Modeling the Toxicokinetics of Suspensions of Soluble Metallic Nanomaterials.

Proper risk assessment of the many new nanoforms (NFs) that are currently being developed and marketed is hindered by constraints in time and resources for testing their fate and (eco) toxicity profile. This problem has also been encountered in conventional chemical risk assessments, where the definition of related chemical groups can facilitate risk assessment for all class members. Whereas grouping and read-across methods are well established, such approaches are in the early stages of development for NFs. In this study, a modeling framework was developed for grouping NFs into distinct classes regarding the contribution of released ions to suspension-induced toxicity. The framework is based on combining dissolution rate constants of NFs with information about the toxicokinetics of the NFs and the dissolution products formed. The framework is exemplified for the specific case of suspension toxicity of metallic NFs (silver and copper). To this end, principles of mixture toxicity and dose-response modeling are integrated to derive threshold values for the key NF properties determining suspension toxicity: size, shape, and chemical composition. The threshold values thus derived offer a possible solution for the high-throughput screening of NFs according to their morphological and compositional properties in a regulatory context.

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective.

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

Experimental evidence for neonicotinoid driven decline in aquatic emerging insects.

There is an ongoing unprecedented loss in insects, both in terms of richness and biomass. The usage of pesticides, especially neonicotinoid insecticides, has been widely suggested to be a contributor to this decline. However, the risks of neonicotinoids to natural insect populations have remained largely unknown due to a lack of field-realistic experiments. Here, we used an outdoor experiment to determine effects of field-realistic concentrations of the commonly applied neonicotinoid thiacloprid on the emergence of naturally assembled aquatic insect populations. Following application, all major orders of emerging aquatic insects (Coleoptera, Diptera, Ephemeroptera, Odonata, and Trichoptera) declined strongly in both abundance and biomass. At the highest concentration (10 µg/L), emergence of most orders was nearly absent. Diversity of the most species-rich family, Chironomidae, decreased by 50% at more commonly observed concentrations (1 µg/L) and was generally reduced to a single species at the highest concentration. Our experimental findings thereby showcase a causal link of neonicotinoids and the ongoing insect decline. Given the urgency of the insect decline, our results highlight the need to reconsider the mass usage of neonicotinoids to preserve freshwater insects as well as the life and services depending on them.

Response of Chlorella vulgaris to exposure to CuO NPs: Contributions of particulate and dissolved metal forms as modulated by tannic acid and pH.

A suspension of copper oxide nanoparticles (CuO NPs) is a mixture of dissolved and particulate Cu, the relative proportions of which highly depend on the water chemistry. However, the relationship between different proportions of particulate and dissolved Cu and the overall toxicity of CuO NPs is still unknown. This study investigated the response of Chlorella vulgaris to CuO NPs at varying solution pH and at different tannic acid (TA) additions, with a focus on exploring whether and how dissolved and particulate Cu contribute to the overall toxicity of CuO NPs. The results of the exposure experiments demonstrated the involvement of both dissolved and particulate Cu in inducing toxicity of CuO NPs, and the inhibition of CuO NPs on cell density of Chlorella vulgaris was found to be significantly (p < 0.05) alleviated with increased levels of TA and pH (< 8). Using the independent action model, the contribution to toxicity of particulate Cu was found to be enhanced with increasing pH values and TA concentrations. The toxic unit indicator better (R2 = 0.86, p < 0.001) explained impacts of CuO NPs on micro-algae cells than commonly used mass concentrations (R2 = 0.27-0.77, p < 0.05) across different levels of pH and TA. Overall, our study provides an additivity-based method to improve the accuracy of toxicity prediction through including contributions to toxicity of both dissolved and particulate Cu and through eliminating the uneven distribution of data due to large variations in total Cu, particulate Cu, dissolved Cu, Cu2+ activities, Cu-TA complexes and other Cu-complexes concentrations with varying water chemistry conditions.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms.

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

Toxicokinetics and Particle Number-Based Trophic Transfer of a Metallic Nanoparticle Mixture in a Terrestrial Food Chain.

Herein, we investigated to which extent metallic nanoparticles (MNPs) affect the trophic transfer of other coexisting MNPs from lettuce to terrestrial snails and the associated tissue-specific distribution using toxicokinetic (TK) modeling and single-particle inductively coupled plasma mass spectrometry. During a period of 22 days, snails were fed with lettuce leaves that were root exposed to AgNO3 (0.05 mg/L), AgNPs (0.75 mg/L), TiO2NPs (200 mg/L), and a mixture of AgNPs and TiO2NPs (equivalent doses as for single NPs). The uptake rate constants (ku) were 0.08 and 0.11 kg leaves/kg snail/d for Ag and 1.63 and 1.79 kg leaves/kg snail/d for Ti in snails fed with NPs single- and mixture-exposed lettuce, respectively. The elimination rate constants (ke) of Ag in snails exposed to single AgNPs and mixed AgNPs were comparable to the corresponding ku, while the ke for Ti were lower than the corresponding ku. As a result, single TiO2NP treatments as well as exposure to mixtures containing TiO2NPs induced significant biomagnification from lettuce to snails with kinetic trophic transfer factors (TTFk) of 7.99 and 6.46. The TTFk of Ag in the single AgNPs treatment (1.15 kg leaves/kg snail) was significantly greater than the TTFk in the mixture treatment (0.85 kg leaves/kg snail), while the fraction of Ag remaining in the body of snails after AgNPs exposure (36%) was lower than the Ag fraction remaining after mixture exposure (50%). These results indicated that the presence of TiO2NPs inhibited the trophic transfer of AgNPs from lettuce to snails but enhanced the retention of AgNPs in snails. Biomagnification of AgNPs from lettuce to snails was observed in an AgNPs single treatment using AgNPs number as the dose metric, which was reflected by the particle number-based TTFs of AgNPs in snails (1.67, i.e., higher than 1). The size distribution of AgNPs was shifted across the lettuce-snail food chain. By making use of particle-specific measurements and fitting TK processes, this research provides important implications for potential risks associated with the trophic transfer of MNP mixtures.

ZZS similarity tool: The online tool for similarity screening to identify chemicals of potential concern.

Screening and prioritization of chemicals is essential to ensure that available evaluation capacity is invested in those substances that are of highest concern. We, therefore, recently developed structural similarity models that evaluate the structural similarity of substances with unknown properties to known Substances of Very High Concern (SVHC), which could be an indication of comparable effects. In the current study the performance of these models is improved by (1) separating known SVHCs in more specific subgroups, (2) (re-)optimizing similarity models for the various SVHC-subgroups, and (3) improving interpretability of the predicted outcomes by providing a confidence score. The improvements are directly incorporated in a freely accessible web-based tool, named the ZZS similarity tool: https://rvszoeksysteem.rivm.nl/ZzsSimilarityTool. Accordingly, this tool can be used by risk assessors, academia and industrial partners to screen and prioritize chemicals for further action and evaluation within varying frameworks, and could support the identification of tomorrow's substances of concern.

The Road to Achieving the European Commission's Chemicals Strategy for Nanomaterial Sustainability-A PATROLS Perspective on New Approach Methodologies.

The European Green Deal outlines ambitions to build a more sustainable, climate neutral, and circular economy by 2050. To achieve this, the European Commission has published the Chemicals Strategy for Sustainability: Towards a Toxic-Free Environment, which provides targets for innovation to better protect human and environmental health, including challenges posed by hazardous chemicals and animal testing. The European project PATROLS (Physiologically Anchored Tools for Realistic nanOmateriaL hazard aSsessment) has addressed multiple aspects of the Chemicals Strategy for Sustainability by establishing a battery of new approach methodologies, including physiologically anchored human and environmental hazard assessment tools to evaluate the safety of engineered nanomaterials. PATROLS has delivered and improved innovative tools to support regulatory decision-making processes. These tools also support the need for reducing regulated vertebrate animal testing; when used at an early stage of the innovation pipeline, the PATROLS tools facilitate the safe and sustainable development of new nano-enabled products before they reach the market.

Predicted Adsorption Affinity for Enteric Microbial Metabolites to Metal and Carbon Nanomaterials.

Ingested nanomaterials are exposed to many metabolites that are produced, modified, or regulated by members of the enteric microbiota. The adsorption of these metabolites potentially affects the identity, fate, and biodistribution of nanomaterials passing the gastrointestinal tract. Here, we explore these interactions using in silico methods, focusing on a concise overview of 170 unique enteric microbial metabolites which we compiled from the literature. First, we construct quantitative structure-activity relationship (QSAR) models to predict their adsorption affinity to 13 metal nanomaterials, 5 carbon nanotubes, and 1 fullerene. The models could be applied to predict log k values for 60 metabolites and were particularly applicable to 'phenolic, benzoyl and phenyl derivatives', 'tryptophan precursors and metabolites', 'short-chain fatty acids', and 'choline metabolites'. The correlations of these predictions to biological surface adsorption index descriptors indicated that hydrophobicity-driven interactions contribute most to the overall adsorption affinity, while hydrogen-bond interactions and polarity/polarizability-driven interactions differentiate the affinity to metal and carbon nanomaterials. Next, we use molecular dynamics (MD) simulations to obtain direct molecular information for a selection of vitamins that could not be assessed quantitatively using QSAR models. This showed how large and flexible metabolites can gain stability on the nanomaterial surface via conformational changes. Additionally, unconstrained MD simulations provided excellent support for the main interaction types identified by QSAR analysis. Combined, these results enable assessing the adsorption affinity for many enteric microbial metabolites quantitatively and support the qualitative assessment of an even larger set of complex and biologically relevant microbial metabolites to carbon and metal nanomaterials.

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials.

The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs.

Applicability of nanomaterial-specific guidelines within long-term Daphnia magna toxicity assays: A case study on multigenerational effects of nTiO2 and nCeO2 exposure in the presence of artificial daylight.

In recent years, various ecotoxicological test guidelines and (technical) guidance documents have been evaluated and updated with regard to their applicability to nanomaterials (NMs). Several of these have currently reached official regulatory status. Ensuring their harmonized implementation with previously recognized methods for ecotoxicity testing of chemicals is a crucial next step towards effective and efficient regulation of NMs. In the present study, we evaluated the feasibility of assessing multigenerational effects in the first generation of offspring derived from exposed Daphnia magna whilst maintaining test conditions in accordance with regulatory test guidelines and guidance documents for NMs. To do so, we integrated the recommendations for ecotoxicological testing of NMs as defined in OECD Guidance Document 317 into an extended long-term D. magna reproduction test method (OECD Test Guideline 211) and assessed effects of two poorly soluble NMs (nTiO2 and nCeO2). Our results show adverse effects on life-history parameters of D. magna exposed to the selected nanomaterials within the range of reported environmental concentrations. We argue that conforming to OECD test guidelines and accompanying guidance for nanomaterials is feasible when performing D. magna reproduction tests and can minimize unnecessary duplication of similar experiments, even when extensions to the standardized test setup are added.

Correlation analysis of single- and multigenerational endpoints in Daphnia magna toxicity tests: A case-study using TiO2 nanoparticles.

Multigenerational toxicity tests provide more sensitive measures of population-level effects than conventional single-generation tests. Particularly for stressors which exhibit slow uptake rates (e.g. nanomaterials), multigenerational tests may also provide a more realistic representation of natural exposure scenarios. To date, the inherently high costs and labor intensity have however limited the use of multigenerational toxicity tests and thereby their incorporation in environmental risk assessment. The aim of the present study was therefore to determine to what extent short(er) term endpoints which are conventionally measured in Daphnia magna toxicity tests hold predictive capacity towards reproduction measured over longer timescales, including multiple generations. To assess this, a case-study was performed in which effects of TiO2 nanoparticles (0, 0.02, 0.2, 2 and 5 mg L-1) on D. magna life-history traits were assessed over five generations. Additionally, it was determined whether offspring derived from exposed parents exhibited sustained adverse effects when rearing them in clean (non-exposed) media after each generation of exposure. The present study showed that although various life-history traits correlate with the total reproductive output in the same- and subsequent generation under non-exposed conditions, these correlations were decoupled in presence of exposure to nTiO2. In addition, it was found that nTiO2 can induce adverse effects on population relevant endpoints at concentrations 1-2 orders of magnitude lower than previously found (i.e. 0.02 mg L-1), and close to the range of concentrations occurring in natural freshwater ecosystems.

Microbiota-dependent TLR2 signaling reduces silver nanoparticle toxicity to zebrafish larvae.

Many host-microbiota interactions depend on the recognition of microbial constituents by toll-like receptors of the host. The impacts of these interactions on host health can shape the hosts response to environmental pollutants such as nanomaterials. Here, we assess the role of toll-like receptor 2 (TLR2) signaling in the protective effects of colonizing microbiota against silver nanoparticle (nAg) toxicity to zebrafish larvae. Zebrafish larvae were exposed to nAg for two days, from 3 to 5 days post-fertilization. Using an il1ß-reporter line, we first characterized the accumulation and particle-specific inflammatory effects of nAg in the total body and intestinal tissues of the larvae. This showed that silver gradually accumulated in both the total body and intestinal tissues, yet specifically caused particle-specific inflammation on the skin of larvae. Subsequently, we assessed the effects of microbiota-dependent TLR2 signaling on nAg toxicity. This was done by comparing the sensitivity of loss-of-function zebrafish mutants for TLR2, and each of the TLR2-adaptor proteins MyD88 and TIRAP (Mal), under germ-free and microbially-colonized conditions. Irrespective of their genotype, microbially-colonized larvae were less sensitive to nAg than their germ-free siblings, supporting the previously identified protective effect of microbiota against nAg toxicity. Under germ-free conditions, tlr2, myd88 and tirap mutants were equally sensitive to nAg as their wildtype siblings. However, when colonized by microbiota, tlr2 and tirap mutants were more sensitive to nAg than their wildtype siblings. The sensitivity of microbially-colonized myd88 mutants did not differ significantly from that of wildtype siblings. These results indicate that the protective effect of colonizing microbiota against nAg-toxicity to zebrafish larvae involves TIRAP-dependent TLR2 signaling. Overall, this supports the conclusion that host-microbiota interactions affect nanomaterial toxicity to zebrafish larvae.

Trophic transfer of Cu nanoparticles in a simulated aquatic food chain.

The goal of the current study was to quantify the trophic transfer of copper nanoparticles (CuNPs) in a food chain consisting of the microalga Pseudokirchneriella subcapitata as the representative of primary producer, the grazer Daphnia magna, and the omnivorous mysid Limnomysis benedeni. To quantify the size and number concentration of CuNPs in the biota, tissue extraction with tetramethylammonium hydroxide (TMAH) was performed and quantification was done by single particle inductively coupled plasma mass spectrometry (sp-ICP-MS). The bioconcentration factor (BCF) of the test species for CuNPs varied between 102 - 103 L/kg dry weight when expressing the internal concentration on a mass basis, which was lower than BCF values reported for Cu2+ (103 - 104 L/kg dry weight). The particle size of CuNPs determined by sp-ICP-MS ranged from 22 to 40 nm in the species. No significant changes in the particle size were measured throughout the food chain. Moreover, the measured number of CuNPs in each trophic level was in the order of 1013 particles/kg wet weight. The calculated trophic transfer factor (mass concentration basis) was > 1. This indicates biomagnification of particulate Cu from P. subcapitata to L. benedeni. It was also found that the uptake of particulate Cu (based on the particle number concentration) was mainly from the dietary route rather than from direct aqueous exposure. Furthermore, dietary exposure to CuNPs had a significant effect on the feeding rate of mysid during their transfer from daphnia to mysid and from alga through daphnia to mysid. This work emphasizes the importance of tracing the particulate fraction of metal-based engineered nanoparticles when studying their uptake and trophic transfer.

Prediction of the Joint Toxicity of Multiple Engineered Nanoparticles: The Integration of Classic Mixture Models and In Silico Methods.

An approach to solve the emerging need of prediction of the toxicity of mixtures of engineered nanoparticles (ENPs) is presented. The integration of classic approaches to mixture toxicity assessment and computational toxicology approaches is proposed to be a smart strategy for forecasting the toxicity of a mixture of ENPs.

Trophic Transfer and Toxicity of (Mixtures of) Ag and TiO2 Nanoparticles in the Lettuce-Terrestrial Snail Food Chain.

The increasing application of biosolids and agrochemicals containing silver nanoparticles (AgNPs) and titanium dioxide nanoparticles (TiO2NPs) results in their inevitable accumulation in soil, with unknown implications along terrestrial food chains. Here, the trophic transfer of single NPs and a mixture of AgNPs and TiO2NPs from lettuce to snails and their associated impacts on snails were investigated. Both AgNPs and TiO2NPs were transferred from lettuce to snails with trophic transfer factors (defined as the ratio of the Ag/Ti concentration in snail tissues to the Ag/Ti concentration in lettuce leaves) of 0.2-1.1 for Ag and 3.8-47 for Ti. Moreover, the majority of Ag captured by snails in the AgNP-containing treatments was excreted via feces, whereas more than 70% of Ti was distributed in the digestive gland of snails in the TiO2NP-containing treatments. Additionally, AgNP-containing treatments significantly inhibited the activity of snails, while TiO2NP-containing treatments significantly reduced feces excretion of snails. Furthermore, the concurrent application of AgNPs and TiO2NPs did not affect the biomagnification and distribution patterns of Ag and Ti in snails, whereas their co-existence exhibited more severe inhibition of the growth and activity of snails than in the case of applying AgNPs or TiO2NPs alone. This highlights the possibility of nanoparticle transfer to organisms of higher trophic levels via food chains and the associated risks to ecosystem health.

Evaluating chemical similarity as a measure to identify potential substances of very high concern.

Due to the large amount of chemical substances on the market, fast and reproducible screening is essential to prioritize chemicals for further evaluation according to highest concern. We here evaluate the performance of structural similarity models that are developed to identify potential substances of very high concern (SVHC) based on structural similarity to known SVHCs. These models were developed following a systematic analysis of the performance of 112 different similarity measures for varying SVHC-subgroups. The final models consist of the best combinations of fingerprint, similarity coefficient and similarity threshold, and suggested a high predictive performance (≥80%) on an internal dataset consisting of SVHC and non-SVHC substances. However, the application performance on an external dataset was not evaluated. Here, we evaluated the application performance of the developed similarity models with a 'pseudo-external assessment' on a set of substances (n = 60-100 for the varying SVHC-subgroups) that were putatively assessed as SVHC or non-SVHC based upon consensus scoring using expert elicitations (n = 30 experts). Expert scores were direct evaluations based on structural similarity to the most similar SVHCs according to the similarity models, and did not consider an extensive evaluation of available data. The use of expert opinions is particularly suitable as this is exactly the intended purpose of the chemical similarity models: a quick, reproducible and automated screening tool that mimics the expert judgement that is frequently applied in various screening applications. In addition, model predictions were analyzed via qualitative approaches and discussed via specific examples, to identify the model's strengths and limitations. The results indicate a good statistical performance for carcinogenic, mutagenic or reprotoxic (CMR) and endocrine disrupting (ED) substances, whereas a moderate performance was observed for (very) persistent, (very) bioaccumulative and toxic (PBT/vPvB) substances when compared to expert opinions. For the PBT/vPvB model, particularly false positive substances were identified, indicating the necessity of outcome interpretation. The developed similarity models are made available as a freely-accessible online tool. In general, the structural similarity models showed great potential for screening and prioritization purposes. The models proved to be effective in identifying groups of substances of potential concern, and could be used to identify follow-up directions for substances of potential concern.

Probing nano-QSAR to assess the interactions between carbon nanoparticles and a SARS-CoV-2 RNA fragment.

The coronavirus disease-19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is rampant in the world and is a serious threat to global health. The SARS-CoV-2 RNA has been detected in various environmental media, which speeds up the pace of the virus becoming a global biological pollutant. Because many engineered nanomaterials (ENMs) are capable of inducing anti-microbial activity, ENMs provide excellent solutions to overcome the virus pandemic, for instance by application as protective coatings, biosensors, or nano-agents. To tackle some mechanistic issues related to the impact of ENMs on SARS-CoV-2, we investigated the molecular interactions between carbon nanoparticles (CNPs) and a SARS-CoV-2 RNA fragment (i.e., a model molecule of frameshift stimulation element from the SARS-CoV-2 RNA genome) using molecular mechanics simulations. The interaction affinity between the CNPs and the SARS-CoV-2 RNA fragment increased in the order of fullerenes < graphenes < carbon nanotubes. Furthermore, we developed quantitative structure-activity relationship (QSAR) models to describe the interactions of 17 different types of CNPs from three dimensions with the SARS-CoV-2 RNA fragment. The QSAR models on the interaction energies of CNPs with the SARS-CoV-2 RNA fragment show high goodness-of-fit and robustness. Molecular weight, surface area, and the sum of degrees of every carbon atom were found to be the primary structural descriptors of CNPs determining the interactions. Our research not only offers a theoretical insight into the adsorption/separation and inactivation of SARS-CoV-2, but also allows to design novel ENMs which act efficiently on the genetic material RNA of SARS-CoV-2. This contributes to minimizing the challenge of time-consuming and labor-intensive virus experiments under high risk of infection, whilst meeting our precautionary demand for options to handle any new versions of the coronavirus that might emerge in the future.

The stochastic association of nanoparticles with algae at the cellular level: Effects of NOM, particle size and particle shape.

Association of nanoparticles (NPs) with algae likely plays a critical role in their transfer in aquatic food chains. Although our understanding of the ecotoxicity and fate of NPs in the environment is increasing, it is still unclear how the physicochemical properties of NPs influence their interaction with algae at cellular levels and how this is reflected at a population level. This is due to the limitation in the existing analytical techniques to quantify the association of NPs with cells. To fill this data gap, we applied the novel technique of single-cell inductively coupled plasma mass spectrometry to quantify the cellular association of gold (Au)-NPs with algal cells (Pseudokirchneriella subcapitata) as a function of particle size, shape (spherical 10 nm, spherical 60 nm, spherical 100 nm, rod-shaped 10 × 40 nm, and rod-shaped 50 × 100 nm), and surface chemistry [citrate and natural organic matter (NOM) coating] on a cell-by-cell basis. The association of Au-NPs with algal cells was found to be a random probability following a so-called stochastic process; after 72 h of exposure, less than 45% of the cell population accumulated NPs on their surface. The number of Au-NPs per cell was found to be heterogeneously distributed as some cells were associated with a significantly higher number (e.g. up to 600 spherical 10 nm particles per cell) of Au-NPs than other cells present in the medium. The presence of NOM on the surface of the particles decreased the percentage of cells containing NPs except for the spherical 60 nm Au-NPs. We conclude that some algae within a population can accumulate NPs on their surface and this accumulation is influenced by the size, shape, and surface chemistry of NPs. It is important to understand how NPs may enter aquatic food chains to assess the possible risk.

Remediation of heavy metal contaminated soil by biodegradable chelator-induced washing: Efficiencies and mechanisms.

Biodegradable chelators (BCs) are promising substitutes for conventional washing agents in the remediation of heavy metal contaminated soil with strong complexing ability and less cost. However, great challenges for the applications of BC-assisted washing still exist, such as the assessment of the factor affecting the efficiency of metal removal and the unclear of the metal removal mechanism. Batch washing was therefore explored to evaluate the potential for four BCs for removing Cd, Pb, and Zn from polluted soils. The soil spectroscopic characteristics before and after washing were also investigated. The results demonstrated that iminodisuccinic acid (ISA) and glutamate-N, N-diacetic acid (GLDA) were an appealing alternative to commonly used non-biodegradable ethylenediaminetetraacetic acid, but glucomonocarbonic acid (GCA) and polyaspartic acid (PASP) were less efficient. Optimal parameters of BCs were determined to be a concentration of 50 mmol L-1, a pH of 5.0, a contact time of 120 min, and a solid/liquid ratio of 1:5, considering metal removal efficiencies and the suitable cost. A single removal washing could be up to 52.39% of Cd, 71.79% of Pb, and 34.13% of Zn from mine soil, and 98.28% of Cd, 91.10% of Pb, and 90.91% of Zn from polluted farmland soil. After washing, the intensity of heavy metal binding to soil colloids increased while the metal mobility reduced because of weakly bound fractions removed by BCs. The BCs-induced soil washing revealed that the possible mechanisms of metal removal included the acid dissolution, ion exchange, and surface complexation. Our findings highlight the potential application of especially ISA and GLDA as efficient washing agents to remove potentially toxic elements from contaminated soils.