Felice C. Simeone's research while affiliated with Institute of Science and Technology for Ceramics and other places

In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation. This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or time- and environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation, representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing.

Fitting theoretical models to experimental data for dose-response screenings of nanoparticles yields values of several hazard metrics that can support risk management. In this paper, we describe a Bayesian approach to the analysis of dose-response data for nanoparticles that takes into account multiple sources of uncertainty. Specifically, we develop a Bayesian model for the analysis of data for the cytotoxicity of ZnO nanoparticles that follow the log-logistic equation. This model reproduces the unequal variance across doses observed in the experimental data, incorporates information about the sensitivity of the cytotoxicity assay used (i.e. resazurin), and complements experimental data with historical information about the system. The model determines probability distributions for multiple values of toxicity potency (EC50), and exponential decay (the slope s); these distributions provide a direct measure of uncertainty in terms of probabilistic credibility intervals. By substituting these distributions in the log-logistic equation, we determine upper and lower limits of the benchmark dose (BMD), corresponding to upper and lower limits of credibility intervals with 95% probability given the experimental data, multiple sources of uncertainty, and historical information. In view of a reduction of costs and time of dose-response screenings, we use the Bayesian model for the cytotoxicity of ZnO nanoparticles to identify the experimental design that uses the minimum number of data while reducing uncertainty in the estimation of both fitting parameters and BMD.

An effective, eco-friendly and easily scalable nanosilver-based technology offers affordable and broad-spectrum antimicrobial solutions against SARS-CoV-2 and Escherichia coli .

Due to their sizes, nanoparticles can penetrate into biological systems easily; anticipating their harmful interactions at the cellular level makes it possible to safeguard workers, consumers, and the environment effectively. Decades of research have identified key properties of nanomaterials that induce adverse responses, and this evidence suggests that the toxicity of a nanoparticle can be inferred from its abiotic behavior. Experimental physical-chemical characterization of nanoparticles, however, is complex and time consuming, and the absence of any standardized method has generated controversial results. This paper shows how known adverse modes of action of nanoparticles arise from fundamental physical-chemical parameters that don’t need any experimental quantification. We show that cytotoxicity of metal oxides in different types of in-vitro systems, which include E. coli, rat alveolar macrophages, human bronchial epithelial cells, Daphnia magna, and Aliivibrio fisheri, can be foreseen on the basis of the values of oxidation number (Z) and ionic potential (IP) of the cation, and surface reducibility (SR), and redox reactivity (RR) of the oxide. Importantly, the values of these fundamental physical-chemical parameters can be easily deduced from the chemical formula of the nanoparticle with the help of a periodic table. Combining these parameters in a naïve Bayes classifier, a robust probabilistic model that can be run on a pocket calculator, makes it possible to determine the most probable level of toxicity of a nanoparticle given its composition. Results indicate that the probability that nano-oxides exhibit very high cytotoxicity (EC50 < 10-3 mol∙L-1) decreases with increasing oxidation number Z of the cation; high values of Z, however, may become unstable and activate adverse redox processes; in contrast, stable, redox-inert reducible oxides tend to be, probabilistically, less toxic than oxidizable ones.

Building safe production places can protect workers more effectively than managing risks in a plant that has been conceived without taking into account safety upfront. In this paper, we describe an approach to assessing potential risks already at the stage of design of production processes of nano-enabled products. In a chemical plant, risk results from the combination of hazard of the chemicals and exposure of workers to them. Toxicological profiles of novel nanomaterials, however, are generally unknown; in addition, the impossibility of measuring exposure in a plant that does not exist yet exacerbates the challenge of designing safe production processes. This paper describes a simple method to formulate realistic hypotheses about the toxicity of untested nanoparticles and derives a simplified model of exposure that enables non-specialists (e.g., managers, engineers) to analyze potential risks in projects of future production plants. As an example of analysis of risk in the absence of experimental data, the paper describes the procedure to generate maps of risks of two envisaged production chains of antibacterial textiles: 1) sonochemical synthesis and deposition of bactericidal nanoparticles, and 2) spray deposition of suspension of bactericidal nanoparticles.

Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.

This manuscript describes a simple and rapid method for fabricating free-standing structures composed primarily (>94% w/w, and 55–80 at%) of noble metals (e.g., gold, silver, platinum, etc.) and having physical morphologies that resemble paper, thread, or fabric. In this method, templates (i.e., pieces of paper, or cotton fabric) are loaded with aqueous solutions of salts of noble metals, and then the cellulosic component is burned off in a furnace held at high temperatures (i.e., from 550 °C to 800 °C, depending on the procedure, in air). Even though the environment in a furnace is ostensibly oxidizing (e.g., hot air), the metal ions are reduced to elemental metal and form paper-templated or fabric-templated structures that have morphologies similar to that of the material from which they were derived (i.e., paper or fabric). Paper-templated structures are fibrous, permeable to gases and liquides, electrically conductive, and in some cases (e.g., paper-templated gold and paper-templated platinum structures), their surfaces are electroactive. The surface areas of paper-templated structures are more than 20 times higher than their projected areas. Paper-templated structures thus have properties that make them potentially useful in catalysis, sensing, and electroanalysis.

This paper reports rates of charge tunneling across self-assembled monolayers (SAMs) of compounds containing oligophenyl groups, supported on gold and silver, using Ga2O3/EGaIn as the top electrode. It compares the attenuation constant, β, and the pre-exponential parameter, J0, of the simplified Simmons equation, across oligophenyl groups (R = Phn; n = 1, 2, 3), with three different anchoring groups (thiol, HSR; methanethiol, HSCH2R; and acetylene, HC≡CR) that attach R to the template-stripped gold or silver substrates. The results demonstrate that the structure of the molecular linker between the anchoring group (-S- or -C≡C-) and the oligophenyl moiety significantly influences rates of charge transport. SAMs of SPhn and C≡CPhn on gold show similar values of β and log|J0| (β = 0.28 ± 0.03 Å-1 and log|J0| = 2.7 ± 0.1 for Au/SPhn; β = 0.30 ± 0.02 Å-1 and log|J0| = 3.0 ± 0.1 for Au/C≡CPhn). The introduction of a single intervening methylene (CH2) group between the anchoring sulfur atom and the aromatic units generates SAMs of SCH2Phn, and increases β to ~0.66 ± 0.06 Å-1 on both gold and silver substrates. (For n-alkanethiolates on gold the corresponding values are β = 0.76 ± 0.03 Å-1 and log|J0| = 4.2 ± 0.2). Density Functional Theory (DFT) calculations indicate that the highest occupied molecular orbitals (HOMOs) of both SPhn and C≡CPhn extend beyond the anchoring group and onto the phenyl rings; SAMs composed of these two groups of molecules result in indistinguishable rates of charge transport. The introduction of the CH2 group, to generate SCH2Phn, disrupts the delocalization of orbitals, localizes the HOMO on the anchoring sulfur atom, and results in the experimentally observed increase in β to a value closer to that of a SAM of n-alkylthiolate molecules.