10784 research outputs found
Sort by
Enhancement of thermal and mechanical properties of waterborne polyurethane-urea via chitin nanocrystal reinforcement
Get PDFPublisher Copyright: © 2025Chitin nanocrystals (ChNC) have attracted significant interest as reinforcement due to their exceptional mechanical properties such as Young's modulus, surface functional groups that promote hydrogen-bonding interactions with specific matrices, and high length/diameter aspect ratio. Therefore, this study investigated the incorporation of chitin nanocrystals into a waterborne polyurethane urea dispersion (WBPUU) at concentrations ranging from 0.5 to 7 wt%. Nanocomposite films were prepared using an ultra-sonication assisted solvent casting method, and their properties were thoroughly analysed. The results demonstrate that the addition of ChNC significantly enhances thermomechanical stability, Young's modulus and stress at break, achieving the percolation threshold at a theoretical concentration of 3 wt% of ChNC. Notably, beyond the percolation threshold, these properties have increased significantly up to WBPUU7, with thermomechanical stability improving by more than 500 % and both Young's modulus and stress at break increasing by over 100 % when compared to values obtained at WBPUU. The study revealed that the addition of ChNC significantly influences water absorption, resulting in a fourfold increase in uptake in a basic medium. Furthermore, the abrasion resistance of the nanocomposites improves with ChNC content due to stable and interconnected network forms.Peer reviewe
The future need for critical raw materials associated with long-term energy and climate strategies: The illustrative case study of power generation in Spain
Get PDFPublisher Copyright: © 2024 The AuthorsThe deployment of renewable energy technologies, though necessary to decarbonise our society, poses a risk stemming from the massive increase in the use of critical raw materials. This work presents a prospective evaluation of a national electricity generation mix up to 2050 and discusses the increase in several critical and strategic materials used in this transition. Results indicate that the deployment of solar photovoltaics and wind energy will raise material criticality concerns in the coming decades. When comparing a decarbonisation scenario aligned with the 2030 Spanish policy with a business-as-usual scenario, results show that a higher penetration of renewables would involve increases of up to 53 % in silicon, 27 % in aluminium, 11 % in copper, and less than 1 % in other materials by 2050. Overall, the decarbonisation scenario would involve up to 12 % more materials. Furthermore, criticality indicators show increases of 0.06 % and 5 % by 2050 depending on the selected indicator. Differences in figures highlight discrepancies in the way criticality is evaluated, suggesting that further research is needed. Nevertheless, national long-term energy policies such as the Spanish one are urged to implement criticality issues in their formulation. Consequently, the authors recommend including critical material usage within energy and climate planning models.Peer reviewe
Novel Binders for Aqueous Electrode Processing of Electrochemical Capacitors
No full textPublisher Copyright: © 2024 The Authors. ChemSusChem published by Wiley-VCH GmbH.This work studies the use of epoxy and polyurethane formulations as binders for the aqueous processing of activated carbon (AC) electrodes used as positive and negative electrodes in Electrochemical Double Layer Capacitors (EDLCs). The use of amine and carbodiimide as crosslinkers is also evaluated. The mechanical properties of those different binders have been investigated, looking towards aqueous processable and flexible electrodes. Microstructural analysis of the fabricated AC electrodes has been carried out to understand the pore-blocking effect exhibited by certain polymers. Furthermore, electrochemical characterization of all the systems has been performed by cyclic voltammetry, electrochemical impedance spectroscopy, and constant current charge/discharge measurements at different current densities. The obtained results show that polyurethane (PU) outperforms in terms of energy and power density the carboxymethyl cellulose:styrene butadiene rubber (CMC : SBR) reference system. Moreover, the studied polyurethanes maintain close to 100 % of their initial capacitance after 2500 cycles under a current density of 5 A g−1 and a discharge time of 20 s.Peer reviewe
Exercise therapy innovations in outpatient mental health care: First insights into the role of exercise therapists’ attitude towards evidence-based practice for real-world implementation settings
No full textPublisher Copyright: © The Author(s) 2025.For successful implementation of healthcare interventions, attitudes of healthcare professionals are a key factor. Research in the field of psychological treatment suggests that attitude towards evidence-based practice (EBP) may play a crucial role. However, little research has been conducted on the extent to which this applies to the field of exercise therapy, where there is a strong need for development regarding the dissemination of EBP. A recent pragmatic multisite randomized controlled trial (ImPuls) can serve as an innovative example for the implementation of an evidence-based exercise intervention including behavior change techniques within the outpatient mental health care system in Germany. The aim of the current study was to explore the role of exercise therapists’ attitude towards EBP for an implementation process by identifying relationships with other factors of importance for implementing innovations (e.g., motivation or self-efficacy). Exercise therapists (n = 26) completed online surveys pre- (week 0) and postintervention (week 24+). Correlation analyses and linear regressions were performed considering a total score for attitude towards EBP as well as mean scores for its subdimensions. The total score was rather positive (mean = 2.58, standard deviation = 0.43) and significantly related to program-specific motivation (r = 0.64, p < 0.001), self-efficacy (r = 0.42, p < 0.034), and acceptance (r = 0.56, p < 0.003) at baseline. Exercise therapists’ education and further training should therefore have an increased focus on EBP, including the usage of scientific findings, the integration into routine practice and the scopes and obligations that come along with it.Peer reviewe
Pedot conducting polymers as corrosion inhibitors additives for acrylic-uv coatings
No full textPublisher Copyright: © 2024 Elsevier B.V.Conductive Polymers (CPs) have gained interest as a promising alternative to standard corrosion-protective coatings. Among CPs, polyaniline and polypirrole are the most widely known, however, they present some critical issues related to their stability and toxicity. This, together with the limited offer of CPs for corrosion inhibition coatings, strongly limits their wide scale application. In this work, we present a new class of corrosion-protective coatings incorporating one of the most stable and low toxicity CPs poly(3,4-ethilenedioxythiophene), commonly known as PEDOT, doped with different counter-anions that themselves also have corrosion inhibition (C.I.) properties. These dopants include three alkyl phosphates and a cinnamate derivative. These new PEDOT: C.Is. polymers were incorporated into an acrylic formulation at different concentrations and UV cured to obtain polymer coatings. The corrosion reduction properties of the coatings applied onto mild steel surfaces were tested in sea-like conditions (NaCl (aq) 3.5 wt%) through Potentiostatic Electrochemical Impedance Spectroscopy (PEIS), Potentiodynamic Polarization (PP), and exposure to harsh environment in a salt spray chamber. The results obtained from the electrochemical characterization showed that the new PEDOT:C.I. coatings present efficiencies up to 99 % and exhibited improved resistance to corrosive environment compared to the reference UV-acrylic coating. These CP-containing coatings demonstrated effective protection of the metal surface from corrosion, indicating their potential as viable alternatives to standard corrosion-protective coatings while being more environmentally friendly.Peer reviewe
ANALYSIS OF THE DIFFERENCE BETWEEN THE REFERENCE TEMPERATURE VALUES DERIVED FROM CONVENTIONAL AND MINI-C(T) SPECIMENS: EFFECT OF THE NUMBER OF VALID FRACTURE TESTS
No full textPublisher Copyright: Copyright © 2025 by ASME.The Long-Term-Operation (LTO) of nuclear power plants (NPPs) requires, among other technical and scientific challenges, a precise knowledge of the fracture behavior of the steels (both base material and welds) that constitute the reactor pressure vessel (RPV). This material property evolves (decreases) with the irradiation level, so surveillance programs are defined and completed to measure it throughout the NPP operational life and to ensure that the structural integrity of the RPV is not jeopardized. The surveillance programs are additionally performed by testing materials placed inside the RPV (surveillance capsules) which were initially designed to cover the initial lifespan (i.e., 40 years). This implies that the amount of available material to be tested during the life extension of NPPs may be scarce and it is not possible to perform fracture toughness tests using conventional specimens (e.g., 1T or 25.4 mm thick). In this context, mini-C(T) specimens (0.16T or 4 mm thick) appear as a key technology to perform the fracture characterization during the LTO, as long as 8 mini-C(T) specimens may be extracted from previously tested Charpy specimens or 48 mini-C(T) specimens may be obtained from a previously tested 1T CT specimen. This provides actual values of the material fracture toughness (unlike in the case of Charpy tests), multiplies the knowledge (i.e., the experimental results) about the material fracture behavior and allows irradiated tested material to be reused. In other words, mini-C(T) specimens optimize the use of the finite remaining stock of surveillance material from operating NPPs. Constituting such a promising technology, the use of mini-C(T) specimens to characterize the reference temperature (T0) of ferritic steels (i.e., the fracture behavior within the ductile-to-brittle transition zone, DBTZ) has been extensively analyzed in the last decade. Generally, mini-C(T) specimens provide similar results to those generated by using conventional (e.g., 1T) specimens in both irradiated and non-irradiated conditions. However, experimental results found in literature reveal situations where the deviation between the T0 predictions of conventional and mini-C(T) specimens are basically negligible, and situations where the deviation between such predictions achieve up to 40 °C. This situation must be clarified so that regulatory bodies accept the use of mini-C(T) specimens for the fracture characterization of RPV steels. This paper provides insights into how the mentioned deviations depend on the extension of the experimental program performed for the characterization of T0, revealing that larger experimental programs (i.e., more fracture toughness results) provide much smaller discrepancies and also suggesting that for very large programs the two types of predictions tend to converge.Peer reviewe
A training workbench based on transient building models for creating intelligent energy operators
Get PDFPublisher Copyright: © The Author(s), 2025. Published by Cambridge University Press.The development of intelligent control-oriented solutions for building energy systems is a promising research field. The development of effective systems relies on seldom available large data sets or on simulation environments, either for training or execution phases. The creation of simulation environments based on thermal models is a challenging task, requiring the usage of third-party solutions and high levels of expertise in the energy engineering field, which poses relevant restrictions to the development of control-oriented research. In this work, a training workbench is presented, integrating an accurate but lightweight lumped capacitance model with proven accuracy to represent the thermal dynamics of buildings, engineering models for energy systems in buildings, and user behavior models into an overall building energy performance forecasting model. It is developed in such a way that it can be easily integrated into control-oriented applications, with no requirements to use complex, third-party tools.Peer reviewe
A new Al80Mg10Si5Cu5 multicomponent aluminium alloy: Microstructure, mechanical, and physical properties
No full textPublisher Copyright: © 2025 The Authors.This study investigates the microstructure, mechanical, and physical properties of a newly developed and patented multicomponent aluminium alloy based on the AlMgSiCu system, produced by High Pressure Die casting (HPDC). This alloy exhibits superior mechanical properties compared to other HPDC alloys, especially at elevated temperatures. Tested at both room temperature (RT), and at 200 °C, a range where most aluminium alloys degrade, it demonstrated remarkable thermal stability, maintaining its characteristics. The alloy's complex microstructure includes an aluminium matrix with Mg2Si, Al2Cu, and Al2CuMg phases. At 200 °C, the alloy's hardness was twice that of the commonly used AlSi9Cu3 alloy. The yield strength (YS) reached 244 MPa, ultimate tensile strength (UTS) 267 MPa, and elongation (E) of 0.69 %, showing a 65 % increase in YS and a 45 % increase in UTS, compared to AlSi9Cu3 alloy. In compressive testing, the alloy also showed superior results, with a YS of 251 MPa, ultimate compressive strength (UCS) of 468 MPa, and deformation (D) of 18.50 %, with a 90 % increase in YS and an 80 % increase in UCS. The results are significant, despite a 40 % lower deformation compared to AlSi9Cu3. The transformation of the Al2Cu phase with temperature to form the Al2CuMg phase had a significant impact on the material's overall mechanical properties, maintaining the mechanical properties at 200 °C. Comparing the YS, UTS and UCS-to-density ratio at 200 °C, this alloy shows great potential for high-temperature applications being an attractive candidate for aerospace and automotive sectors, particularly for components like drum brakes, traditionally made of cast iron.Peer reviewe
Fault Tolerance and Fallback Strategies in Connected and Automated Vehicles: A Review
Get PDFPublisher Copyright: © 2020 IEEE.Connected and Automated Vehicles (CAVs) are considered the future of transportation, offering increased safety, efficiency, and convenience. However, their reliance on sophisticated sensors and complex algorithms poses challenges, especially in scenarios with uncertainties, constraints, or failures. Dynamic Driving Task (DDT) fallback and fault tolerance strategies serve as critical mechanisms to ensure safe operation when primary systems fail or face functional insufficiencies. This paper provides an analysis of the fault-related taxonomy established by international standards and a comprehensive review of the DDT fallback and fault tolerance strategies used in CAVs, focusing on their strategy, classification, and implementation methods. Moreover, the challenges and future research directions for the development and improvement of fault tolerance strategies are discussed. The analysis shows that the main trends are to avoid the termination of the CAV operation in case of a failure or functional insufficiency, or at least to be able to guide the vehicle to a safe state. However, there is a tendency towards the possibility of continuing the operation. This review contributes to a deeper understanding of the role of DDT fallback and fault tolerance strategies for CAVs and future trends.Peer reviewe
Offline reinforcement learning for job-shop scheduling problems
No full textPublisher Copyright: © 2025 Elsevier B.V.Recent advances in deep learning have shown significant potential for solving combinatorial optimization problems in real-time. Unlike traditional methods, deep learning can generate high-quality solutions efficiently, which is crucial for applications like routing and scheduling. However, existing approaches like deep reinforcement learning (RL) and behavioral cloning have notable limitations, with deep RL suffering from slow learning and behavioral cloning relying solely on expert actions, which can lead to generalization issues and neglect of the optimization objective. Offline RL addresses these challenges by learning from fixed datasets while leveraging reward signals, making it especially suitable for constrained combinatorial problems where online exploration is impractical. This paper introduces a novel offline RL method designed for combinatorial optimization problems with complex constraints, where the state is represented as a heterogeneous graph and the action space is variable. Our approach encodes actions in edge attributes and balances expected rewards with the imitation of expert solutions. We demonstrate the effectiveness of this method on job-shop scheduling and flexible job-shop scheduling benchmarks, achieving superior performance compared to state-of-the-art techniques.Peer reviewe