No abstract

The sonication process is commonly used for de-agglomerating and dispersing nanomaterials in aqueous based media, necessary to improve homogeneity and stability of the suspension. In this study, a systematic step-wise approach is carried out to identify optimal sonication conditions in order to achieve a stable dispersion. This approach has been adopted and shown to be suitable for several nanomaterials (cerium oxide, zinc oxide, and carbon nanotubes) dispersed in deionized (DI) water. However, with any change in either the nanomaterial type or dispersing medium, there needs to be optimization of the basic protocol by adjusting various factors such as sonication time, power, and sonicator type as well as temperature rise during the process. The approach records the dispersion process in detail. This is necessary to identify the time points as well as other above-mentioned conditions during the sonication process in which there may be undesirable changes, such as damage to the particle surface thus affecting surface properties. Our goal is to offer a harmonized approach that can control the quality of the final, produced dispersion. Such a guideline is instrumental in ensuring dispersion quality repeatability in the nanoscience community, particularly in the field of nanotoxicology.

Pristine engineered nanomaterials (NMs) entering the aquatic environment become ‘aged’ during their lifetime via chemical, physical and/or biological process.

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.

Understanding the environmental behaviour of nanoparticles (NPs) after release into aquatic systems is essential to predict the environmental implications of nanotechnology. Silver nanoparticles (AgNPs) represent a major class of engineered NPs with a significant potential for environmental impact. Therefore, investigating their transformations in natural waters will help predict their long term environmental fate and behaviour. AgNPs were characterized in natural lake water collected seasonally from the same freshwater source, using column microcosms to assess their behaviour and transport at different depths. Building on our previous work using similar systems with synthetic waters, the influence of water chemistry and NP surface modifications on colloidal stability and dissolution in natural lake water over time was investigated. A simple sedimentation-diffusion model parameterized by the particle properties and total Ag concentration was successfully used to understand AgNPs transport behaviour. PVP coated AgNPs remained colloidally stable, with their transport in the water column dominated by diffusion, and exhibited no significant or substantial changes in data or model parameters for different seasons. Citrate coated AgNPs were susceptible to rapid aggregation, sedimentation, dissolution and reprecipitation; their transport in the water column was determined by both diffusion and sedimentation.