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Durable copper nanowires for flexible curvature sensors
No full textMetal nanowire-based flexible conducting surfaces (FCS) are vital for next-generation flexible and wearable sensors. Copper nanowires (CuNWs) offer a low-cost alternative to the expensive silver nanowires for fabricating FCS, yet their poor stability remains a significant challenge. In this study, we report the synthesis of ultralong CuNWs using a hydrothermal polyol method across a range of temperatures (120–180 ◦C). The CuNWs synthesised at 160 ◦C (CuNW-160) demonstrated the best performance. CuNW-160 films maintained stable conductivity for over 60 days in ambient conditions and thermal stability up to 140 ◦C. A capacitive curvature sensor was fabricated using FCS made with CuNW-160, which maintained consistent performance over 10,000 bending cycles and still showed good curvature sensitivity after 75 days. This highlights the potential use of the copper nanowires by tuning reaction temperature for use in reliable, low-cost flexible electronics
Cold Climate Wind: Challenges, Technological Solutions and Policy
No full textCold-climate wind power represents a major subset of onshore global capacity, with approximately one third of existing installations located in regions prone to low temperatures and icing. The main technical challenge in cold-climate conditions is ice accretion on turbine blades, which degrades aerodynamic performance and causes production losses, while ice falling from turbines poses a significant safety risk. These issues have driven the development of specialized technological solutions, including ice detection and mitigation systems and detailed forecasting models. In parallel, specific policy approaches have also been developed to address increased icing risks. This review focuses on wind turbine blade icing and related issues for onshore wind. It presents state-of-the-art technical solutions for icing-related challenges, as well as approaches for icing modeling and forecasting of icing conditions. In addition, relevant policies from different countries are reviewed. Production losses due to icing are highly variable, influenced by ice thickness, shape, and post-icing wind conditions. Accurate estimation and forecasting of these losses require advanced tools, ranging from SCADA-based analyses to machine learning methods and mesoscale weather prediction models. Ice detection technologies are being developed based on both direct and indirect measurement principles. Efforts to validate and certify these systems for operational use, such as automatically stopping and starting turbines, are ongoing. Icing mitigation includes both active technologies, such as blade heating systems, and passive approaches, such as icephobic coatings. Uncertainty quantification has become central to project financing and planning, with standards emerging to guide risk assessment. Policy and regulatory responses vary internationally: some regions, like Québec, mandate cold-climate certifications and real-time operational data reporting, while others focus on risk assessments and safety zones. Regulatory approaches remain somewhat fragmented and guided by local priorities. Further harmonization is needed to address critical safety issues such as ice throw
Deep spatio-temporal learning for multi-hazard events: A ConvGRU multi-label classification approach
No full textThe forecasting of multi-hazards is a vital, though underinvestigated, area of disaster risk management. The traditional studies have mainly focused on single-hazard forecasting, thus leaving its utility in real-world and realistic scenarios. This study, in turn, presents a spatio-temporal multi-label classification model, a framework designed expressly to capture the complex interrelationships between a range of hazards. The methodological framework used disaster occurrence data from the Open Federal Emergency Management Agency (OpenFEMA) database and converted the raw records of disasters into a multi-label dataset. Pressure-level reanalysis data is extracted from Climate Data Store (CDS) based on the multi-hazard event. Spatial data is extracted in 25 59 grid format in different temporal dependencies (12 h, 8 h, 6 h) at the 850 hPa pressure level. The model architecture combines convolutional neural networks (CNNs) with spatial attention mechanisms and gated recurrent units (GRUs) that model the temporal sequences. This combination enables multi-hazard predictions by utilizing the spatial and temporal data. Experimental analysis reveals that the proposed model outperformed the baseline variants, i.e., 2D CNN, Convolutional Long Short-Term Memory (ConvLSTM), and Convolutional Gated Recurrent Unit (ConvGRU) without attention. The proposed model achieved per-class accuracy up to 0.8868, the subset accuracy is 0.55, and the Hamming loss up to 0.127, which are 3.88%, 13.59% and 21.12% performance improvements over the baseline models respectively. In addition, the use of various lead times and the fusion of multiple lead times (12 h+8 h+6 h) significantly improves the predictive capability. The proposed framework has high potential for disaster preparedness and early warning systems in the real world. It proposes a flexible and efficient method of dealing with the growing complexity of multi-hazard environments
High-temperature corrosion of steels by nitridation in ammonia: Degradation mechanisms and comparison between steel grades
No full textAmmonia is an integral part of hydrogen economy and a viable carbon-free fuel for marine combustion engines. The combination of ammonia, high temperatures and long exposure times can cause nitridation corrosion in steels relevant for combustion engines, but limited research has been conducted on the topic. In this work, three steels, 34CrNiMo6 (low-alloy steel EN1.6582), X40CrSiMo10-2 (alloy steel EN1.4731), and 316plus (stainless steel EN1.4420), were exposed to gaseous ammonia atmospheres at 400 °C and 500 °C for up to 1000 h. The specimen surfaces were characterised by a variety of techniques, e.g., electron backscatter diffraction, glow-discharge optical emission spectroscopy, and micro-indentation, while the system thermodynamics was modelled with Thermo-Calc Software making use of compositional depth profile data. All materials underwent nitridation under the test conditions, and the formed nitride surface film was in most cases brittle, porous, and cracked, and typically tens of micrometres thick. In all investigated alloys, the structure, phase and elemental composition of the surface films were function of the alloying elements. For the studied stainless steel grade, the surface film compositions were dependent also on temperature, with a protective chromium nitride film being formed at 500 °C compared to an iron nitride film at 400 °C, in agreement with thermodynamics of nitride formation. The obtained results can be used to tailor the film composition in the desired direction. The study highlighted the importance of careful material selection for the conditions in ammonia combustion engines
Design and evaluation of load-follow control schemes for a small district heating reactor LDR lite
No full textLoad following is needed in district heating networks, where heat demand varies significantly. LDR lite is a small light water reactor designed for flexible, low-temperature heat production. In this study two load-follow control schemes were designed for LDR lite. In the first option, control rods are used to regulate the power, while the secondary circuit temperatures are kept constant. The second option utilizes temperature feedback, adjusting the reactor power via secondary circuit pump control. The control schemes were evaluated by simulating a simplified load-follow scenario using coupled 3D neutronics and system thermal hydraulics. The control rod-driven scheme successfully followed the power demand within a narrow margin of error and reliably produced the required supply temperature to the district heating network. In contrast, the temperature feedback-driven control resulted in larger deviations, unpredictable temperature behaviour, and failed to meet the required supply temperature consistently. The results demonstrate that the control rod-driven power regulation is the more viable strategy for load following with LDR lite. Further optimization is still required, but the established control scheme provides a foundation for future research also in other fields
Global solar energy potential forecasting through machine learning and deep learning models
No full textClimate change is accelerating at an alarming rate, 2024 has been verified as the hottest year so far, with an average temperature of 1.55 °C warmer than upstream values set in the Paris Agreement. As such, extreme weather patterns like floods, hot weather, wild fires and glaciers melts that all pose a threat of harm to ecological systems. By investing in solar technology, nations can work towards a more sustainable energy future and addressing the pressing challenge of climate change. This study exploited the global solar photovoltaic (PV) energy potential using the Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) and Temporal Convolutional Network (TCN) models implemented in Python for the period 2023 to 2050 by taking input data from 2000 to 2022. The results revealed that, the solar PV capacity was 1.23 GW in the year 2000 which then increased to 1053.12 GW by 2022. SARIMAX and TCN models estimated the future of solar PV capacity which is increased from 1291.29 GW and 1094.40 GW in 2023 to 11641.41 GW and 11577.24 GW until 2050. However, the solar PV energy was 1.03 TWh in 2000 which then increased to 1323.32 TWh in 2022. SARIMAX and TCN models forecasted the future of solar PV energy which is increased from 1935.52 TWh and 1557.92 TWh in 2023 to 14967.15 TWh and 15928.52 TWh until 2050. It is observed from the results that SARIMAX model has higher accuracy as compared with the TCN model
Towards the development of a CRISPR-Cas9 based kill switch for Saccharomyces cerevisiae
No full textBACKGROUND: Advancements in synthetic genetic circuits have enabled programmable and condition-dependent control of microbial cell growth. CRISPR-Cas9-based kill switches, genetic systems that program cells to lose viability in response to specific conditions, have recently been demonstrated for bacterial cell factories but not yet in yeast.RESULTS: In this study, we present a foundational demonstration for a CRISPR-based ki ll s witch in S accharomyces cerevisiae, CRISPR KiSS. The CRISPR KiSS employs inducible CRISPR targeting essential genes to elicit growth inhibition. The activation of the KiSS system is achieved through conditional expression of a guide RNA (gRNA) upon anhydrotetracycline (ATc) induction, thereby activating CRISPR-mediated gene disruption. We demonstrate that targeting the essential genes ( ERG13, PGA3, TPI1 or CDC19) leads to severe growth inhibition upon ATc induction. Still, the current set up does not allow complete killing of the cells due to system inactivation, e.g. escape from CRISPR based cutting. We studied reasons for system inactivation and substantially improved the system by simultaneous expression of two different gRNAs. Sequencing escape mutants revealed mutations in both the gRNA sequences and target genes as potential sources of system inactivation. CONCLUSIONS: This work highlights the potential of harnessing a CRISPR-based kill switch in S. cerevisiae. Cells expressing the system were able to escape growth inhibition through mutations and further optimization of the KiSS system is still needed for it to be used in various cell factory applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12934-026-02959-2.</p
Global solar energy potential forecasting through machine learning and deep learning models
No full textClimate change is accelerating at an alarming rate, 2024 has been verified as the hottest year so far, with an average temperature of 1.55 °C warmer than upstream values set in the Paris Agreement. As such, extreme weather patterns like floods, hot weather, wild fires and glaciers melts that all pose a threat of harm to ecological systems. By investing in solar technology, nations can work towards a more sustainable energy future and addressing the pressing challenge of climate change. This study exploited the global solar photovoltaic (PV) energy potential using the Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) and Temporal Convolutional Network (TCN) models implemented in Python for the period 2023 to 2050 by taking input data from 2000 to 2022. The results revealed that, the solar PV capacity was 1.23 GW in the year 2000 which then increased to 1053.12 GW by 2022. SARIMAX and TCN models estimated the future of solar PV capacity which is increased from 1291.29 GW and 1094.40 GW in 2023 to 11641.41 GW and 11577.24 GW until 2050. However, the solar PV energy was 1.03 TWh in 2000 which then increased to 1323.32 TWh in 2022. SARIMAX and TCN models forecasted the future of solar PV energy which is increased from 1935.52 TWh and 1557.92 TWh in 2023 to 14967.15 TWh and 15928.52 TWh until 2050. It is observed from the results that SARIMAX model has higher accuracy as compared with the TCN model
Towards the development of a CRISPR-Cas9 based kill switch for Saccharomyces cerevisiae
No full textBACKGROUND: Advancements in synthetic genetic circuits have enabled programmable and condition-dependent control of microbial cell growth. CRISPR-Cas9-based kill switches, genetic systems that program cells to lose viability in response to specific conditions, have recently been demonstrated for bacterial cell factories but not yet in yeast.RESULTS: In this study, we present a foundational demonstration for a CRISPR-based ki ll s witch in S accharomyces cerevisiae, CRISPR KiSS. The CRISPR KiSS employs inducible CRISPR targeting essential genes to elicit growth inhibition. The activation of the KiSS system is achieved through conditional expression of a guide RNA (gRNA) upon anhydrotetracycline (ATc) induction, thereby activating CRISPR-mediated gene disruption. We demonstrate that targeting the essential genes ( ERG13, PGA3, TPI1 or CDC19) leads to severe growth inhibition upon ATc induction. Still, the current set up does not allow complete killing of the cells due to system inactivation, e.g. escape from CRISPR based cutting. We studied reasons for system inactivation and substantially improved the system by simultaneous expression of two different gRNAs. Sequencing escape mutants revealed mutations in both the gRNA sequences and target genes as potential sources of system inactivation. CONCLUSIONS: This work highlights the potential of harnessing a CRISPR-based kill switch in S. cerevisiae. Cells expressing the system were able to escape growth inhibition through mutations and further optimization of the KiSS system is still needed for it to be used in various cell factory applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12934-026-02959-2.</p
A nodalization study on modeling the containment and reactor pool of an integral PWR
Get PDFThis study investigates the impact of nodalization of the containment and reactor pool of a small integral Pressurized Water Reactor (iPWR) using the MELCOR 2.2 code. The research focuses on Design 1 of the EU-funded SASPAM-SA project, featuring a containment partially submerged in the reactor pool. Two accident scenarios were analyzed: a Design Basis Accident (DBA) and a severe accident. The simulations were conducted with four different nodalizations: a detailed base model, a single-volume containment model, a single-volume pool model, and a single-volume model for both the containment and the pool. The results indicate that while detailed nodalization can simulate temperature stratification, its effect on the overall accident simulation results, pressures and fission product releases is limited. The findings suggest that differences between single-volume and more detailed nodalizations are relatively small, with the detailed nodalization providing slightly lower containment pressures during the DBA scenario due to more efficient heat transfer