Search Results (6,676)

This study reports the synthesis and comprehensive spectroscopic characterization of 4-indolylcyanamide (4ICA), a novel indole-derived infrared (IR) probe designed for assessing local microenvironments in biological systems. 4ICA was synthesized via a two-step procedure with an overall yield of 43%, and its structure was confirmed using high-resolution mass spectrometry and ¹HNMR. Fourier Transform Infrared (FTIR) spectroscopy revealed that the cyanamide group stretching vibration of 4ICA exhibits exceptional solvent-dependent frequency shifts, significantly greater than those of conventional cyanoindole probes. A strong linear correlation was observed between the vibrational frequency and the combined Kamlet–Taft parameter, underscoring the dominant role of solvent polarizability and hydrogen bond acceptance in modulating its spectroscopic behavior. Quantum chemical calculations employing density functional theory (DFT) with a conductor-like polarizable continuum model (CPCM) provided further insight into the solvatochromic shifts and suppression of Fermi resonance in high-polarity solvents such as DMSO. Additionally, IR pump–probe measurements revealed short vibrational lifetimes (~1.35 ps in DMSO and ~1.13 ps in ethanol), indicative of efficient energy relaxation. With a transition dipole moment nearly twice that of traditional nitrile-based probes, 4ICA demonstrates enhanced sensitivity and signal intensity, establishing its potential as a powerful tool for site-specific environmental mapping in proteins and complex biological assemblies using nonlinear IR techniques. Full article

Deep retrofits are one of the few pathways to decarbonize the existing building stock while simultaneously improving climate resilience. These retrofits improve insulation, airtightness, and mechanical equipment efficiency. NRCan’s Prefabricated Exterior Energy Retrofit (PEER) project developed prefabricated building envelope retrofit solutions to enable net-zero performance. The PEER process was demonstrated on two different pilot projects completed between 2017 and 2023. In 2024, in partnership with industry partners, NRCan developed new low-carbon retrofit panel designs and completed a pilot project to evaluate their performance and better understand resiliency and occupant comfort post-retrofit. The Low-Carbon Climate-Resilient (LCCR) Living Lab pilot retrofit was completed in 2024 in Ottawa, Canada, using low-carbon PEER panels. This paper outlines the design and construction for the pilot, including panel designs, the retrofitting process, and post-retrofit building and envelope commissioning. The retrofitting process included the design and installation of new prefabricated exterior retrofitted panels for the walls and the roof. These panels were insulated with cellulose, wood fibre, hemp, and chopped straw. During construction, blower door testing and infrared imaging were conducted to identify air leakage paths and thermal bridges in the enclosure. The retrofit envelope thermal resistance is RSI 7.0 walls, RSI 10.5 roof, and an RSI 3.5 floor with 0.80 W/m²·K U-factor high-gain windows. The measured normalized leakage area @10Pa was 0.074 cm²/m². The net carbon stored during retrofitting was over 1480 kg CO₂. Monitoring equipment was placed within the LCCR to enable the validation of hygrothermal models for heat, air, and moisture transport, and energy, comfort, and climate resilience models. Full article

This paper presents an advanced radio frequency (RF)–direct current (DC) power conversion architecture based on a multistage Cockcroft–Walton topology. The proposed design achieves an enhanced voltage conversion ratio while maintaining superior RF-DC conversion efficiency under low input power conditions. To address the inherent limitations of cascading Cockcroft–Walton topologies with class-F load networks, a novel ground plane isolation technique was developed, which utilizes the reverse-side metallization of the circuit board. A 5.8 GHz two-stage Cockcroft–Walton voltage multiplier rectifier was fabricated and characterized. Measurement results demonstrate that the circuit achieves a maximum output voltage of 7.4 V and a peak conversion efficiency of 70.5% with an input power of only 30 mW, while maintaining stable performance across varying load conditions. A comparison with a two-stage Dickson rectifier reveals that the Cockcroft–Walton rectifier exhibits superior output voltage and conversion efficiency. The proposed architecture delivers significant improvements in power conversion efficiency and voltage multiplication capability compared to conventional designs, establishing a new benchmark for low-power wireless energy harvesting applications. Full article

This study introduces a novel adaptive Mechanical Wave Energy Converter (MWEC) designed to efficiently capture nearshore wave energy for sustainable electricity generation along the southeast surf coast of NEOM (135° longitude). The MWEC system features a polyvinyl chloride (PVC) cubic buoy integrated with a mechanical power take-off (PTO) mechanism, optimized for deployment in shallow waters for a depth of around 1 m. Three buoy volumes, V1: 6000 cm³, V2: 30,000 cm³, and V3: 72,000 cm³, were experimentally evaluated under consistent PTO and spring tension configurations. The findings reveal a direct relationship between buoy volume and force output, with larger buoys exhibiting greater energy capture potential, while smaller buoys provided faster and more stable response dynamics. The energy retention efficiency of the buoy–PTO system was measured at 20% for V1, 14% for V2, and 10% for V3, indicating a trade-off between responsiveness and total energy capture. Notably, the largest buoy (V3) generated a peak power output of 213 W at an average wave amplitude of 65 cm, confirming its suitability for high-energy conditions along NEOM’s surf coast. In contrast, the smaller buoy (V1) performed more effectively during periods of reduced wave activity. Wave climate data collected during November and December 2024 support a hybrid deployment strategy, utilizing different buoy sizes to adapt to seasonal wave variability. These results highlight the potential of modular, wave-adaptive mechanical systems for scalable, site-specific renewable energy solutions in coastal environments like NEOM. The proposed MWEC offers a promising path toward low-cost, low-maintenance wave energy harvesting in shallow waters, contributing to Saudi Arabia’s sustainable energy goals. Full article

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) power outputs through scenario-based analysis. Considering the diversity of generation entities and their complex interest demands, a bi-level capacity optimization framework based on game theory is proposed. In the upper-level framework, a game-theoretic method is designed to analyze the multi-agent decision-making process, and the objective function of capacity allocation for multiple entities is established. In the lower-level framework, multi-objective optimization is performed on utility functions and node voltage deviations. The Nash equilibrium of the non-cooperative game and the Shapley value of the cooperative game are solved to study the differences in the capacity allocation, economic benefits, and power supply stability of the combined power generation system under different game modes. The case study results indicate that under the cooperative game mode, when the four generation entities form a coalition, the overall system achieves the highest supply stability, the lowest carbon emissions at 30,195.29 tons, and the highest renewable energy consumption rate at 53.93%. Moreover, both overall and individual economic and environmental performance are superior to those under the non-cooperative game mode. By investigating the capacity configuration and joint operation strategies of the combined generation system, this study effectively enhances the enthusiasm of each generation entity to participate in the energy market; reduces carbon emissions; and promotes the development of a more efficient, environmentally friendly, and economical power generation model. Full article

Public educational buildings, particularly schools, are often overlooked in energy efficiency initiatives, despite their potential for substantial energy and cost savings. This study presents an integrative, measurement-informed, calibrated model-based approach for assessing and enhancing energy performance in elementary schools located in Israel’s hot-arid climate. By combining multiscale environmental monitoring with a rigorously calibrated Energy Plus simulation model, the study evaluates the impact of three demand-side management (DSM) strategies: night ventilation, external envelope insulation, and a combination of the two. Quantitative results show that night ventilation reduced average indoor temperatures by up to 3.3 °C during peak occupancy hours and led to daily energy savings of 10–15%, equating to approximately 1500–2200 kWh annually per classroom. Envelope insulation further reduced diurnal temperature fluctuations from 7.75 °C to 1.0 °C and achieved an additional 9% energy savings. When combined, the two strategies yielded up to 20% energy savings and improved thermal comfort. The findings provide a transferable framework for evaluating retrofitting options in public buildings, offering actionable insights for policymakers and facility managers aiming to implement scalable, cost-effective energy interventions in educational environments. Full article

Higher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in Türkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO₂e (1.19 tCO₂e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions. Full article

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the $H_0$ tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted. Full article

In this study, nanostructured ferberites (FeWO₄) were synthesized via hydrothermal routes in an acidic medium. It was then investigated as an efficient photocatalyst for degrading organic dye molecules, with methylene blue (MB) as a model pollutant. The formation mechanism of ferberite revealed that the physical form of the precursor, FeSO₄·7H₂O, acts as a decisive factor in morphological evolution. Depending on whether it is in a solid or dilute solution form, two distinct nanostructures are produced: nanoplatelets and self-organized microspheres. Both structures are composed of stoichiometric FeWO₄ (Fe: 49%, W: 51%) in a single monoclinic phase (space group P2/c:1) with high purity and crystallinity. The p-type semiconductor behavior was confirmed using Mott–Schottky model and the optical analysis, resulting in small band gap energies (≈1.7 eV) favoring visible absorption light. Photocatalytic tests under simulated solar irradiation revealed rapid and efficient degradation in less than 10 min under near-industrial conditions (pH 5). This was achieved using only a ferberite catalyst and a low concentration of H₂O₂ (4 mM) without additives, dopants, or artificial light sources. Advanced studies based on photocurrent measurements, trapping and stability tests were carried out to identify the main reactive species involved in the photocatalytic process and better understanding of photodegradation mechanisms. These results demonstrate the potential of nanostructured FeWO₄ as a sustainable and effective photocatalyst for water purification applications. Full article

The cement industry accounts for approximately 7–8% of global CO₂ emissions, primarily due to energy-intensive clinker production and limestone calcination. With cement demand continuing to rise, particularly in emerging economies, decarbonization has become an urgent global challenge. The objective of this study is to systematically map and synthesize existing evidence on technological pathways, policy measures, and economic barriers to four core decarbonization strategies: clinker substitution, energy efficiency, alternative fuels, as well as carbon capture, utilization, and storage (CCUS) in the cement sector, with the goal of identifying practical strategies that can align industry practice with long-term climate goals. A scoping review methodology was adopted, drawing on peer-reviewed journal articles, technical reports, and policy documents to ensure a comprehensive perspective. The results demonstrate that each mitigation pathway is technically feasible but faces substantial real-world constraints. Clinker substitution delivers immediate reduction but is limited by SCM availability/quality, durability qualification, and conservative codes; LC₃ is promising where clay logistics allow. Energy-efficiency measures like waste-heat recovery and advanced controls reduce fuel use but face high capital expenditure, downtime, and diminishing returns in modern plants. Alternative fuels can reduce combustion-related emissions but face challenges of supply chains, technical integration challenges, quality, weak waste-management systems, and regulatory acceptance. CCUS, the most considerable long-term potential, addresses process CO₂ and enables deep reductions, but remains commercially unviable due to current economics, high costs, limited policy support, lack of large-scale deployment, and access to transport and storage. Cross-cutting economic challenges, regulatory gaps, skill shortages, and social resistance including NIMBYism further slow adoption, particularly in low-income regions. This study concludes that a single pathway is insufficient. An integrated portfolio supported by modernized standards, targeted policy incentives, expanded access to SCMs and waste fuels, scaled CCUS investment, and international collaboration is essential to bridge the gap between climate ambition and industrial implementation. Key recommendations include modernizing cement standards to support higher clinker replacement, providing incentives for energy-efficient upgrades, scaling CCUS through joint investment and carbon pricing and expanding access to biomass and waste-derived fuels. Full article

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids. Full article

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation—including noise, bias, scale factor, power consumption, and drift—demonstrates the device’s reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions. Full article

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

The acceleration of climate change and increasing weather-related disasters require more active utilization of renewable energy. To maximize the use of renewable energy, energy storage is an essential part. Liquid piston gas compressors have recently drawn attention because of their applicability to compressed air-based energy storage. Aqueous foam can be used to enhance the efficiency of liquid piston gas compression by boosting heat transfer. To validate the effectiveness of the combination of liquid piston and aqueous foam in a multi-stage compression system, which can contribute to higher efficiency, the present work performed experimental study at various pressure levels. Compressions were performed with and without aqueous foam at three different initial pressure levels of 1, 2, and 3 bars. For each cycle of compression, a pressure ratio of 2 was used, and the impact of pressure levels on compression efficiency was measured. With the use of foam, isothermal efficiencies of 91.4, 88.2, and 86.6% were observed at 1, 2, and 3 bar(s), which improved by 2.2, 2.1, and 1.3% compared to the baseline compressions. To identify the cause of the effectiveness variations, the volume changes in the foam at the different pressure levels were visually compared. In higher-pressure tests, a significant reduction in the foam amount was observed, and this change may contribute to the decreased effectiveness of the technique. Full article