The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features’ impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.

Industrial spray coating processes are known to produce excellent coatings on large surfaces and are thus often used for in-line production. However, they could be one of the most critical sources of worker exposure to ultrafine particles (UFPs). A monitoring campaign at the Witek s.r.l. (Florence, Italy) was deployed to characterize the release of TiO₂ NPs doped with nitrogen (TiO₂-N) and Ag capped with hydroxyethyl cellulose (AgHEC) during automatic industrial spray-coating of polymethyl methacrylate (PMMA) and polyester. Aerosol particles were characterized inside the spray chamber at near field (NF) and far field (FF) locations using on-line and off-line instruments. Results showed that TiO₂-N suspension produced higher particle number concentrations than AgHEC in the size range 0.3–1 µm (on average 1.9 10² p/cm³ and 2.5 10¹ p/cm³, respectively) after background removing. At FF, especially at worst case scenario (4 nozzles, 800 mL/min flow rate) for TiO₂-N, the spray spikes were correlated with NF, with an observed time lag of 1 minute corresponding to a diffusion speed of 0.1 m/s. The averaged ratio between particles mass concentrations in the NF position and inside the spray chamber was 1.7% and 1.5% for TiO₂-N and for AgHEC suspensions, respectively. The released particles’ number concentration of TiO₂-N in the size particles range 0.3–1 µm was comparable for both PMMA and polyester substrates, about 1.5 and 1.6 10² p/cm³. In the size range 0.01–30 µm, the aerosol number concentration at NF for both suspensions was lower than the nano reference values (NRVs) of 16·10³ p/cm^-3.

The great antimicrobial action of copper oxide nanoparticles (CuO NPs) and their consequent use in many applications in the biomedical field have caused concern about the potentially negative effects of human skin exposure to CuO NPs. However, so far, very few studies have investigated the toxic action of CuO NPs toward humans, with a special focus on the skin penetration route of entry. This study considers for the first time the absorption of topically applied CuO NPs through the human skin, using a Franz static diffusion cell model. We characterised the colloidal behaviour and static dissolution of commercial bare CuO NPs, evaluating both intact and damaged skins. We tested physiological solutions as the receiving phase; as the donor phase, CuO NPs were dispersed in synthetic sweat and exposed to the outer surface of the skin (0.11 mg cm⁻²) for 24 hours. The Cu elemental analysis in the receptor fluid and in the exposed skin allowed us to quantify the copper translocation within the epidermis and dermis. The experimental data showed that the CuO NPs absorption through intact skin was negligible. However, in damaged skin, we observed an increasing permeation of copper. The latter would justify the capacity of CuO NPs to pass the first skin layers and release Cu²⁺ ions into the stratum corneum, reaching the receptor fluid in the in vitro diffusion cell system.

The green and safe polymer HEC confers bio-interactive properties to Ag-Nps that, together with the small size and positive ζ potential, increase their biological reactivity towards zebrafish embryos.