Atomistic-Level Insights into the Role of Mutations in the Engineering of PET Hydrolases: A Systematic Review.

Plastic pollution is a growing global challenge, and traditional plastic waste management methods are proving inadequate in tackling the issue. Enzymatic biodegradation has emerged as a promising alternative or addition to plastic waste management due to its environmentally friendly profile. Polyethylene terephthalate (PET) is among the most widely used polymers in packaging, and recent research has identified several PET-degrading enzymes, such as TfCut2, PETase, and LCC, as promising candidates for biodegradation applications at the industrial level.

Safe and Sustainable by Design MOF Beads for Selective Entrapment and Recovery of Rare Earth Elements.

We report the development of CA-BNMG-1 composite beads-cellulose acetate macrobeads embedded with nanosized copper imidazolate MOFs (BNMG-1) -engineered via nonsolvent-induced phase separation for the selective recovery of rare earth elements (REEs) from complex aqueous environments. This encapsulation strategy ensures uniform MOF dispersion, enhanced mechanical integrity, and minimized Cu(II) leaching (<1%), fulfilling the Safe and Sustainable by Design (SSbD) criteria. The CA matrix not only mitigates copper toxicity but also enables facile bead handling, recyclability, and scalable deployment in fixed-bed systems.

Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials.

The development of new approach methodologies (NAMs) to replace current testing for the safety assessment of engineered nanomaterials (ENMs) is hindered by the scarcity of validated experimental data for many ENMs. We introduce a framework to address this challenge by harnessing the collective expertise of professionals from multiple complementary and related fields ("wisdom of crowds" or WoC). By integrating expert insights, we aim to fill data gaps and generate consensus concern scores for diverse ENMs, thereby enhancing the predictive power of nanosafety computational models.

Screening uncalibrated priority pollutants by improved AHP-CRITIC method at development land.

Unlisted uncalibrated pollutants in the industrial land of northeast China are continuously accumulating due to insufficient regulatory control, posing a serious threat to the ecological environment and human health. To address this challenge and begin to quantify the currently unlisted uncalibrated pollutants present in the industrial land in northeast China, 170 candidate pollutants were screened based on the literature research method. The Analytical Hierarchy Process (AHP) and the Criteria Importance Through Intercrieria Correlation (CRITIC) were utilized equally to screen for priority control of unlisted uncalibrated pollutants.

Microplastic composition-dependent effects on NO emissions driven by changes in soil N process and microbial communities.

Biodegradable microplastics (MPs) are increasingly promoted as alternatives to conventional plastics to mitigate agricultural plastic pollution. However, their effects on soil microbial functions and nitrogen (N) cycling remain poorly understood. Here, we investigated the effects of biodegradable (polylactic acid and polybutylene adipate-co-terephthalate; PLA and PBAT) and conventional (polyethylene, PE) MPs on plant growth, nitrous oxide (NO) emissions, and microbial communities in an 80-day coriander (Coriandrum sativum L.

Prediction of Metal Nanoparticle Interactions with Soil Properties: Machine Learning Insights into Soil Health Dynamics.

Metal nanoparticles (MNPs) offer great potential to enable precision and sustainable agriculture. However, a comprehensive understanding of the interactions between multiple MNPs and soil properties, including impacts on overall soil health, remains elusive. Here, 4 different interpretable machine learning models were employed to systematically analyze the interactive effects of 7 soil physicochemical properties, 3 MNP properties, and 3 external factors on soil health indicators including pH, soil microbial biomass, Shannon index, and enzyme activities.

Prediction of blood-brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks.

In this study, we introduce a novel approach for predicting two key drug properties, blood-brain barrier (BBB) permeability and human intestinal absorption via Caco-2 permeability. Our methodology centers around a specialized neural network, the atom transformer-based Message Passing Neural Network (MPNN), which we have combined with contrastive learning techniques to enhance the process of representing and embedding molecular structures for more accurate property prediction. These innovative models focus on predicting BBB and Caco-2 permeability -two critical factors in drug absorption and distribution- which fall under the broader scope of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties.

Environmental and safety aspects of nanotechnology in genetically modified crops for sustainable agriculture.

The rising global demand for food poses a significant threat to environmental health through both biotic (e.g., pests, pathogens) and abiotic (e.

Key events relating to homeostasis and regeneration of freshwater planarians (Dugesia Japonica) after exposure to various ZnO-forms.

This study aims to investigate the toxicity and underlying mechanisms of ZnO nanoparticles (ZnO NPs), bulk ZnO (ZnO MPs), and zinc ions (Zn) on Dugesia japonica planarians, with a focus on their bioaccumulation, transformation, and associated biological effects. Using advanced techniques such as synchrotron X-ray fluorescence (XRF), X-ray Absorption Near Edge Structure (XANES) and single particle ICP-MS (sp-ICP-MS), we measured the accumulation, distribution, and transformation of these materials in planarians. All treatments caused significant Zn accumulation: ZnO NPs increased Zn by 120-fold, ZnO MPs by 100-fold, and Zn by 430-fold.

Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.

Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood-brain barrier permeability, and bioconcentration factor.

INSIGHT: An integrated framework for safe and sustainable chemical and material assessment.

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework. The EU INSIGHT project addresses this challenge by developing a novel computational framework for integrated impact assessment, based on the Impact Outcome Pathway (IOP) approach.

Going digital to boost safe and sustainable materials innovation markets. The digital safe-and-sustainability-by-design innovation approach of the PINK project.

In this innovation report, we present the vision of the PINK project to foster Safe-and-Sustainable-by-Design (SSbD) advanced materials and chemicals (AdMas&Chems) development by integrating state-of-the-art computational modelling, simulation tools and data resources. PINK proposes a novel approach for the use of the SSbD Framework, whose innovative approach is based on the application of a multi-objective optimisation procedure for the criteria of functionality, safety, sustainability and cost efficiency. At the core is the PINK open innovation platform, a distributed system that integrates all relevant modelling resources enriched with advanced data visualisation and an AI-driven decision support system.

MACRAMÉ - Advanced characterisation methodologies to assess and predict the health and environmental risks of advanced materials.

Ensuring the safety and sustainability of advanced materials (AdMas) is critical for fostering innovation while protecting human health and the environment. As industries integrate AdMas into commercial products to innovate in the next stage of the value chains, there is an urgent need for robust methodologies to detect, characterize, and assess their potential risks throughout their life cycle. The MACRAMÉ Project addresses this challenge by advancing standardized testing and regulatory frameworks, supporting the EU's vision for a toxic-free environment.

Intrinsic linkage of double S-scheme heterojunctions based on Fe@BiMoO@BiOI construction for photodegradation of tetracyclines: Enhanced antibiotic mineralization and detoxification.

This paper presents the design and evaluation of a novel photocatalyst, Fe@BiMoO@BiOI, featuring a double S-scheme-type heterojunction aimed at enhancing antibiotic removal and degradation. This innovative configuration promotes a high degree of dispersion of nano-zero-valent iron (NZVI), significantly improving the removal efficiency of antibiotics and the deep mineralization of tetracycline (TC). The optical properties of the NZVI passivation layer were confirmed through characterization techniques, demonstrating energy band structural alignment with BiMoO and BiOI, which facilitated the effective construction of the heterojunction.

: A Web-Based Multiscale Nanodocking Tool for Predicting Protein Adsorption onto Nanoparticles.

is a web-based application with a graphical user interface designed for modeling protein-nanomaterial interactions, accessible via the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.

An integrated multimethod approach for size-specific assessment of potentially toxic element adsorption onto micro- and nanoplastics: implications for environmental risk.

Micro- and nanoscale plastics (MnPs), arising from the environmental degradation of plastic waste, pose significant environmental and health risks as carriers for potentially toxic element (PTE) metals. This study employs asymmetrical flow field-flow fractionation (AF4) coupled with multi-angle light scattering (MALS) and inductively coupled plasma mass spectrometry (ICP-MS) to provide a size-resolved assessment of chromium (Cr), arsenic (As), and selenium (Se) adsorption onto carboxylated polystyrene nanoparticles (COOH-PSNPs) of 100 nm, 500 nm, and 1000 nm. Cr exhibited the highest adsorption, with adsorption per particle surface area increasing from 9.

Instance maps as an organising concept for complex experimental workflows as demonstrated for (nano)material safety research.

Nanosafety assessment, which seeks to evaluate the risks from exposure to nanoscale materials, spans materials synthesis and characterisation, exposure science, toxicology, and computational approaches, resulting in complex experimental workflows and diverse data types. Managing the data flows, with a focus on provenance (who generated the data and for what purpose) and quality (how was the data generated, using which protocol with which controls), as part of good research output management, is necessary to maximise the reuse potential and value of the data. Instance maps have been developed and evolved to visualise experimental nanosafety workflows and to bridge the gap between the theoretical principles of FAIR (Findable, Accessible, Interoperable and Re-usable) data and the everyday practice of experimental researchers.

CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials.

The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced models, approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems.

Combining the N Gas Flux Method and NO Isotopocule Data for the Determination of Soil Microbial NO Sources.

Rationale: The analysis of natural abundance isotopes in biogenic NO molecules provides valuable insights into the nature of their precursors and their role in biogeochemical cycles. However, current methodologies (for example, the isotopocule map approach) face limitations, as they only enable the estimation of combined contributions from multiple processes at once rather than discriminating individual sources. This study aimed to overcome this challenge by developing a novel methodology for the partitioning of NO sources in soil, combining natural abundance isotopes and the use of a N tracer (N Gas Flux method) in parallel incubations.

Easy-MODA: Simplifying standardised registration of scientific simulation workflows through MODA template guidelines powered by the Enalos Cloud Platform.

Modelling Data (MODA) reporting guidelines have been proposed for common terminology and for recording metadata for physics-based materials modelling and simulations in a CEN Workshop Agreement (CWA 17284:2018). Their purpose is similar to that of the Quantitative Structure-Activity Relationship (QSAR) model report form (QMRF) that aims to increase industry and regulatory confidence in QSAR models, but for a wider range of model types. Recently, the WorldFAIR project's nanomaterials case study suggested that both QMRF and MODA templates are an important means to enhance compliance of nanoinformatics models, and their underpinning datasets, with the FAIR principles (Findable, Accessible, Interoperable, Reusable).

A systematic review on the state-of-the-art and research gaps regarding inorganic and carbon-based multicomponent and high-aspect ratio nanomaterials.

This review explores the state-of-the-art with respect to multicomponent nanomaterials (MCNMs) and high aspect ratio nanomaterials (HARNs), with a focus on their physicochemical characterisation, applications, and hazard, fate, and risk assessment. Utilising the PRISMA approach, this study investigates specific MCNMs including cerium-zirconium mixtures (CeZrO) and ZnO nanomaterials doped with transition metals and rare earth elements, as well as Titanium Carbide (TiC) nanomaterials contained in Ti-6Al-4V alloy powders. HARNs of interest include graphene, carbon-derived nanotubes (CNTs), and metallic nanowires, specifically Ag-based nanowires.

NanoTube Construct: A web tool for the digital construction of nanotubes of single-layer materials and the calculation of their atomistic descriptors powered by Enalos Cloud Platform.

NanoTube Construct is a web tool for the digital construction of nanotubes based on real and hypothetical single-layer materials including carbon-based materials such as graphene, graphane, graphyne polymorphs, graphidiyene and non-carbon materials such as silicene, germanene, boron nitride, hexagonal bilayer silica, haeckelite silica, molybdene disulfide and tungsten disulfide. Contrary to other available tools, NanoTube Construct has the following features: a) it is not limited to zero thickness materials with specific symmetry, b) it applies energy minimisation to the geometrically constructed Nanotubes to generate realistic ones, c) it derives atomistic descriptors (e.g.

NanoBioAccumulate: Modelling the uptake and bioaccumulation of nanomaterials in soil and aquatic invertebrates via the Enalos DIAGONAL Cloud Platform.

is a free-to-use web-based tool hosted on the Enalos DIAGONAL Cloud Platform (https://www.enaloscloud.novamechanics.