Monique E. Johnson

In response to the growing concern of microplastics (1 μm to 5 mm) accumulation affecting human health, the development of analytical methods continues to be critical for the detection and characterization of microplastic particles. In this context, pursuing exceptional particle detection capability down to practical low levels and rapid analyses with high sample throughput makes single particle inductively coupled plasma mass spectrometry (spICP-MS) very attractive for microplastics analysis. Existing spICP-MS-based studies have routinely shown limitations in the accurate sizing and quantification of particle number concentration through targeting carbon content, with reported size limits of detection in the range of 0.62 to 1.8 μm and a substantial reduction in the transport of particles larger than 3 μm. In this work, the linear dynamic range of spICP-MS for the accurate quantification of polystyrene microparticles (PS MPs) via the monitoring of their carbon content (¹³C⁺) is extended to larger particle sizes (5 μm) through using a high efficiency sample introduction system with rigorous optimization of the ¹³C signal and operating at a lowered nebulizer gas flow to improve sample transport of larger particles to the plasma. Reliable quantification of particle number concentration (PNC), accepted as falling within 20% of expected particle stock concentrations, was achieved through a 20% lowered nebulizer gas flow for a full suite of commercial PS MPs ranging from 2 to 5 μm as well as a 2.2 and 4.8 μm PS MP contained within mixtures of the two materials, regardless of PNC ratio.

The particle number concentration (PNC) in a suspension is a key measurand in nanotechnology. A common approach for deriving PNC is to divide the total mass concentration by the per-particle mass, calculated as density times volume. The volume is most frequently derived from the arithmetic mean diameter (AMD) of the size distribution. The harmonic mean volume (HMV) has also been used. Given a known size distribution, we show that the correct PNC is obtained by using the arithmetic mean volume (AMV). The AMD-based volume results in an overestimate in PNC that increases superlinearly with increasing coefficient of variation (CV), reaching 12% at CV = 0.2 for a normal distribution. HMV would yield a much greater overestimate, exceeding 50%. The error in the AMD-derived PNC shows only weak skew dependence, suggesting a simple approximate correction as a function of CV in the common situation where AMD and CV are known but the overall size distribution is unknown. Using published data sets of gold nanoparticles, we demonstrate an overall consistency of ±1.1% in comparing the PNC directly determined by single-particle inductively coupled plasma-mass spectrometry (spICP-MS) and the PNCs derived from AMV using size distributions independently measured by high-resolution scanning electron microscopy and spICP-MS. We further compare AMV and AMD-derived PNCs for well-characterized polystyrene nanoparticle standards, illustrating sensitivity to distributional characteristics along with common errors to avoid. Nanoparticles in environmental samples, food additives, and nanomedicines often have CVs greater than 0.3, for which uncorrected AMD-derived PNC errors can exceed 35%.

The National Institute of Standards and Technology (NIST) Standard Reference Material® (SRM®) 956e Electrolytes in Frozen Human Serum is intended for use in the calibration and validation of procedures and methods employed in clinical analysis for the determination of electrolytes in either diluted or undiluted human serum or plasma. This SRM can be used for calibrating direct-reading ion-selective electrode (ISE) analyzers, and for validating methods used to measure electrolytes in similar matrices. The material was produced commercially in accordance with the protocol described in Clinical Laboratory Standards Institute (CLSI) Standard C29-A2, Standardization of Sodium and Potassium Ion-Selective Electrode Systems to the Flame Photometric Reference Method. This publication documents the production, analytical measurements, and statistical evaluations leading to the certification of the SRM.

The National Institute of Standards and Technology (NIST) SRM 1568c Rice Flour has value assignments for over 30 analytes, including arsenic species. This material is intended to be used for the evaluation of methods for the determination of elements and arsenic species in this and similar matrices. The material was purchased from commercial vendors. Measurement results were obtained from NIST, National Metrology Institute of Japan (NMIJ), and interlaboratory comparison exercises. This document describes the material, sample preparations, results, and data analysis.

The National Institute of Standards and Technology (NIST) Reference Material (RM) 8210 Hemp Plant delivers non-certified values for cannabinoids and toxic elements in a dried ground hemp plant material to help cannabis and forensic laboratories for use as a control and research material. A unit of RM 8210 contains three sample packets (approximately 1.5 g each), each sealed with a desiccant pouch in an aluminized polyester bag. This publication documents the production, analytical methods, and computations involved in characterizing this product.

Due to enhanced properties at the nanoscale, nanomaterials (NMs) have been incorporated into foods, food additives, and food packaging materials. Knowledge gaps related to (but not limited to) fate, transport, bioaccumulation, and toxicity of nanomaterials have led to an expedient need to expand research efforts in the food research field. While classical techniques can provide information on dilute suspensions, these techniques sample a low throughput of nanoparticles (NPs) in the suspension and are limited in the range of the measurement metrics so orthogonal techniques must be used in tandem to fill in measurement gaps. New and innovative characterization techniques have been developed and optimized for employment in food nano-characterization. Single particle inductively coupled plasma mass spectrometry, a high-throughput nanoparticle characterization technique capable of providing vital measurands of NP-containing samples such as size distribution, number concentration, and NP evolution has been employed as a characterization technique in food research since its inception. Here, we offer a short, critical review highlighting existing studies that employ spICP-MS in food research with a particular focus on method validation and trends in sample preparation and spICP-MS methodology. Importantly, we identify and address areas in research as well as offer insights into yet to be addressed knowledge gaps in methodology.

To fully understand the potential ecological and human health risks from nanoplastics and microplastics (NMPs) in the environment, it is critical to make accurate measurements. Similar to past research on the toxicology of engineered nanomaterials, a broad range of measurement artifacts and biases are possible when testing their potential toxicity. For example, antimicrobials and surfactants may be present in commercially available NMP dispersions, and these compounds may account for toxicity observed instead of being caused by exposure to the NMP particles. Therefore, control measurements are needed to assess potential artifacts, and revisions to the protocol may be needed to eliminate or reduce the artifacts. In this paper, we comprehensively review and suggest a next generation of control experiments to identify measurement artifacts and biases that can occur while performing NMP toxicity experiments. This review covers the broad range of potential NMP toxicological experiments, such as in vitro studies with a single cell type or complex 3-D tissue constructs, in vivo mammalian studies, and ecotoxicity experiments testing pelagic, sediment, and soil organisms. Incorporation of these control experiments can reduce the likelihood of false positive and false negative results and more accurately elucidate the potential ecological and human health risks of NMPs.

This study compared results of nanoparticle number concentration measurements collected from 74 instruments hosted across 50 laboratories, providing users with useful discussion and reference data to assess and benchmark their measurement capability.

Particle number concentration (PNC) measurements are critical for research and regulatory decision making related to the potential applications and implications of nanotechnology. However, the degree to which different analytical methods yield similar PNCs has not yet been studied. In this study, monodisperse gold nanoparticles (AuNPs) with varying sizes (30 nm or 60 nm) and surface coatings (citrate, polyvinylpyrrolidone, or branched polyethyleneimine) were evaluated using five techniques: scanning electron microscopy (SEM), dynamic light scattering (DLS), differential mobility analysis (DMA), nanoparticle tracking analysis (NTA), and single particle inductively coupled plasma-mass spectrometry (spICP-MS). The two techniques that only measured the NP core size (spICP-MS and SEM), as opposed to the larger hydrodynamic diameter, yielded PNCs with the closest agreement (within 20% of each other), while PNCs among all techniques sometimes varied by a factor of 3. Positively charged AuNPs coated with branched polyethyleneimine yielded the most variable results. Deriving the PNC using the particle size distribution has several advantages over using only the mean size based on these results and statistical modeling given the substantial impact of the tails of the distribution toward smaller particles. The size distributions measured by the different techniques were also used to model the AuNP concentration that would reach the cells in an in vitro toxicity experiment. Surprisingly, there was a strong impact of the analytical technique on the modeled cellular AuNP concentration for some of the AuNPs.