Novel techniques for detection and characterization of nanomaterials based on aerosol science supporting environmental applications

Analysis of Nanomaterials on Biological and Environmental Systems and New Analytical Methods for Improved Detection

The advancing field of nanoscience has produced lower mass, smaller size, and expanded chemical composition nanoparticles over recent years. These new nanoparticles have challenged traditional analytical methods of qualification and quantification. Such advancements in nanoparticles and nanomaterials have captured the attention of toxicologists with concerns regarding the environment and human health impacts. Given that nanoparticles are only limited by size (1–100 nm), their chemical and physical characteristics can drastically change and thus alter their overall nanotoxicity in unpredictable ways. A significant limitation to the development of nanomaterials is that traditional regulatory and scientific methods used to assess the biological and environmental toxicity of chemicals do not generally apply to the assessment of nanomaterials. Significant research effort has been initiated, but much more is still needed to develop new and improved analytical measurement methods for detecting and quantitating nanomaterials in biological and environmental systems.

Automation and Standardization-A Coupled Approach towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis

Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by automation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been developed, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air-liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.

Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors

Air pollution with PM2.5 (particulate matter smaller than 2.5 micro-metres in diameter) is a major health hazard in many cities worldwide, but since measuring instruments have traditionally been expensive, monitoring sites are rare and generally show only background concentrations. With the advent of low-cost, wirelessly connected sensors, air quality measurements are increasingly being made in places where many people spend time and pollution is much worse: on streets near traffic. In the interests of enabling members of the public to measure the air that they breathe, we took an open-source approach to designing a device for measuring PM2.5. Parts are relatively cheap, but of good quality and can be easily found in electronics or hardware stores, or on-line. Software is open source and the free LoRaWAN-based "The Things Network" the platform. A number of low-cost sensors we tested had problems, but those selected performed well when co-located with reference-quality instruments. A network of the devices was deployed in an urban centre, yielding valuable data for an extended time. Concentrations of PM2.5 at street level were often ten times worse than at air quality stations. The devices and network offer the opportunity for measurements in locations that concern the public.

A versatile generator of nanoparticle aerosols. A novel tool in environmental and occupational exposure assessment

The increasing presence of nanotechnology on the market entails a growing probability of finding ENMs in the environment. Nanoparticles aerosols are a yet unknown risk for human and environmental exposure that may normally occur at any point during the nanomaterial lifecycle. There is a research gap in standardized methods to assess the exposure to airborne nanoparticles in different environments. The controllable generation of nanoparticle aerosols has long been a challenging objective for researchers and industries dealing with airborne nanoparticles. In this work, a versatile system to generate nanoparticulate aerosols has been designed. The system allows the production of both i) instantaneous nanoparticle clouds and ii) continuous nanoparticle streams with quasi-stable values of particle concentration and size distribution. This novel device uses a compressed-air pressure pulse to disperse the target material into either the testing environment (instantaneous cloud formation) or a secondary chamber, from which a continuous aerosol stream can be drawn, with a tunable nanoparticle concentration. The system is robust, highly versatile and easy to operate, enabling reproducible generation of aerosols from a variety of sources. The system has been verified with four dry nanomaterials: TiO₂, ZnO, CuO and CNT bundles.