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.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies.

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

Trophic Transfer and Toxic Potency of Rare Earth Elements along a Terrestrial Plant-Herbivore Food Chain.

Extensive rare earth element (REE) mining activities have caused REE contamination of ambient agricultural soils, posing threats to associated food webs. Here, a simulated lettuce-snail food chain was conducted to evaluate the trophic transfer characteristics and the consequent effects of REEs on consumers. After 50-day exposure to soil, lettuce roots dose-dependently accumulated 9.4-76 mg kg-1 REEs and translocated 3.7-20 mg kg-1 REEs to shoots. Snails feeding on REE-contaminated shoots accumulated 3.0-6.7 mg kg-1 REEs with trophic transfer factors of 0.20-0.98, indicating trophic dilution in the lettuce-snail system. REE profiles in lettuce and snails indicated light REE (LREE) enrichment only in snails and the varied REE profiles along the food chain. This was corroborated by toxicokinetics. Estimated uptake (Ku) and elimination (Ke) parameters were 0.010-2.9 kgshoot kgsnail-1 day-1 and 0.010-1.8 day-1, respectively, with higher Ku values for LREE and HREE. The relatively high Ke, compared to Ku, indicating a fast REE elimination, supports the trophic dilution. Dietary exposure to REEs dose-dependently affected gut microbiota and metabolites in snails. These effects are mainly related to oxidative damage and energy expenditure, which are further substantiated by targeted analysis. Our study provides essential information about REE bioaccumulation characteristics and its associated risks to terrestrial food chains near REE mining areas.

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.

The role of trust in the use of artificial intelligence for chemical risk assessment.

Risk assessment of chemicals is a time-consuming process and needs to be optimized to ensure all chemicals are timely evaluated and regulated. This transition could be stimulated by valuable applications of in silico Artificial Intelligence (AI)/Machine Learning (ML) models. However, implementation of AI/ML models in risk assessment is lagging behind. Most AI/ML models are considered 'black boxes' that lack mechanistical explainability, causing risk assessors to have insufficient trust in their predictions. Here, we explore 'trust' as an essential factor towards regulatory acceptance of AI/ML models. We provide an overview of the elements of trust, including technical and beyond-technical aspects, and highlight elements that are considered most important to build trust by risk assessors. The results provide recommendations for risk assessors and computational modelers for future development of AI/ML models, including: 1) Keep models simple and interpretable; 2) Offer transparency in the data and data curation; 3) Clearly define and communicate the scope/intended purpose; 4) Define adoption criteria; 5) Make models accessible and user-friendly; 6) Demonstrate the added value in practical settings; and 7) Engage in interdisciplinary settings. These recommendations should ideally be acknowledged in future developments to stimulate trust and acceptance of AI/ML models for regulatory purposes.

Molecular characterization and transcriptional response of Lactuca sativa seedlings to co-exposure to graphene nanoplatelets and titanium dioxide nanoparticles.

The widespread use of nanomaterials in agriculture may introduce multiple engineered nanoparticles (ENPs) into the environment, posing a combined risk to crops. However, the precise molecular mechanisms explaining how plant tissues respond to mixtures of individual ENPs remain unclear, despite indications that their combined toxicity differs from the summed toxicity of the individual ENPs. Here, we used a variety of methods including physicochemical, biochemical, and transcriptional analyses to examine the combined effects of graphene nanoplatelets (GNPs) and titanium dioxide nanoparticles (TiO2 NPs) on hydroponically exposed lettuce (Lactuca sativa) seedlings. Results indicated that the presence of GNPs facilitated the accumulation of Ti as TiO2 NPs in the seedling roots. Combined exposure to GNPs and TiO2 NPs caused less severe oxidative damage in the roots compared to individual exposures. Yet, GNPs and TiO2 NPs alone and in combination did not cause oxidative damage in the shoots. RNA sequencing data showed that the mixture of GNPs and TiO2 NPs led to a higher number of differentially expressed genes (DEGs) in the seedlings compared to exposure to the individual ENPs. Moreover, the majority of the DEGs encoding superoxide dismutase displayed heightened expression levels in the seedlings exposed to the combination of GNPs and TiO2 NPs. The level of gene ontology (GO) enrichment in the seedlings exposed to the mixture of GNPs and TiO2 NPs was found to be greater than the level of GO enrichment observed after exposure to isolated GNPs or TiO2 NPs. Furthermore, the signaling pathways, specifically the "MAPK signaling pathway-plant" and "phenylpropanoid biosynthesis," exhibited a close association with oxidative stress. This study has provided valuable insights into the molecular mechanisms underlying plant resistance against multiple ENPs.

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.

Informing the public about chemical mixtures in the local environment: Currently applied indicators in the Netherlands and ways forward.

The current use of chemicals puts pressure on human and ecological health. Based on the Aarhus Convention, citizens have the right to have access to information on substances in their local environment. Providing this information is a major challenge, especially considering complex mixtures, as the current substance-by-substance risk assessment may not adequately address the risk of co-exposure to multiple substances. Here, we provide an overview of the currently available indicators in the Netherlands to explore current scientific possibilities to indicate the impacts of complex chemical mixtures in the environment on human health and ecology at the local scale. This is limited to impact estimates on freshwater species for 701 substances, impact estimates of four metals on soil organisms, and impacts on human health for particulate matter (PM10) and nitrogen dioxide (NO2) in air. The main limiting factors in developing and expanding these indicators to cover more compartments and substances are the availability of emission and concentration data of substances and dose-response relationships at the population (human health) or community (ecology) level. As ways forward, we propose; 1) developing cumulative assessment groups (CAGs) for substances on the European Pollutant Transfer and Release Register and Water Framework Directive substance lists, to enable the development of mixture indicators based on mixture risk assessment and concentration addition principles; 2) to gain insight into local mixtures by also applying these CAGs to emission data, which is available for soil and air for more substances than concentrations data; 3) the application of analytical non-target screening methods as well as effect-based methods for whole-mixture assessment.

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.

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks.

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure-activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks─each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.

Coupled Lipidomics and Digital Pathology as an Effective Strategy to Identify Novel Adverse Outcome Pathways in Eisenia fetida Exposed to MoS2 Nanosheets and Ionic Mo.

Molybdenum disulfide (MoS2) nanosheets are increasingly applied in several fields, but effective and accurate strategies to fully characterize potential risks to soil ecosystems are lacking. We introduce a coelomocyte-based in vivo exposure strategy to identify novel adverse outcome pathways (AOPs) and molecular endpoints from nontransformed (NTMoS2) and ultraviolet-transformed (UTMoS2) MoS2 nanosheets (10 and 100 mg Mo/L) on the earthworm Eisenia fetida using nontargeted lipidomics integrated with transcriptomics. Machine learning-based digital pathology analysis coupled with phenotypic monitoring was further used to establish the correlation between lipid profiling and whole organism effects. As an ionic control, Na2MoO4 exposure significantly reduced (61.2-79.5%) the cellular contents of membrane-associated lipids (glycerophospholipids) in earthworm coelomocytes. Downregulation of the unsaturated fatty acid synthesis pathway and leakage of lactate dehydrogenase (LDH) verified the Na2MoO4-induced membrane stress. Compared to conventional molybdate, NTMoS2 inhibited genes related to transmembrane transport and caused the differential upregulation of phospholipid content. Unlike NTMoS2, UTMoS2 specifically upregulated the glyceride metabolism (10.3-179%) and lipid peroxidation degree (50.4-69.4%). Consequently, lipolytic pathways were activated to compensate for the potential energy deprivation. With pathology image quantification, we report that UTMoS2 caused more severe epithelial damage and intestinal steatosis than NTMoS2, which is attributed to the edge effect and higher Mo release upon UV irradiation. Our results reveal differential AOPs involving soil sentinel organisms exposed to different Mo forms, demonstrating the potential of liposome analysis to identify novel AOPs and furthermore accurate soil risk assessment strategies for emerging contaminants.

Earthworm Coelomocyte Internalization of MoS2 Nanosheets: Multiplexed Imaging, Molecular Profiling, and Computational Modeling.

Fully understanding the cellular uptake and intracellular localization of MoS2 nanosheets (NSMoS2) is a prerequisite for their safe applications. Here, we characterized the uptake profile of NSMoS2 by functional coelomocytes of the earthworm Eisenia fetida. Considering that vacancy engineering is widely applied to enhance the NSMoS2 performance, we assessed the potential role of such atomic vacancies in regulating cellular uptake processes. Coelomocyte internalization and lysosomal accumulation of NSMoS2 were tracked by fluorescent labeling imaging. Cellular uptake inhibitors, proteomics, and transcriptomics helped to mechanistically distinguish vacancy-mediated endocytosis pathways. Specifically, Mo ions activated transmembrane transporter and ion-binding pathways, entering the coelomocyte through assisted diffusion. Unlike molybdate, pristine NSMoS2 (P-NSMoS2) induced protein polymerization and upregulated gene expression related to actin filament binding, which phenotypically initiated actin-mediated endocytosis. Conversely, vacancy-rich NSMoS2 (V-NSMoS2) were internalized by coelomocytes through a vesicle-mediated and energy-dependent pathway. Mechanistically, atomic vacancies inhibited mitochondrial transport gene expression and likely induced membrane stress, significantly enhancing endocytosis (20.3%, p < 0.001). Molecular dynamics modeling revealed structural and conformational damage of cytoskeletal protein caused by P-NSMoS2, as well as the rapid response of transport protein to V-NSMoS2. These findings demonstrate that earthworm functional coelomocytes can accumulate NSMoS2 and directly mediate cytotoxicity and that atomic vacancies can alter the endocytic pathway and enhance cellular uptake by reprogramming protein response and gene expression patterns. This study provides an important mechanistic understanding of the ecological risks of NSMoS2.

Evaluating the spatial and temporal distribution of emerging contaminants in the Pearl River Basin for regulating purposes.

Little information is available on how the types, concentrations, and distribution of chemicals have evolved over the years. The objective of the present study is therefore to review the spatial and temporal distribution profile of emerging contaminants with limited toxicology data in the pearl river basin over the years to build up the emerging contaminants database in this region for risk assessment and regulatory purposes. The result revealed that seven groups of emerging contaminants were abundant in this region, and many emerging contaminants had been detected at much higher concentrations before 2011. Specifically, antibiotics, phenolic compounds, and acidic pharmaceuticals were the most abundant emerging contaminants detected in the aquatic compartment, while phenolic compounds were of the most profound concern in soil. Flame retardants and plastics were the most frequently studied chemicals in organisms. The abundance of the field concentrations and frequencies varied considerably over the years, and currently available data can hardly be used for regulation purposes. It is suggested that watershed management should establish a regular monitoring scheme and comprehensive database to monitor the distribution of emerging contaminants considering the highly condensed population in this region. The priority monitoring list should be formed in consideration of historical abundance, potential toxic effects of emerging contaminants as well as the distribution of heavily polluting industries in the region.

Graphene oxide disruption of homeostasis and regeneration processes in freshwater planarian Dugesia japonica via intracellular redox deviation and apoptosis.

The aquatic system is a major sink for engineered nanomaterials released into the environment. Here, we assessed the toxicity of graphene oxide (GO) using the freshwater planarian Dugesia japonica, an invertebrate model that has been widely used for studying the effects of toxins on tissue regeneration and neuronal development. GO not only impaired the growth of normal (homeostatic) worms, but also inhibited the regeneration processes of regenerating (amputated) worms, with LC10 values of 9.86 mg/L and 9.32 mg/L for the 48-h acute toxicity test, respectively. High concentration (200 mg/L) of GO killed all the worms after 3 (regenerating) or 4 (homeostasis) days of exposure. Whole-mount in situ hybridization (WISH) and immunofluorescence analyses suggest GO impaired stem cell proliferation and differentiation, and subsequently caused cell apoptosis and oxidative DNA damage during planarian regeneration. Mechanistic analysis suggests that GO disturbed the antioxidative system (enzymatic and non-enzymatic) and energy metabolism in the planarian at both molecular and genetic levels, thus causing reactive oxygen species (ROS) over accumulation and oxidative damage, including oxidative DNA damage, loss of mitochondrial membrane integrity, lack of energy supply for cell differentiation and proliferation leading to retardance of neuron regeneration. The intrinsic oxidative potential of GO contributes to the GO-induced toxicity in planarians. These data suggest that GO in aquatic systems can cause oxidative stress and neurotoxicity in planarians. Overall, regenerated tissues are more sensitive to GO toxicity than homeostatic ones, suggesting that careful handling and appropriate decisions are needed in the application of GO to achieve healing and tissue regeneration.

Enhanced Production of Active Photosynthetic and Biochemical Molecules in Silybum marianum L. Using Biotic and Abiotic Elicitors in Hydroponic Culture.

Elicitors are stressors that activate secondary pathways that lead to the increased production of bioactive molecules in plants. Different elicitors including the fungus Aspergillus niger (0.2 g/L), methyl jasmonate (MeJA, 100 µM/L), and silver nanoparticles (1 µg/L) were added, individually and in combination, in a hydroponic medium. The application of these elicitors in hydroponic culture significantly increased the concentration of photosynthetic pigments and total phenolic contents. The treatment with MeJA (methyl jasmonate) (100 µM/L) and the co-treatment of MeJA and AgNPs (silver nanoparticles) (100 µM/L + 1 µg/L) exhibited the highest chlorophyll a (29 µg g-1 FW) and chlorophyll b (33.6 µg g-1 FW) contents, respectively. The elicitor MeJA (100 µM/L) gave a substantial rise in chlorophyll a and b and total chlorophyll contents. Likewise, a significant rise in carotenoid contents (9 µg/g FW) was also observed when subjected to meJA (100 µM/L). For the phenolic content, the treatment with meJA (100 µM/L) proved to be very effective. Nevertheless, the highest production (431 µg/g FW) was observed when treated with AgNPs (1 µg/L). The treatments with various elicitors in this study had a significant effect on flavonoid and lignin content. The highest concentration of flavonoids and lignin was observed when MeJA (100 mM) was used as an elicitor, following a 72-h treatment period. Hence, for different plant metabolites, the treatment with meJA (100 µM/L) and a co-treatment of MeJA and AgNPs (100 µM/L + 1 µg/L) under prolonged exposure times of 120-144 h proved to be the most promising in the accretion of valuable bioactive molecules. The study opens new insights into the use of these elicitors, individually or in combination, by using different concentrations and compositions.

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.

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.

Dissolved Organic Matter Enhanced the Aggregation and Oxidation of Nanoplastics under Simulated Sunlight Irradiation in Water.

Nanoplastics (NPs) have become a new type of pollutant of high concern that is ubiquitous in aqueous environments. However, the transport and transformation of NPs in natural waters are not yet fully understood. In this study, the aggregation and photooxidation of NPs were assessed with nanosized polystyrene (PS) as an example, and the effects of dissolved organic matter (DOM) were investigated with Suwannee River fulvic acid (SRFA) as representative DOM. The results showed that simulated sunlight irradiation exhibited negligible effects on the aggregation of PS, while SRFA enhanced its heteroaggregation through hydrophobic interactions. In SRFA solutions, photooxidation of PS with a particle size of 200 nm was observed, which led to an increase in the O/C ratio on its surface at a rate of (2.20 ± 0.40) × 10-2 h-1. This indicates the promotional effect of SRFA on the oxidation of nanosized PS, which is attributed to the generation of the excited triplet state (3SRFA*), hydroxyl radicals (•OH), and singlet oxygen (1O2). Among these reactive species, 1O2 played a crucial role in the oxidation of PS. The findings in this study are helpful for an in-depth understanding of the environmental behavior of NPs in natural waters.