Summary of the main technical and scientific activities performed and their achievements:
1. Machine Learning for Predictive Modeling in Nanoparticle Synthesis (Furxhi et al., 2024)
Activities: Leveraged machine learning to predict the properties of nanoparticles using synthesis data collected from literature. Data extraction covered synthesis parameters, antibacterial efficiency, and toxicological profiles. Achievements: Regression models accurately predicted core size and antibacterial efficiency, identifying key factors (e.g. synthesis duration, scale, capping agents) through Shapley values. This model advances Safe-by-Design (SbD) approaches by optimizing synthesis processes based on machine learning insights. This paper is a result of an exercise with ISSMC colleagues for data collection and harmonization. This methodology can be applied in any case of different AdMa.
2. Risk Assessment for Silver Nanoparticles in Textiles (Koivisto et al., 2024)
Activities: Conducted risk assessments for dermal and inadvertent (peri-)oral exposure to AgNPs from coated textiles using a mass balance model, evaluating release under conditions like artificial sweat and mechanical stress. Achievements: Identified that AgNP-containing gloves pose a high risk if followed by finger mouthing, emphasizing the need for realistic release test settings and good hygiene practices. The study establishes a foundation for safer design. This methodology can be applied in any case of different AdMa.
3. Development of Safe and Sustainable by Design (SSbD) Roadmap for Silver-Based Textiles (Furxhi et al., 2024)
Activities: Developed a roadmap with quantitative metrics (PCFs, KDFs, KPIs) for implementing SSbD in silver-based antimicrobial textile coatings, aligned with the ASINA project. Achievements: Established guidelines and a decision support tool for SSbD, addressing safety, functionality, and environmental impact. This roadmap promotes a holistic lifecycle approach to SSbD, guiding future nanotechnology projects within a broader EU sustainability context.
4. SSbD Roadmap for TiO2-based Depolluting Surfaces (Furxhi et al., 2024)
Activities: Provided an SSbD framework for TiO2 photocatalytic applications, focusing on depolluting surfaces. Methods included spray-finishing techniques for TiO2 formulations and integrating Key Performance Indicators into the roadmap. Achievements: Demonstrated a quantitative SSbD methodology for environmental applications, creating a benchmark for nanomaterials in various sectors. This contributes to standardized SSbD practices, furthering EU’s Green Deal goals for safer and sustainable innovation.
5. Machine Learning for TiO2 Genotoxicity Prediction (Furxhi et al., 2024)
Activities: Utilized machine learning to predict TiO2-induced DNA damage from comet assay datasets. Models incorporated physicochemical properties and experimental parameters (e.g. concentration, cold lysis conditions). Achievements: Extra Trees and XGB regressors achieved high predictive accuracy, with concentration emerging as a critical factor. This study underscores ML’s role in nanotoxicology, highlighting key predictors of genotoxicity and contributing a FAIR dataset for further research.
6. Bayesian Networks for AgNPs Hazard Prediction in Soil (Furxhi et al., 2024)
Activities: Developed a Bayesian Network model to predict the hazard potential of AdMa in soil environments, incorporating factors like particle characteristics, exposure medium, and ecological data. Achievements: Achieved 82% predictive accuracy for chronic NOEC, identifying significant predictors of ecotoxicological risk. This model aids rapid hazard assessment and reduces dependence on costly lab experiments, aligning with the EU Green Deal’s sustainable practices.
7. Impact of Granulation Parameters on TiO2 Properties (Vespignani, 2024)
Activities: Investigated high-shear wet granulation (HSWG) for TiO2, examining the influence of granulation parameters (e.g. liquid-to-solid ratio, granulation time) on granulate size, porosity, and mechanical strength. Achievements: Identified the liquid-to-solid ratio and granulation time as key factors influencing granulate properties, enabling process optimization. The findings provide actionable insights for tailoring TiO2 granulation processes for specific applications, promoting material quality and stability.
8. High-Shear Granulation for Clay-Based Powders (Vespignani, 2024)
Activities: Assessed HSWG parameters (e.g. liquid addition rate, binder use) on hydrophilic and hydrophobic clays, focusing on granule size distribution and mechanical attributes. Achievements: Revealed the importance of the liquid-to-solid ratio for hydrophilic clays, with granulation time and impeller speed influencing hydrophobic clays. This knowledge supports tailored process design for different clay types, enhancing material performance and applicability.
Summary of Achievements
Collectively, these studies exemplify the integration of machine learning, Bayesian networks, and experimental optimization to advance the SSbD framework for AdMa. Key predictors and process parameters were identified across applications, from nanotoxicology to material synthesis, aiding safer and sustainable product designs. The methods developed, particularly the decision support tools and data-driven approaches, contribute valuable frameworks for reproducible, transparent, and predictive models in nanosafety and advanced material science.
