Estimation of particle size distributions from bulk scattering spectra: sensitivity to distribution type and spectral noise

Aerosol Sauter mean diameter measurement based on the light scattering response of the combined particle volume-surface area

The relationship between the numerical of Sauter mean diameter (SMD) and aerosol distribution parameters, as well as its physical significance are lacking in detailed research. Meanwhile, existing method is not widely used for SMD accurate measurement due to many restrictions on the incident light wavelengths. In this study, we analyzed the relationship between SMDs and the mean and median values of the lognormal, normal, and Weibull distributions with different parameters. It is found that SMD can be directly used to substitute the mean particle size in lognormal distributions with slight deviations. A new method for aerosol SMD measurement with no wavelength limitation based on the light scattering response of the combined volume-surface area of particles is proposed. SMD inversion results show that this method reduces the error caused by inconsistent integration of the wavelength of incident light and particle size in existing measurements, because has no limitation on the wavelength of incident light. SMDs of N-Heptane combustion smokes measured using the developed sensor indicates that our proposed SMD measurement method effectively compensates the shortcomings of the existing method and improves the measurement accuracy, with the minimum and average errors of 8.9% and 14.78%, respectively.

A Simple Model to Estimate the Number of Metal Engineered Nanoparticles in Samples Using Inductively Coupled Plasma Optical Emission Spectrometry

Accurate determination of the size and the number of nanoparticles plays an important role in many different environmental studies of nanomaterials, such as fate, toxicity, and occurrence in general. This work presents an accurate model that estimates the number of nanoparticles from the mass and molar concentration of gold nanoparticles (AuNPs) in water. Citrate-capped AuNPs were synthesized and characterized using transmission electron microscopy (TEM) and ultraviolet–visible spectroscopy (UV-vis). A mimic of environmental matrices was achieved by spiking sediments with AuNPs, extracted with leachate, and separated from the bulk matrix using centrifuge and phase transfer separation techniques. The quantification of AuNPs’ molar concentration on the extracted residues was achieved by inductively coupled plasma optical emission spectroscopy (ICP-OES). The molar concentrations, an average diameter of 27 nm, and the colloidal suspension volumes of AuNPs enable the calculation of the number of nanoparticles in separated residues. The plot of the number of AuNPs against the mass of AuNPs yielded a simple linear model that was used to estimate the number of nanoparticles in the sample using ICP-OES. According to the authors’ knowledge, this is the first adaptation of the gravimetric method to ICP-OES for estimating the number of nanoparticles after separation with phase transfer.

Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties

Retrieval of particle size distribution from bulk optical properties based on evolutionary algorithms is usually computationally expensive. In this paper, we report an efficient numerical approach to solving the inverse scattering problem by accelerating the calculation of bulk optical properties based on machine learning. With the assumption of spherical particles, the forward scattering by particles is first solved by Mie scattering theory and then approximated by machine learning. The particle swarm optimization algorithm is finally employed to optimize the particle size distribution parameters by minimizing the deviation between the target and simulated bulk optical properties. The accuracies of machine learning and particle swarm optimization are separately investigated. Meanwhile, both monomodal and bimodal size distributions are tested, considering the influences of random noise. Results show that machine learning is capable of accurately predicting the scattering efficiency for a specific size distribution in approximately 0.5 µs on a standalone computer. Therefore, the proposed method has the potential to serve as a powerful tool in real-time particle size measurement due to its advantages of simplicity and high efficiency.

Light Scattering Intensity Field Imaging Sensor for In Situ Aerosol Analysis

Aerosol plays an important role in a broad range of scientific disciplines, such as atmospheric chemistry and physics, fuel combustion, and human health. Current particle sizing instruments are usually bulky, complicated, and expensive, while the portable ones cannot provide sufficient measurement channels to describe the particle size distribution accurately. To address this challenge, we propose an optical sensing method to analyze the particle size distribution of aerosols based on the light scattering intensity field (LSIF). The LSIF is a set of scattering lights in all directions around the particles, which contains the scattering light signals in different observing angles. Then, the particle size distribution of the aerosol samples is retrieved by the Tikhonov regularization algorithm. A portable and low-cost aerosol sizing prototype sensor is designed to image part of the LSIF signals, where the LSIF is collected by a parabolic reflector and projected on the image sensor as an image with telecentric lenses. According to the experimental result of di-ethyl-hexyl-sebacate aerosol test, the relative measurement error of LSIF can be controlled to ±10%. With an integrated and cost-effective design, this particle sizing sensor shows great potential for routine field measurements outside of the laboratory.

Estimation of Particle Size Distribution from Bulk Scattering Spectra: Validation on Monomodal Suspensions

A particle size distribution (PSD) estimation method based on light-scattering properties was validated on experimental visible/near-infrared scattering spectra of polystyrene suspensions, with a nominal particle size ranging from 0.1 to 12 μm in diameter. On the basis of μ_s and g spectra extracted from double integrating sphere measurements, good PSD estimates were obtained for particles ≥1 μm. The particle volume fraction estimates in the case of μ_s were close to the target concentrations, although influenced by small baseline fluctuations on the spectra. For submicrometer particles, on the other hand, the non-oscillating μ_s spectra lack discriminating power, resulting in erroneous PSD estimates. The reduced scattering coefficient spectra (μ_s') were found less useful for particle size estimation as they lack a characteristic shape, causing an over- or underestimation of the distribution width. In summary, the estimation routine proved to deliver PSD estimates in line with the reference measurements for micrometer-sized or larger particles based on their μ_s and g scattering spectra. Additional validation on more polydisperse samples forms the next step before going to bimodal PSD estimates.