Search Results (8,069)

The adoption of telework increased as a sustainable strategy after the COVID-19 pandemic. However, its impact on transportation and energy consumption are controversial, emphasizing the need for context-specific analysis. This research developed a System Dynamics (SD) simulation that integrated the generalized Bass Diffusion Model (BDM) and Technology Acceptance Model (TAM) to analyze telework diffusion in organizations and its influence on transport-related CO₂ emissions and energy consumption in Colombia. Internal conditions, particularly managerial attitudes and perceptions of telework performance, play a crucial role in the adoption rate. Telework adoption follows a weak S-curve pattern primarily driven by internal dynamics rather than external pressures, lagging behind the projections set by public policies and global trends. Simulations based on government data for the period 2012–2022 indicated that the number of teleworkers could reach 1.61 million by 2032, resulting in annual energy savings of approximately 1.5% and a 2% reduction in transport-related CO₂ emissions. Sustained governmental tracking of sectoral adoption and including records of household energy use will support sensitivity analysis and strengthen model robustness. The integrated SD, TAM, and BDM modeling approach identified critical factors to boost telework adoption and its environmental benefits, providing insights for sustainable organizational strategies and public policies. Full article

Hybrid halide perovskites are promising materials for optoelectronics and space applications due to their excellent light absorption, high efficiency, and light weight. However, their stability under radiation exposure remains a key challenge, especially in space environments, where high-energy particles can cause significant damage. Here, we present the effects of primary and secondary radiation on perovskite materials, using Monte-Carlo simulations with the GEANT4 toolkit. The interactions of protons, electrons, neutrons, and γ-rays with APbI3 (A = Ma, FA, Cs) perovskites under space-relevant conditions typical for low Earth orbit (LEO) were studied. The results show that different perovskite compositions respond uniquely to radiation: CsPbI₃ generates higher-energy secondary positrons, neutrons, and protons, while MAPbI₃ produces more secondary electrons under proton irradiation. Mixed-cation perovskites exhibit narrower energy distributions for secondary γ-rays, indicating material-dependent differences in radiation tolerance. These findings suggest the potential role of secondary particle generation in perovskite degradation, based on our simulations, and they emphasize the need for comprehensive modeling to improve the radiation resistance of perovskite-based technologies for space applications. Future studies should consider contributions from encapsulating materials in device structures Full article

The decarbonization of the heating sector requires the progressive transformation of district heating systems toward low-temperature and renewable-based configurations. In this context, the integration of thermal prosumers, capable of both consuming and producing heat, represents a promising solution to increase network flexibility and support sector coupling through technologies such as heat pumps. This work presents a thermo-fluid dynamic modeling framework developed to analyze the integration of a heat pump-based prosumer into an existing large-scale district heating network in Italy. The model adopts a graph-based, thermo-fluid dynamic model, combining a steady-state hydraulic formulation with a transient thermal analysis, and is complemented by a set of Key Performance Indicators for the evaluation of energy exchanges and self-sufficiency at user and network levels. Different operational configurations are analyzed, including local sharing within the distribution network and heat export to the main transport network, with and without local thermal storage. The study focuses on summer operation, when the network supplies only domestic hot water, a condition in which distributed renewable generation can play a major role in reducing central plant operation. The results highlight the potential of thermal prosumers to enhance energy autonomy and flexibility in existing district heating networks, paving the way for their evolution toward fully renewable and bidirectional systems. Full article

Anaerobic digestion (AD) is increasingly recognized as a key technology for renewable energy generation and sustainable waste management within the circular economy. However, its performance is highly sensitive to feedstock variability and environmental fluctuations, making stable operation and high methane yields difficult to sustain. Conventional monitoring and control systems, based on limited sensors and mechanistic models, often fail to anticipate disturbances or optimize process performance. This review discusses recent progress in electrochemical, optical, spectroscopic, microbial, and hybrid sensors, highlighting their advantages and limitations in artificial intelligence (AI)-assisted monitoring. The role of soft sensors, data preprocessing, feature engineering, and explainable AI is emphasized to enable predictive and adaptive process control. Various machine learning (ML) techniques, including neural networks, support vector machines, ensemble methods, and hybrid gray-box models, are evaluated for yield forecasting, anomaly detection, and operational optimization. Persistent challenges include sensor fouling, calibration drift, and the lack of standardized open datasets. Emerging strategies such as digital twins, data augmentation, and automated optimization frameworks are proposed to address these issues. Future progress will rely on more robust sensors, shared datasets, and interpretable AI tools to achieve predictive, transparent, and efficient biogas production supporting the energy transition. Full article

Magnesium metal boasts a high theoretical volumetric specific capacity and abundant reserves. Magnesium batteries offer high safety and environmental friendliness. In recent years, magnesium-ion batteries (MIBs) with Mg or Mg alloys as anodes have garnered extensive interest and emerged as promising candidates for next-generation competitive energy storage technologies. However, MIBs are plagued by issues such as sluggish desolvation kinetics and slow migration kinetics, which lead to limitations including a limited electrochemical window and poor magnesium storage reversibility. Herein, the sodium vanadium phosphate @ carbon (Na₃V₂(PO₄)₃@C, hereafter abbreviated as NVP@C) cathode material was synthesized via a sol–gel method. The electrochemical performance and magnesium storage mechanism of NVP@C in a 0.5 M magnesium bis(trifluoromethanesulfonyl)imide/ethylene glycol dimethyl ether (Mg(TFSI)₂/DME) electrolyte were investigated. The as-prepared NVP@C features a pure-phase orthorhombic structure with a porous microspherical morphology. The discharge voltage of NVP@C is 0.75 V vs. activated carbon (AC), corresponding to 3.5 V vs. Mg/Mg²⁺. The magnesium storage process of NVP@C is tentatively proposed to follow a ‘sodium extraction → magnesium intercalation → magnesium deintercalation’ three-step intercalation–deintercalation mechanism, based on the characterization results of ICP-OES, ex situ XRD, and FTIR. No abnormal phases are generated throughout the process, and the lattice parameter variation is below 0.5%. Additionally, the vibration peaks of PO₄ tetrahedrons and VO₆ octahedrons shift reversibly, and the valence state transitions between V³⁺ and V⁴⁺/V⁵⁺ are reversible. These results confirm the excellent reversibility of the material’s structure and chemical environment. At a current density of 50 mA/g, NVP@C delivers a maximum discharge specific capacity of 62 mAh/g, with a capacity retention rate of 66% after 200 cycles. The observed performance degradation is attributed to the gradual densification of the CEI film during cycling, leading to increased Mg²⁺ diffusion resistance. This work offers valuable insights for the development of high-voltage MIB systems. Full article

The Indoor Positioning System (IPS) is used to locate devices and people in smart environments. In recent years, position determination methods have evolved from simple Received Signal Strength Indicator (RSSI) measurements to more advanced approaches such as Channel State Information (CSI), Round Trip Time (RTT), and Angle of Arrival (AoA), increasingly combined with Machine Learning (ML). This article presents a systematic review of the literature on ML-based IPS using IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE), including studies published between 2020 and 2024 under the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) methodology. This study examines the techniques used to collect measurements and the ML models used, and discusses the growing use of Deep Learning (DL) approaches. This review identifies some challenges that remain for the implementation of these systems, such as environmental variability, device heterogeneity, and the need for calibration. Future research should expand ML applications to RTT and AoA, explore hybrid multimetric systems, and design lightweight, adaptive DL models. Advances in wireless standards and emerging technologies are also expected to further enhance accuracy and scalability in next-generation IPS. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

Amidst the green transition of the energy structure, as a sustainable innovation, photovoltaic pavement technology has garnered significant attention for its ability to utilize road surfaces for clean energy generation. However, roadway infrastructure must meet both load-bearing and safety requirements, making the structural performance analysis of photovoltaic pavements particularly crucial. This study focuses on load-bearing photovoltaic highways and employs finite element simulation to systematically analyze the effects of different surface transparent layer thicknesses and base sidewall thicknesses on the dynamic mechanical response of the photovoltaic pavement structure. The results indicate that increasing the surface transparent layer thicknesses significantly reduces structural deformation, stress, and strain, thereby enhancing overall stiffness and stability. Similarly, increasing the base sidewall thicknesses within a certain range also markedly improves structural performance, although the benefits tend to plateau beyond a specific thickness. Optimizing the structure can significantly enhance the load-bearing capacity and durability of photovoltaic pavements, thereby facilitating the achievement of green transportation and sustainable energy goals, and making a significant contribution to sustainable development. Full article

Small Modular Reactor (SMR) concepts have developed faster than anyone could have predicted even ten years ago. Over the next decade, it is highly likely that we will see the construction and the operation of multiple SMRs based on both third- and fourth-generation nuclear reactors. This review paper aims to evaluate the development and maturity of Small Modular Reactor technologies using the Technology Readiness Level (TRL) framework, providing both quantitative and qualitative insights into their readiness. Since a key application of SMRs is the decarbonization of the energy sector, an example of data-driven methodology has been given for selecting both the site and type of reactor in Poland. However, TRL assessment and site selection with potential use of existing infrastructure are general in nature. They are based on international standards and recommendations from the IAEA and NEA OECD. The review further examines critical issues shaping SMR deployment, with particular attention to licensing requirements and compliance with the Non-Proliferation Treaty (NPT). Full article

Paraffin-based phase change materials (PCMs) have emerged as promising candidates for thermal energy storage (TES) applications due to their high latent heat, chemical stability, and low cost. However, their inherently low thermal conductivity and the risk of leakage during melting–solidification cycles significantly limit their practical performance. To address these limitations, numerous studies have investigated composite PCMs in which paraffin is incorporated into porous supporting matrices. Among these, diatomite has garnered particular attention due to its high porosity, large specific surface area, and chemical compatibility with organic materials. Serving as both a carrier and stabilizing shell, diatomite effectively suppresses leakage and enhances thermal conductivity, thereby improving the overall efficiency and reliability of the PCM. This review synthesizes recent research on paraffin–diatomite composites, with a focus on impregnation methods, surface modification techniques, and the influence of synthesis parameters on thermal performance and cyclic stability. The mechanisms of heat and mass transport within the composite structure are examined, alongside comparative analyses of paraffin–diatomite systems and other inorganic or polymeric supports. Particular emphasis is placed on applications in energy-efficient buildings, passive heating and cooling, and hybrid thermal storage systems. The review concludes that paraffin–diatomite composites present a promising avenue for stable, efficient, and sustainable phase change materials (PCMs). However, challenges such as the optimization of pore structure, long-term durability, and large-scale manufacturing must be addressed to facilitate their broader implementation in next-generation energy storage technologies. Full article

The oxygen reduction reaction (ORR) is a pivotal electrochemical process in energy technologies and in the generation of hydrogen peroxide (H₂O₂), which serves as both an effective agent for dye degradation and a fuel in H₂O₂-based fuel cells. In this regard, a titanium (Ti) sheet was anodized to generate a TiO₂ layer, and then the oxide layer was modified with gold (presented as Au/TiO₂/Ti) via electrodeposition. The developed electrocatalyst was confirmed by X-ray photoelectron spectroscopy (XPS), which showed characteristic binding energies for Ti⁴⁺ in TiO₂ and metallic Au. In addition, the Nyquist plot verified the electrode modification process, since the diameter of the semicircular arc, corresponding to charge transfer resistance, significantly decreased due to Au deposition. Voltametric studies revealed that the TiO₂ layer with a Ti surface exhibited a good synergistic effect on Au and the ORR in a bicarbonate medium (0.1 M KHCO₃) by lowering the overpotential, enhancing current density, and boosting durability. The scan rate-dependent study of the ORR produced by the developed electrocatalyst showed a Tafel slope of 180 ± 2 mV dec⁻¹ over a scan rate range of 0.05–0.4 V s⁻¹, thereby indicating a 2e⁻ transfer process in which the initial electron transfer process was the rate-limiting step. The study also revealed that the Au/TiO₂/Ti electrode caused oxygen electro-reduction with a heterogenous rate constant (k⁰) of $4.40\times {10}^{-3}$ cm s⁻¹ at a formal potential (E⁰′) of 0.54 V vs. RHE. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

Quantum energy teleportation (QET) has been proposed to overcome the restrictions of strong local passivity (SLP) and to facilitate energy transfer in quantum systems. Traditionally, QET has only been considered under strict constraints, including the requirements that the initial state be the ground state of an interacting Hamiltonian, that Alice’s measurement commute with the interaction terms, and that entanglement be present. These constraints have significantly limited the broader applicability of QET protocols. In this work, we demonstrate that SLP can arise beyond these conventional constraints, establishing the necessity of QET in a wider range of scenarios for local energy extraction. This leads to a more flexible and generalized framework for QET. Furthermore, we introduce the concept of a “local effective Hamiltonian,” which eliminates the need for optimization techniques in determining Bob’s optimal energy extraction in QET protocols. As an additional advantage, the amount of energy that can be extracted using our new protocol is amplified to be 7.2 times higher than that of the original protocol. These advancements enhance our understanding of QET and extend its broader applications to quantum technologies. To support our findings, we implement the protocol on quantum hardware, confirming its theoretical validity and experimental feasibility. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article