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Size-dependent selectivity of Cu2O nanocube catalysts for CO2 reduction at industrial current densities
Get PDFThe electrochemical reduction of CO2 (CO2RR) to value-added chemicals offers a promising route for carbon recycling and renewable energy storage. Cu-based catalysts are uniquely capable of producing multi-carbon products such as ethylene, but their selectivity is highly sensitive to their morphology. In this work, we systematically investigate the impact of Cu2O nanocube size (45–600 nm) on CO2RR performance in both alkaline flow cell and zero-gap electrolyzer, both operating at industrially-relevant current densities. The catalysts were synthesized with well-controlled geometries and edge lengths. In the flow-cell, smaller nanocubes (45–75 nm) exhibited superior selectivity toward ethylene and liquid C2 products, achieving Faradaic efficiencies toward C2 products (FEC2) of up to 50%, attributed to an optimal balance between edge and facet sites. In contrast, in the zero-gap cell, although 45 nm cubes were the most ethylene-selective, overall FEC2 was reduced and strongly influenced by operational parameters, such as anolyte composition. Long-term tests revealed a trade-off between catalyst durability and ethylene selectivity. These findings demonstrate the critical interplay between nanostructure, testing configuration, and electrolyte, and emphasize the need to assess catalyst performance under industrially-relevant conditions
Understanding crystal surface anisotropy of organic materials via molecular modelling and facet-specific experimental characterization
Get PDFThe different facets of crystalline particles expose specific functional groups depending on their structure and morphology, thus, influencing surface properties of the resulting materials. As particle surface properties impact product performance, safety, and manufacturing efficiency, it is important to understand how crystal structure influences facet-specific surface properties. In this work, we focused on the effect of crystal structure and morphology on properties such as roughness, mechanical strength, and chemical features. Quercetin-dimethylformamide (QDMF), a solvated form of quercetin, was selected as a single-crystal model compound. By combining computational approaches with experimental validation, we developed a standardized procedure to correlate crystal structure packing and specific surface features. Experimental data collected using various techniques were then used to validate the simulations. First, we utilized Particle Informatics tools to analyse the surface chemistry and topology of specific QDMF crystal facets observed experimentally, namely {1–10}, {001}, and {200}. These computational results were then validated using Atomic Force Microscopy (AFM) integrated with Infrared (IR) spectroscopy, which provided topographical insights, chemical characterization, surface roughness measurements, and mechanical properties characterization (e.g., Young Modulus). For chemical imaging at high spatial resolution, we employed advanced mid-infrared techniques, such as Optical Photothermal Infrared (OPTIR) microscopy and scattering-type Scanning Near-field Infrared Microscopy (s-SNIM). The experimental data were in agreement with the simulations, showing how Particle Informatics tools can assist in the design of crystalline materials with tailored surface properties
Design of 3D bioengineered cardiac tissue models for advanced in vitro testing
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Reliability assessment of concrete walls as rockfall protection systems in mountainous areas
Get PDFRockfall protection systems are essential for safeguarding infrastructure and human activities in
mountainous regions, where falling rock masses pose persistent hazards that can cause severe damages. Structural mitigation measures, such as energy-dissipating barriers, are commonly installed near roads, buildings, and industrial facilities to reduce impact forces. These systems function by either absorbing the kinetic energy of falling blocks through deformation or resisting impact via mass and frictional dissipation. Two main energy dissipation strategies characterize these systems. Flexible barriers, such as net fences, absorb energy through large deformations and are widely used for their adaptability and effectiveness across varying block sizes. However, they require sufficient clearance between the barrier and protected infrastructure, which limits their application in narrow corridors. To overcome this constraint, rigid systems, such as L-shaped reinforced concrete walls, have been developed. For high-impact energies, a cushion layer of granular material is often added to the upslope face of the wall. However, this solution requires a large footprint, which is not always feasible, and in steep areas, the added weight can even lead to overall slope instability. Therefore, for expected impact energies up to approximately 800 kJ, a rigid system alone can represent an effective and economical solution. These structures rely on mass and bending resistance to dissipate energy without requiring buffer space, making them suitable for constrained environments. Nevertheless, their long-term reliability under diverse impact conditions remains an open question. This study introduces a time-integrated reliability analysis of rigid rockfall protection systems, focusing on performance under variable loading over extended periods. The approach incorporates the frequency–magnitude distribution of rockfall events, acknowledging that smaller blocks occur more frequently than larger ones. Variability in block mass, impact velocity, and kinetic energy is modelled within a probabilistic framework that accounts for uncertainties in material properties and structural response. The analysis is applied
to a real-world slope with documented rockfall activity, evaluating the reliability of a concrete wall system under
cumulative low-energy impacts and rare high-energy events. Using limit state functions, failure probabilities
and critical performance thresholds are estimated. Results emphasize the importance of integrating temporal and probabilistic dimensions into design, showing that rigid barriers, while effective in constrained spaces, exhibit reliability sensitivity to impact frequency, energy dissipation capacity, and degradation over time
A SysML-based framework towards EASA CS-23 digitalization: An MBSE approach
Get PDFThe general aviation sector is undergoing rapid transformation, driven by technological innovation and increasing market demand. However, certification frameworks remain predominantly document-based, resulting in inefficiencies and elevated costs. This study presents a digital certification framework grounded in Model-Based Systems Engineering (MBSE), utilizing the Systems Modeling Language (SysML) to encode the European Union Aviation Safety Agency (EASA) Certification Specifications (CS-23) and Acceptable Means of Compliance (AMC) into a structured, machine-readable model. The proposed methodology enables automated verification and validation, report generation, and traceability across regulatory artifacts. Beyond its technical contributions, the framework addresses interdisciplinary challenges in the present regulatory landscape, systems engineering, and organizational communication. It highlights the complexity of certification processes, emphasizing the need for coherent information exchange among stakeholders with varying levels of technical proficiency. The study contributes to ongoing discussions in the social sciences regarding institutional adaptation, digital transformation, and collaborative governance in high-reliability sectors. The framework’s scalability to commercial aviation and its potential to support emerging aircraft architectures underscore its relevance to both industry and regulatory bodies
LLM Edge Predictive Maintenance: Bridging Industrial IoT Sensors and Large Language Models for Predictive Maintenance
No full textOpen-source reference architecture connecting industrial edge sensor hardware (STEVAL-STWINBX1 / STWIN.box) to LLM-based diagnostic assistants (Claude) via the Model Context Protocol (MCP). Includes MCP servers for sensor acquisition and vibration signal processing (FFT, envelope analysis, bearing fault detection, ISO 10816), plus Claude Skills for guided condition monitoring, fault diagnosis, and operator-friendly report generation
A Technology Acceptance Model to Explore Factors of Acceptance of Emotional Monitoring Technologies in the Logistics Industry
No full textTowards a Population-Based Approach for Dynamic Monitoring of Underground Structures: A Numerical Study on Metro Tunnel Models
Get PDFUnderground structures are becoming increasingly vital components of modern transportation networks and urban systems, making their structural integrity a critical factor for safety and operational reliability. However, despite considerable progress in Structural Health Monitoring (SHM), the application of data-driven and vibration-based strategies to underground infrastructures remains an open and under-explored field, often because of limited data availability. Population-Based Structural Health Monitoring (PBSHM) offers a promising pathway to overcome this challenge by leveraging transfer learning to share diagnostic knowledge among similar structures. This study investigates the feasibility of extending the PBSHM paradigm to underground infrastructures, with a particular focus on a metro tunnel application. Through dynamic finite element simulations, relevant vibration features are identified, and damage detection strategies based on transmissibilities and cross-correlation functions are evaluated. The numerical results show that transmissibility-based indicators enable accurate damage localisation along the tunnel lining, even under noisy conditions. In contrast, cross-correlation features exhibit more limited performance in some configurations. Building on this evidence, the transmissibility-based damage indicator is subsequently embedded within the PBSHM framework and used as a transferable feature between tunnel models, achieving reliable damage detection in a second tunnel with heterogeneous characteristics, with F1 scores exceeding 80% for all considered damage severities and above 94% for the most critical case, thereby highlighting the potential of knowledge transfer for large-scale underground networks
Electrospun scaffolds incorporating nanoparticles for controlled drug delivery in post-infarction cardiac repair
Get PDFL'abstract è presente nell'allegato / the abstract is in the attachmen
A large multi-agent system with noise both in position and control
No full textIn this work, we consider a multi-population system where the dynamics of each agent
evolve according to a system of stochastic differential equations in a general functional setup, determined by the global state of the system. Each agent is associated with a probability measure, that
assigns the label accounting for the population to which the agent belongs. We do not assume any
prior knowledge of the label of a single agent, and we allow that it can change as a consequence of
the interaction among the agents. Furthermore, the system is affected by noise both in the agent’s
position and labels. First, we study the well-posedness of such a system and then a mean-field limit, as
the number of agents diverges, is investigated together with the analysis of the properties of the limit
distribution both with Eulerian and Lagrangian perspectives. As an application, we consider a large
network of interacting neurons with random synaptic weights, introducing resets in the dynamics