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Titanium dioxide (TiO2) pigment is a non-toxic, particulate material in widespread use and found in everyone’s daily life. The particle size of the anatase or rutile crystals are optimised to produce a pigment that provides the best possible whiteness and opacity. The average particle size is intentionally much larger than the 100 nm boundary of the EU nanomaterial definition, but the TiO2 pigment manufacturing processes results in a finite nanoscale content fraction. This optically inefficient nanoscale fraction needs to be quantified in line with EU regulations. In this paper, we describe the measurement procedures used for product quality assurance by three TiO2 manufacturing companies and present number-based primary particle size distributions (PSDs) obtained in a round-robin study performed on five anatase pigments fabricated by means of sulfate processes in different plants and commonly used worldwide in food, feed, pharmaceutical and cosmetic applications. The PSDs measured by the three titanium dioxide manufacturers based on electron micrographs are in excellent agreement with one another but differ significantly from those published elsewhere. Importantly, in some cases, the PSDs result in a different regulatory classification for some of the samples tested. The electron microscopy results published here are supported by results from other complementary methods including surface area measurements. It is the intention of this publication to contribute to an ongoing discussion on size measurements of TiO2 pigments and other particulate materials and advance the development of widely acceptable, precise, and reproducible measurement protocols for measuring the number-based PSDs of particulate products in the size range of TiO2 pigments.

Dynamic light scattering (DLS) is a highly efficient approach for extracting particle size distributions (PSDs) from autocorrelation functions (ACFs) to measure nanoparticle particles. However, it is a technical challenge to get an exact inversion of the PSD in DLS. Generally, Tikhonov regularization is widely used to address this issue; it uses the L2 norm for both the data fitting term (DFT) and the regularization constraint term. However, the L2 norm’s DFT has poor robustness, and its regularization term lacks sparsity, making the solution susceptible to noise and a reduction in accuracy. To solve this problem, the Lp,q norm restrictive model is formulated to examine the impact of various norms in the DFT and regularization term on the inversion results. On this basis, combined with the robustness of DFT and the sparsity of regularization terms, an L1,∞-constrained Tikhonov regularization model was constructed. This model improves the inversion accuracy of PSD and offers a better noise-resistance performance. Simulation tests reveal that the L1,∞ model has strong noise resistance, exceptional inversion precision, and excellent bimodal resolution. The inversion outcomes for the 33 nm unimodal particles, the 55 nm unimodal, and the 33 nm/203 nm bimodal experimental particles show that L1,∞ reduces peak errors by at most 6.06%, 5.46%, and 12.12%/3.94% compared to L2,2, L1,2, and L2,∞ models, respectively. These simulations are validated by experimental data.

Abstract There is a critical need to generate environmentally relevant microplastics (MPs) and nanoplastics (NPs) to better investigate their behavior in laboratory settings. Environmental MPs are heterogenous in size and shape, unlike monodisperse and uniform microspheres commonly used as surrogates. Cryogenic grinding, or cryomilling, was successfully utilized to transform polystyrene (PS) bulk material into heterogenous micro and nano fragments. Fourier-Transform Infrared (FTIR) spectroscopy confirmed that this approach did not alter polymer surface chemistry. The number of milling cycles (time of milling) and frequency of grinding (intensity of milling) were varied to investigate the role cryomilling parameters had on generated MP characteristics. The resulting particle size distributions of cryomilled samples were measured and compared. Coulter Counter and Nanoparticle Tracking Analysis (NTA) were used to measure the particle size distributions at the micro and nanoparticle size ranges, respectively. Microspheres were used to determine what camera settings yielded more accurate sizing and to reduce bias in the NTA analysis. Increasing milling cycles generally increased the number of smaller particles. The evolution of the measured size distributions indicated that small nanosized fragments broke off from larger MPs during cryomilling, steadily eroding larger MP fragments. The number of milling cycles was observed to more consistently impact the size distributions of fragments compared to the frequency of milling. This study offers both analysis of the cryomilling process and recommendations for generating more realistic PS MP/NPs for examining environmental fate and effects.

Purpose: The goal of this study was to characterize an acellular pertussis vaccine (Tdap) containing genetically modified pertussis toxin (gdPT) and TLR agonist adsorbed to AlOOH adjuvant. Methods: Several analytical tools including nanoDSF, FTIR, and LD were used to examine the conformation of novel gdPT and the composition of AlOOH adjuvant formulations adsorbed to pertussis vaccine. Results: DLS particle size results were 9.3 nm and 320 nm for gdPT. For pertussis toxoid (PT), the DLS particle size results were larger at ~440 nm. After adsorption to AlOOH, which was driven by the protein antigen, the size distribution ranged from 3.5 to 22 µm. Two thermal transitions were observed by DSC for gdPT at 70 °C and 102 °C. The main thermal transition was confirmed to be at 72 °C by nanoDSF. All three vaccine formulations showed one thermal transition: Tdap-AlOOH had a thermal transition of 74.6 °C, Tdap-E6020-AlOOH had a thermal transition at 74.2 °C, and Tdap-CpG-AlOOH had a thermal transition at 77.0 °C. Analysis of pertussis toxin (PTx) and gdPT was also performed by FTIR spectroscopy for the purpose of comparison. The second derivative of the FTIR spectra showed an additional feature for PTx at 1685 cm−1 compared to gdPT. The antigen’s amide I and II regions were largely unchanged after adsorption to AlOOH adjuvant as shown by FTIR, suggesting that there were no significant changes in the secondary structure. Conclusion: gdPT conformation was successfully characterized using an array of analytical methods. All three Tdap formulations have similar thermal stability as shown by nanoDSF, similar size distribution as shown by LD, and similar overall secondary structure as shown by FTIR. In-line particle sizing and IR can be used as in-process characterization tools to monitor consistency of adsorbed vaccine and to confirm product identity.

Dynamic Light Scattering is a well-established technique used in particle sizing. An alternative procedure for Dynamic Light Scattering time series processing based on spectral entropy computation and Artificial Neural Networks is described. An error analysis of the proposed method was carried out and the results on both the simulated and on the experimental DLS time series are presented in detail. The results reveal the possibility of designing an advanced sensor capable of detecting particles with a size bigger than a threshold using this alternative for processing the DLS time series.

The state of a suspension is crucial with regard to processing pathways, functionality and performance of the end product. In the past decade, substantial progress has been made in designing highly specialized and functionalized particles. In current particle technology, besides classic particle properties such as particle size distribution, shape and density, surface properties play an essential role for processing, product specification and use. For example, in medical therapy, analytical diagnostic applications, as well as in separation processing and harvesting of high-valued materials, magnetic micro- and nanoparticles play an increasing role. In addition to traditional parameters such as size, the particle magnetization has to be quantified here.Sedimentation techniques have been used for hundreds of years to determine the geometrical characteristics of dispersed particles. Numerous national and international standards regarding these techniques have been published. Mainly due to the fast growing market share of laser scattering techniques over the past two decades, most customers these days are not aware of some advantageous features of particle characterization via a first-principle fractionating approach such as sedimentation. This is unfortunate as sedimentation techniques have made huge technological leaps forward regarding electronics, sensors and computing abilities.This paper aims to give a short review about different cumulative and incremental sedimentation approaches to measure the particle size distribution. It focuses mainly on the in-situ visualization (STEP-Technology®) of particle migration in gravitational and centrifugal fields. It describes the basics of the new multi-sample measuring approaches to quantify the separation kinetics by spatial and time-resolved particle concentration over the entire sample height. Based on these data, the sedimentation velocity and particle size distribution are elucidated and estimates of accuracy, precision and experimental uncertainties are discussed. Multi-wavelength approaches, correction of higher concentration, and the influence of rheological behavior of continuous phase will also be discussed. Applications beyond the traditional scope of sedimentation analysis are presented. This concerns the in-situ determination of hydrodynamic particle density and of magnetophoretic velocity distributions for magnetic particulate objects.

Dynamic Light Scattering is a technique currently used to assess the particle size and size distribution by processing the scattered light intensity. Typically, the particles to be investigated are suspended in a liquid solvent. An analysis of the particular conditions required to perform a light scattering experiment on particles in air is presented in detail, together with a simple experimental setup and the data processing procedure. The results reveal that such an experiment is possible and using the setup and the procedure, both simplified to extreme, enables the design of an advanced sensor for particles and fumes that can output the average size of the particles in air.

Bacille Calmette–Guerin, BCG, is a live attenuated bovine tubercle bacillus used for the treatment of non-muscle invasive bladder cancer. In this study, an Electrical Sensing Zone (ESZ) method was developed to measure the particle count and the size of BCG immunotherapeutic (BCG IT), or ImmuCyst® product using a Coulter Counter Multisizer 4® instrument. The focus of this study was to establish a baseline for reconstituted lyophilized BCG IT product using visible and sub-visible particle concentration and size distribution as reportable values. ESZ method was used to assess manufacturing process consistency using 20 production scale lots of BCG IT product. The results demonstrated that ESZ can be used to accumulate product and process knowledge of BCG IT.

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.

Whispering gallery mode resonators such as silica microtoroids can be used as sensitive biochemical sensors. One sensing modality is mode-splitting, where the binding of individual targets to the resonator breaks the degeneracy between clockwise and counter-clockwise resonant modes. Compared to other sensing modalities, mode-splitting is attractive because the signal shift is theoretically insensitive to the polar coordinate where the target binds. However, this theory relies on several assumptions, and previous experimental and numerical results have shown some discrepancies with analytical theory. More accurate numerical modeling techniques could help to elucidate the underlying physics, but efficient 3D electromagnetic finite-element method simulations of large microtoroid (diameter ~90 µm) and their resonance features have previously been intractable. In addition, applications of mode-splitting often involve bacteria or viruses, which are too large to be accurately described by the existing analytical dipole approximation theory. A numerical simulation approach could accurately explain mode splitting induced by these larger particles. Here, we simulate mode-splitting in a large microtoroid using a beam envelope method with periodic boundary conditions in a wedge-shaped domain. We show that particle sizing is accurate to within 11% for radii a<λ/7, where the dipole approximation is valid. Polarizability calculations need only be based on the background media and need not consider the microtoroid material. This modeling approach can be applied to other sizes and shapes of microresonators in the future.

In this work we focus on the characterization of micro- and nano-powders typically adopted for chemical mechanical polishing, extensively used whenever the global and local planarization of surfaces is required as in nanoelectronic fabs. We present an innovative method for the accurate characterization of water suspensions of nanoparticles. It relies upon the combination of a new approach to extract light-scattering information from single particles and the recently developed diagnostic tool named Single Particle Extinction and Scattering. It can be used in line. Data interpretation becomes independent of any a-priori assumptions about the samples. The results of accurate measurements performed on ceria as well as aluminium oxide slurries are reported. We show the strong advantages of this method compared with traditional ones by explicitly reporting experimental results on calibrated spheres made of different materials. We discuss possible applications for in-line characterization of ultrapure water, chemicals, slurries for abrasive processes, for example, as well as the detection of any undesired particles – which could be the key for future improvements to advanced process control systems.

The effect of the design and process parameters of a particle-classification zone on the sizing precision of a plate differential mobility analyzer (PDMA) was investigated by simulation software in this work. The design parameters consisted of the length, width, and height of the classification zone, and the process parameters could lead to height deviation and an uneven classification-zone wall. Prior to investigating the parameters, a comparison between experimental voltages and PDMA simulation results was applied to validate the simulation model. Thanks to velocity-field and particle-trajectory simulations, as well as experimental data from the former version of the PDMA, the dimensions of the classification zone and other key parameters were optimized. Our research shows that the dimensions of the classification zone influence the applied voltages and the measuring range of the PDMA, and range can be extended to 500 nm with low applied voltages after optimization. When the reasonable error range of the height is ±2% for the classification zone, the PDMA could basically achieve a classification role, and when it is ±1%, the PDMA has a better classifying effect.

RNA interference (RNAi) has been harnessed to produce a new class of drugs for treatment of various diseases. This review summarizes the most important parameters that govern the silencing efficiency and duration of the RNAi effect such as small interfering RNA (siRNA) stability and modification, the type of delivery system and particle sizing methods. It also discusses the predominant barriers for siRNA delivery, such as off-target effects and introduces internalization, endosomal escape and mathematical modeling in RNAi therapy and combinatorial RNAi. At present, effective delivery of RNAi therapeutics in vivo remains a challenge although significant progress has been made in this field.