https://hdl.handle.net/10259/9477 Mon, 17 Aug 2026 11:00:13 GMT 2026-08-17T11:00:13Z https://hdl.handle.net/10259/11955 Processing, microstructure, electrical properties and cytotoxic behaviour of lead-free 0.99K0.5Na0.5NbO3-0.01BiFeO3 piezoceramics prepared using Spark Plasma Sintering (SPS) Iacomini, Antonio .; Garroni, Sebastiano; Mureddu, Marzia; Malfatti, Luca; Thakkar, Swapneel; Orrù, Roberto; Barbarossa, Simone; Pakhomova, Ekaterina; Cao, Giacomo; Tamayo Ramos, Juan Antonio; Parra de la Parra, Sandra de la; Rumbo Lorenzo, Carlos; García, Álvaro; Bartolomé, José F.; Pardo, Lorena . In this work, “lead free” 0.99K0.5Na0.5NbO3-0.01BiFeO3 (KNN–BF) piezoceramics were prepared by a combination of mechanochemical activation, Spark Plasma Sintering and post-annealing treatment. Results show that the annealing treatment causes important microstructural and electrical changes. The SPS sample was characterized by low electrical resistance, while the air annealed samples showed better insulating properties due to a partial compensation of the oxygen vacancy. The best piezoelectric properties were found for the samples annealed at 1000 and 1050 ​°C for 2h due to the optimum grain size and oxygen vacancy compensation achieved. A further enhancement of the dielectric and piezoelectric properties was achieved through a second annealing treatment in oxygen flux at 1050 ​°C for 30 ​min. Moreover, the toxicity of the pellets was evaluated through exposure experiments to the pulverized KNN–BF pellets, employing two widely used human and environmental cellular models. Thu, 01 Sep 2022 00:00:00 GMT https://hdl.handle.net/10259/11955 2022-09-01T00:00:00Z https://hdl.handle.net/10259/11723 SAGRNet: A novel object-based graph convolutional neural network for diverse vegetation cover classification in remotely-sensed imagery Gui, Baoling; Sam, Lydia; Bhardwaj, Anshuman; Soto Gómez, Diego; González Peñaloza, Félix; Buchroithner, Manfred F.; Green, David R. Growing global population, changing climate, and shrinking land resources demand for quicker, efficient, and more accurate methods of mapping and monitoring vegetation cover in remote sensing datasets. Many deep learning-based methods have been widely applied for semantic segmentation tasks in remote sensing images of vegetated environments. However, most existing models are pixel-based, which introduces challenges such as high time consumption, cumbersome implementation, and limited scalability. This paper presents the SAGRNet model, a Graph Convolutional Neural Network (GCN) that incorporates sampling aggregation and self-attention mechanisms, while leveraging the ResNet residual network structure. A key innovation of SAGRNet is its ability to fuse features extracted through diverse algorithms, enabling comprehensive representation and enhanced classification performance. The SAGRNet model demonstrates superior performance over leading pixel-based neural networks, such as U-Net++ and DeepLabV3, in terms of both time efficiency and accuracy in vegetation image classification tasks. We achieved an overall mapping accuracy of ~90 % using SAGRNet, compared to ~87% and ~85% from U-Net++ and DeepLabV3, respectively. Additionally, it offers more convenience in data processing. Furthermore, the model significantly outperforms cutting-edge graph-based convolutional networks, including Graph U-Net (achieved overall accuracy ~65%) and TGNN (achieved overall accuracy ~75%), showcasing exceptional generalization capability and classification accuracy. This paper provides a comprehensive analysis of the various processing aspects of this object-based GCN for vegetation mapping and emphasizes its significant potential for practical use. The model’s versatility can also be expanded to other image processing domains, offering unprecedented possibilities of information extraction from satellite imagery Mon, 01 Sep 2025 00:00:00 GMT https://hdl.handle.net/10259/11723 2025-09-01T00:00:00Z https://hdl.handle.net/10259/11718 Methods and tools for the safety assessment part of the European Commission’s safe and sustainable by design framework when applied to advanced materials Pomar-Portillo, Vicenç; Suarez-Merino, Blanca; Aparicio Martínez, Santiago; Badetti, Elena; Boyles, Mathew; Brunelli, Andrea; Fito-López, Carlos; Garmendia-Aguirre, Irantzu; Giubilato, Elisa; Katsumiti, Alberto; Laurini, Erik; Lofty, Morgan; Marson, Domenico; Pizzol, Lisa; Rodríguez-Llopis, Isabel; Rumbo Lorenzo, Carlos; Scott-Fordsmand, Janeck J.; Stone, Vicki; Trabucco, Sara; Hristozov, Danail; Nowack, Bernd The Safe and Sustainable-by-Design (SSbD) framework by the EC-JRC (European Commission – Joint Research Centre) provides a structured approach to integrate safety and sustainability considerations from the earliest stages of chemical and material innovation. However, applying SSbD principles to advanced materials poses specific challenges due to their complex and diverse physicochemical properties. This work analyzes and maps hazard, exposure, fate and risk assessment methods and tools applicable to Steps 1, 2 and 3 of the EC-JRC SSbD framework, categorizing them across its three tiers to address different stages of product development. The analysis highlights the challenges of adapting conventional testing and modelling approaches to advanced materials, particularly for hazard assessment, and considers the relevance and limitations of tools originally developed for exposure and risk assessment of engineered nanomaterials when applied to broader advanced materials categories. An assessment of operational status, access conditions, and tool formats provides practical insights for researchers and industry stakeholders. The study identifies key methodological gaps and offers recommendations to improve and expand the current tool landscape. By providing a structured mapping of available resources and challenges, this work supports the effective implementation of SSbD principles, promoting the safe and sustainable development of advanced materials Sat, 01 Nov 2025 00:00:00 GMT https://hdl.handle.net/10259/11718 2025-11-01T00:00:00Z https://hdl.handle.net/10259/11717 Molecular layering and CO₂ selectivity in graphene-supported natural deep eutectic solvent films: An in-silico investigation Rozas Azcona, Sara; Aguilar Cuesta, Nuria; Marcos Villa, Pedro A.; Bol Arreba, Alfredo; Aparicio Martínez, Santiago A multiscale computational study was conducted to investigate graphene-supported thin films composed of a natural deep eutectic solvent (NADES) formed by menthol and decanoic acid (MENTH:DA), with a focus on applications in sustainable CO₂ capture. Density functional theory (DFT) and molecular dynamics (MD) simulations were employed to elucidate interfacial structuring, molecular interactions, and gas adsorption behavior. DFT results indicated a strong interaction between decanoic acid and the graphene surface (− 35.88 kJ/mol), characterized by a parallel orientation that maximizes van der Waals interactions. In contrast, menthol displayed weaker adsorption energies (− 5.15 kJ/mol) and a predominantly perpendicular orientation. MD simulations revealed the formation of distinct adsorption layers, with decanoic acid enriched in the first layer and menthol in the second, while the NADES hydrogen-bonding network remained largely intact. CO₂ exhibited preferential adsorption over flue gas components (N₂, H₂O, O₂), with substantial accumulation in both the first and second interfacial layers. Approximately 50% of the CO₂ content from flue gas mixtures was retained within the structured region. Adsorption performance was found to be largely independent of temperature (303− 323K) and NADES film thickness (20–50 Å). These results provide fundamental insight into NADES–graphene interactions and highlight the potential of type V, naturally derived deep eutectic solvents as selective and environmentally benign materials for CO₂ separation technologie Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/10259/11717 2026-01-01T00:00:00Z