiles. Furthermore, the Ångström exponents obtained from the limb-viewing instrument OSIRIS are used for the comparison. This comparison shows an average difference within 7 %. The time series of these differences do not show signatures of any remarkable events (e.g., volcanic eruptions or biomass burning events). In addition, the temporal behaviour of the Ångström exponent in the tropics is analyzed using the SCIAMACHY data set. It is shown that there is no trivial relation between the Ångström exponent value at a single wavelength pair and the PSD because the same value of Ångström exponent can be obtained from an infinite number of combinations of the PSD parameters.

Relationship between physical properties and particle-size distribution of geomaterials

Different from continuous materials, geomaterials often are collections of discrete particles, such as soils, and aggregates. For these materials, the particle size distribution (PSD) is one of their most important properties and, in many circumstances, geomaterials are classified or named according to their PSDs. After many years, scholars have generally reached an agreement that some properties of geomaterials are tightly linked to their PSDs. Identifying and formulating the relationship between PSDs and properties can greatly help practitioners. This study derived the key PSD parameters from 2D Fractal Model and proposed a genetic formula based on the theory of Fourier Series to estimate the different properties of geomaterials from their PSDs. Based on the lab testing data of this study and published results in literature, this study demonstrated that the parameters of 2D Fractal Model fully carried PSD information and could be used to correlate to a variety of geomaterial properties, including maximum and minimum densities, friction angle, and permeability. This study also showcased that 2D Fractal Model parameters were superior to traditional indexes, such as D10, D30, D50, D60, Cc and Cu, to represent PSD information.

Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter.

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

Information-weighted constrained regularization for particle size distribution recovery in multiangle dynamic light scattering

In particle size measurement with dynamic light scattering (DLS), it is difficult to get an accurate recovery of a bimodal particle size distribution (PSD) with a peak position ratio less than ~2:1, especially when large particles (>350nm) are present. This is due to the inherent noise in the autocorrelation function (ACF) data and the scarce utilization of PSD information during the inversion process. In this paper, the PSD information distribution in the ACF data is investigated. It was found that the initial decay section of the ACF contains more information, especially for a bimodal PSD. Based on this, an information-weighted constrained regularization (IWCR) method is proposed in this paper and applied in multiangle DLS analysis for bimodal PSD recovery. By using larger (or smaller) coefficients for weighting the ACF data, more (or less) weight can then be given to the initial part of the ACF. In this way, the IWCR method can enhance utilization of the PSD information in the ACF data, and effectively weaken the effect of noise at large delay time on PSD recovery. Using this method, bimodal PSDs (with nominal diameters of 400:608 nm, 448:608 nm, 500:600 nm) were recovered successfully from simulated data and it appears that the IWCR method can improve the recovery resolution for closely spaced bimodal particles. Results of the PSD recovery from experimental DLS data confirm the performance of this method.

Overcoming physical limitations in alternative growing media with and without peat

Significant efforts are being made to find alternative to peat. In doing so, chemical, biological and physical limitations have appeared for using some alternative components to sphagnum peat in organic growing media. Aeration limitations may be linked to too fine particles limiting air space or too coarse particles limiting gas exchange in growing media, with a working substrates being made of a combination of less than 4 mm material with a coarse fraction between 2-4 mm. High microbial respiration may be a problem solved by increasing air filled porosity, or through an appropriate choice of container geometry. Too high a proportion of coarse fragments may also result in poor capillary rise properties and too low available water, resulting in temporary water stress being experienced by the crop and subsequent yield drops. Increasing the proportion of fines in substrates and the choice of an appropriate container configuration may solve the problem here also. The wettability of alternatives has however to be considered, even if they generally rewet better than peat, because the risk of hydrophobicity varies with material and may evolve during cultivation, with a strong influence on water retention and flow properties. Too low or too high a bulk density at the beginning of the growth has also been observed to reduce rooting into the substrate with poor consequent root and shoot growth, a problem now possibly solved by adjusting the compaction level of the substrate during mechanical potting. Salt levels in some material requires a close follow-up for some waste already high in salts and in material of low biostability. Finally, loss of structural stability has been observed with some composts, which can be compensated by decreasing the proportion of composted material in the substrate. Attempts made to design substrates based on information on particle distribution only has so far reached limited success. Alternatively, characterizing the growing media properties once potted is one additional step proven to be more successful than using particle size distribution information only, because of the important impact that processing, handling and potting as well as container configuration have on physical properties and the subsequent plant growth.

Maillard reaction in natural rubber latex: Improvement of concentration process

ABSTRACTMaillard reaction in natural rubber (NR) latex was studied by treating fresh latex with formaldehyde and glutaraldehyde. Volatile fatty acid number of the treated latex could be lower than acceptable limit (< 0.08) for more than 20 days. Attenuated total reflectance infrared spectroscopy and sodium dodecyl sulfate polyacrylamide gel electrophoresis revealed the occurrence of protein crosslinks in the treated latex. NR treated with glutaraldehyde has much higher nitrogen content than NR treated with formaldehyde and untreated NR, respectively. Colloidal stability of rubber particles, represented by pH, zeta potential, viscosity, and particle size distribution information, were unchanged after treating the latex with formaldehyde. Treatment of the fresh latex with glutaraldehyde 100 and 200 mmol/kg of latex led to the aggregation of rubber particles. Interestingly, the Maillard crosslinking of proteins in fresh latex could efficiently promote the transfer of rubber particles from the serum phase into the cream phase during centrifugation. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45224.

Fractal characteristics of crushed particles of coal gangue under compaction

The surface morphology of compacted and crushed gangue presents self-similarity and other fractal characteristics. This research constructed a fractal model for the particle size of compacted and crushed gangue based on fractal theory and particle size distribution information. To investigate the fractal characteristics of compacted gangue, compaction experiments were carried out under varied stresses and with different particle sizes. Results showed that the particle size distributions of crushed gangue specimens with two distinct lithology exhibited fractal characteristics. Fractal dimension of each crushed specimen ranged from 0.352 to 2.654, with increase of stress, the particle size of each specimen tended to be distributed in a more dispersed fashion. Meanwhile, the fractal dimension increased with increased content of small particles, and tended to be a definite value. While the fractal dimension decreased with increased rock strength for the same initial particle size gradation and stress.

Fractal features of soil particle size distribution in newly formed wetlands in the Yellow River Delta.

The characteristic of particle size distribution (PSD) in the newly formed wetlands in coast has seldom been studied. We applied fractal-scaling theory in assessing soil particle size distribution (PSD) features of newly formed wetlands in the Yellow River Delta (YRD), China. The singular fractal dimensions (D) values ranged from 1.82 to 1.90, the capacity dimension (D0) values ranged from 0.84 to 0.93, and the entropy dimension (D1) values ranged from 0.66 to 0.84. Constrained corresponding analysis revealed that 43.5% of the variance in soil PSD can be explained by environmental factors, including 14.7% by seasonal variation, 8.6% by soil depth, and 8.0% by vegetation type. The fractal dimensions D and D1 were sensitive with fine particles with size ranging less than 126 μm, and D0 was sensitive with coarse particles with size ranging between 126 μm to 2000 μm. Fractal analysis makes full use of soil PSD information, and offers a useful approach to quantify and assess the soil physical attributes in the newly formed wetland.

Integrating activated sludge floc size information in MBR fouling modeling.

Although studied extensively, modeling fouling phenomena in membrane bioreactors (MBRs) remains challenging. It has been well established that cake layer formation and pore blocking have a strong impact on the filtration performance but how to capture that in comprehensive models is not fully defined yet. Since it has been shown that bioflocculation characteristics of activated sludge have a clear link with (the extent of) membrane fouling, this study integrates activated sludge floc size (i.e., particle size distribution) information in the model for pore blocking and cake layer formation with a focus on constant flux operated MBRs. Based on these floc size distributions, a three-dimensional modeling and visualization of the cake layer is envisaged which can then provide the required input information (e.g., the porosity of the cake layer) for the fouling model. The model is calibrated and validated on the basis of experimental data from Hwang et al. (2012) in 'Membrane bioreactor: TMP rise and characterization of biocake structure using CLSM-image analysis' (see J. Membr. Sci. 419-420, 33-41).

An integrated experimental-modeling approach to study the acid leaching behavior of lead from sub-micrometer lead silicate glass particles

This work focuses on the development of a procedure to study the mechanism of leaching of lead from sub-micrometer lead glass particles using 0.3moll−1 HNO3 as a leachant. Glass particles with an effective size distribution range from 0.05 to 1.4μm were generated by laser ablation (213nm Nd:YAG laser) and collected on an inline 0.2μm syringe filter. Subsequently, the glass particles on the filter were subjected to online leaching and continuous monitoring of lead (Pb-208) in the leachate by quadrupole ICP-MS. The lead leaching profile, aided by the particle size distribution information from cascade impaction, was numerically fitted to a mathematical model based on the glass intraparticle diffusion, liquid film distribution and thermodynamic glass-leachant distribution equilibrium. The findings of the modeling show that the rate-limiting step of leaching is the migration of lead from the core to the surface of the glass particle by an ion-exchange mechanism, governed by the apparent intraparticle lead diffusivity in glass which was calculated to be 3.1×10−18m2s−1. Lead leaching is illustrated in the form of graphs and animations of intraparticle lead release (in time and intraparticle position) from particles with sizes of 0.1 and 0.3μm.

Oxygen Electroreduction on Nanoscale Pt/[TaOPO4/VC] and Pt/[Ta2O5/VC] in Alkaline Electrolyte

Oxide- and phosphate-supported catalysts have been shown to promote the oxygen reduction reaction (ORR) in acid [see references in 1 ], and our question herein is whether similar enhancement of the ORR occurs in alkaline condition. We previously reported a high mass- and area-specific ORR activity for an electrocatalyst with 18% loading of 2.4 nm Pt nanoparticles supported on a 3 wt% tantalumoxyphosphate-treated VC (Pt/[TaOPO4/VC]). The electrocatalyst was heat treated at 660 ◦ C in a reducing atmosphere and characterized using thin-film RDE in an acidic electrolyte. 2,3 A two-fold increase in ORR activity was observed for the Pt/[TaOPO4/VC] compared with a commercial Pt/VC electrocatalyst based on RDE results: at 0.90 V vs. RHE, 1600 rpm, and a potential sweep rate of 20 mV s −1 ,am ass and area-specific ORR activity of 0.46 A mgPt −1 and 625 μ Ac mPt −2 , respectively,weremeasuredfortheheattreatedPt/[TaOPO4/VC]electrocatalyst, compared to mass and area-specific ORR activities of 0.24AmgPt −1 and 333 μ Ac mPt −2 , respectively, for a standard Pt/VC electrocatalyst. The Pt/[TaOPO4/VC] electrocatalyst also proved highly durable to extensive voltage cycling in the acid electrolyte. Meanwhile, cyclic voltammetry (CV) of the Pt/[TaOPO4/VC] and Pt/VC showed no difference in onset potentials for oxygen-species adsorption on the Pt, suggesting there was a lack of an electronic effect to change the oxygen coverage of the Pt via a change in bond strength. Further investigation of the Pt/[TaOPO4/VC] electrocatalyst using an in situ X-ray absorption spectroscopy (XAS) difference technique, � μ, showed differences in the surface species adsorbed on the active Pt/[TaOPO4/VC] electrocatalyst compared to Pt/VC. 3 This combination of characterization results led to the conclusion that the higher ORR activity of the 660 ◦ C-heated Pt/[TaOPO4/VC] electrocatalyst was due to the presence of phosphate groups on the surfaces of the Pt nanoparticles, creating a higher proton concentration or acidity at the electrocatalyst surface. Encouraged by the high activity and durability results previously reported, the work presented here examines the ORR performance of Pt/[TaOPO4/VC] and Pt/[Ta2O5/VC] electrocatalysts, heat treated at 660 ◦ C in an Ar atmosphere, in an alkaline electrolyte. The catalyst particle dispersion, size distribution, and structural information were obtained using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). The two supported materials, Pt/[TaOPO4/VC] and Pt/[Ta2O5/VC], and Pt/VC standards were evaluatedinanalkalineelectrolytebyCVandRDEevaluationoftheORR activity.

Nanoparticle size measurement from dynamic light scattering data based on an autoregressive model

The letter puts forward a new method of nanoparticle size measurement using small amounts of data on a dynamic light scattering signal, and establishes an arithmetic model based on Burg arithmetic. According to our method, an autoregressive (AR) model of the nanoparticle dynamic light scattering signal was set up, and the parameters of the AR model were solved, the final prediction error criterion was used to select the most appropriate order of the AR model, the power spectrum density was estimated and the particle size distribution information could be deduced from the inverse of the power spectrum. Standard polystyrene particles of 50, 90 and 100 nm were measured using this method, and the experimental results indicated that the particle size inversion results of this method were more accurate than the autocorrelation method using small amounts of data.