Search Results (6,182)

Alkaline water electrolysis remains one of the leading and most mature technologies for large-scale hydrogen production. Its advantages stem from the use of inexpensive, earth-abundant materials and well-established industrial deployment, yet the technology continues to face challenges, including sluggish hydrogen evolution reaction (HER) kinetics and energy-efficiency limitations compared with acidic electrolysis systems. This review provides a comprehensive overview of the fundamental principles governing alkaline electrolysis, encompassing electrolyte chemistry, electrode materials, electrochemical mechanisms, and the roles of overpotentials, cell resistances, and surface morphology in determining system performance. Key developments in catalytic materials are discussed, highlighting both noble-metal and non-noble-metal electrocatalysts, as well as advanced approaches to surface modification and nanostructuring designed to enhance catalytic activity and long-term stability. Particular emphasis is placed on the emerging strategy of in situ ionic activation, wherein transition-metal ions and oxyanions are introduced directly into the operating electrolyte. These species dynamically interact with electrode surfaces under polarization, inducing real-time surface reconstruction, improving water dissociation kinetics, tuning hydrogen adsorption energies, and extending electrode durability. Results derived from polarization measurements, electrochemical impedance spectroscopy, and surface morphology analyses consistently demonstrate that ionic activators, such as Ni–Co–Mo systems, significantly increase the HER performance through substantial increase in surface roughness and increased intrinsic electrocatalytic activity through synergy of d-metals. By integrating both historical context and recent research findings, this review underscores the potential of ionic activation as a scalable and cost-effective way toward improving the efficiency of alkaline water electrolysis and accelerating progress toward sustainable, large-scale green hydrogen production. Full article

Pt-rare earth metal (Pt-RE) alloys are considered to be one of the most promising electrocatalysts for producing oxygen reduction reactions (ORRs) due to their compressively strained Pt overlayer and their exceptional negative-alloy formation energies, which result in excellent activity and stability. However, there are still great challenges in the chemical synthesis of Pt-RE nanoalloys. Herein, we report a simple method employing the nanopores of porous carbon as nanoreactors to synthesize a Pt₅La nanoalloy. The Pt₅La alloy nanoparticles are embedded in porous carbon (Pt₅La@C) with a particle size of around 1–3 nm and also exhibit a very narrow size distribution because of the confined-space effect. The as-prepared Pt₅La@C nanoalloy exhibits highly efficient ORR performance with a half-wave potential of 0.912 V in 0.1 M HClO₄, which is 56 mV higher than that of a commercial Pt/C catalyst. Moreover, it achieves an improved intrinsic activity of 0.69 mA cm⁻² and, a mass activity of 0.42 A mg_Pt⁻¹ at 0.90 V. In addition, it also delivers a very stable lifespan performance, with negligible decay in half-wave potential after accelerated stress testing for 10,000 cycles. This work also provides a new method for the development of promising Pt-RE nanoalloys with ultrasmall nanoparticles with a very narrow size distribution for various efficient energy-conversion devices. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

Sugarcane (Saccharum spp.) is a globally important crop for sugar and bioenergy production. However, genetic improvement through conventional breeding is constrained by long breeding cycles, low genetic gain, and considerable operational complexity arising from its highly allopolyploid and aneuploid genome. With the increasing global demand for sustainable food and renewable energy, sugarcane breeding programs must accelerate the development of high-yielding, stress-tolerant cultivars through the integration of advanced biotechnological tools with traditional breeding approaches. Recent advances in genetic engineering, genomic selection (GS), and high-throughput omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and phenomics, have substantially enhanced the efficiency of trait improvement related to growth, development, yield, and stress resilience. The integration of multi-omics data enables the dissection of regulatory networks linking genotype to phenotype, improves predictive accuracy, and provides deeper insights into the molecular mechanisms underlying complex traits. These integrative approaches support more informed selection decisions and accelerate genetic gain in sugarcane breeding programs. This review synthesizes recent technological developments and their practical applications in sugarcane improvement. It highlights the strategic implementation of transgenic and genome-editing technologies, genomic selection, and multi-omics integration to enhance yield potential and resistance to biotic and abiotic stresses, thereby contributing to sustainable sugarcane production and global food and bioenergy security. Full article

The ever-increasing need for energy-efficient implementation of AI algorithms has driven the research community towards the development of many hardware architectures and frameworks for AI. A lot of work has been presented around FPGAs, while more sophisticated architectures like CGRAs have also been at the center. However, AI ecosystems are isolated and fragmented, with no standardized way to compare different frameworks with detailed Power–Performance–Area (PPA) analysis. This paper bridges the gap by presenting a unified, fully open-source hardware-aware AI acceleration pipeline that enables seamless deployment of neural networks on both FPGA and CGRA architectures. Built around the Brevitas quantization framework, it supports two distinct backend flows: FINN for high-performance dataflow accelerators and CGRA4ML for low-power coarse-grained reconfigurable designs. To facilitate this, a model translation layer from QONNX to QKeras is also introduced. To demonstrate its effectiveness, we use an autoencoder model for anomaly detection in wind turbines. We deploy our accelerated models on the AMD’s ZCU104 and benchmark it against a Raspberry Pi. Evaluation on a realistic cyber–physical testbed shows that the hardware-accelerated solutions achieve substantial performance and energy-efficiency gains—up to 10× and 37× faster inference per flow and over 11× higher efficiency—while maintaining acceptable reconstruction accuracy. Full article

South Africa’s manufacturing sector, a driving force for sustainable development, faces a profound challenge in decarbonizing without deindustrializing. This study provides an optimized, scenario-based assessment of the sector explicitly aligned with its Just Energy Transition Partnership (JETP) objectives. A novel framework is applied, integrating an extended Kaya–Logarithmic Mean Divisia Index (Kaya–LMDI) decomposition with scenario forecasting and Genetic Algorithm (GA) optimization. The decomposition disaggregates a conventional carbon intensity (CI) driver to include Electrification Share (ELE), Renewable Share (REN), and a newly defined Residual Carbon Factor (RCF) that captures direct fossil fuel use for industrial process heat. Historical analysis (2002–2022) shows that emissions growth was primarily driven by the RCF (224.1 MtCO₂, 160%) and Economic Activity (187.5 MtCO₂, 134%), partly offset by gains in Energy Intensity (−141.8 MtCO₂, 101.35%) and REN (−202.2 MtCO₂, −144.53%). Carbon emissions projections to 2040 reveal a critical sustainability trilemma: the Just Transition accelerated scenario (JTAS), despite achieving rapid renewable deployment, increases emissions by 469% as economic growth overwhelms decarbonization efforts. Conversely, the mathematically optimal (GA) pathway achieves a 90.8% reduction but only through structural contraction that implies socially unsustainable deindustrialization. This tension exposes the systemic limits of incremental decarbonization and underscores that a truly sustainable pathway requires transcending this binary choice by directly addressing the fossil fuel substrate of industrial production. Full article

Against the backdrop of China’s “dual carbon” targets, the energy transition is accelerating. However, the expansion of onshore renewables is often constrained by land scarcity. Offshore areas thus present a promising alternative. In this study, high-resolution wind field data from 1995 to 2024 were generated using the WRF model driven by ERA5 reanalysis, enabling a 30-year spatiotemporal assessment of offshore wind power density (at 160 m hub height) and photovoltaic potential (PVP) across China’s four major seas—the Bohai Sea, Yellow Sea, East China Sea, and South China Sea. The results show clear spatial and seasonal patterns: solar PV potential decreases from south to north, with the South China Sea exhibiting the highest and most stable annual average PVP (16–18%) and summer peaks exceeding 25%. Wind energy resources are spatially heterogeneous; the East China Sea and Taiwan Strait are identified as the richest zones, where wind power density frequently reaches 800–1800 W/m² during autumn and winter. Importantly, a pronounced seasonal complementarity is observed: wind peaks in autumn/winter while solar peaks in spring/summer at representative coastal sites. This study provides, for the first time, a long-term, integrated assessment of both offshore wind and solar resources over all four Chinese seas, offering quantitative data and a scientific basis for differentiated marine energy planning, optimized siting, and the design of wind–solar hybrid systems. Full article

Convolutional Neural Networks (CNNs) generally suffer from excessive computational overhead, high resource consumption, and complex network structures, which severely restrict the deployment on microprocessor chips. Existing related accelerators only have an energy efficiency ratio of 2.32–6.5925 GOPs/W, making it difficult to meet the low-power requirements of embedded application scenarios. To address these issues, this paper proposes a low-power and high-energy-efficiency CNN accelerator architecture based on a central processing unit (CPU) and an Application-Specific Integrated Circuit (ASIC) heterogeneous computing architecture, adopting an operator-fused systolic array algorithm with the YOLOv5n target detection network as the application benchmark. It integrates a 2D systolic array with Conv-BN fusion technology to achieve deep operator fusion of convolution, batch normalization and activation functions; optimizes the RISC-V core to reduce resource usage; and adopts a locking mechanism and a prefetching strategy for the asynchronous platform to ensure operational stability. Experiments on the Nexys Video development board show that the architecture achieves 20.6 GFLOPs of computational performance, 1.96 W of power consumption, and 10.46 GOPs/W of energy efficiency ratio, which is 58–350% higher than existing mainstream accelerators, thus demonstrating excellent potential for embedded deployment. Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

The capability of wind turbines (WTs) to provide frequency response is crucial for future power systems with high wind power penetration. Existing strategies primarily focus on mitigating initial and secondary frequency drop (SFD), often overlooking the adverse effects of rotor speed recovery on turbine safety and sustained grid support. Moreover, the lack of a dynamic linkage between the frequency support stage (FSS) and speed recovery stage (SRS) impedes multi-objective coordination encompassing initial drop suppression, SFD mitigation, and rapid rotor speed recovery. To address these gaps, this paper proposes a two-stage coordinated control strategy. In the FSS, the frequency regulation coefficients (K_p and K_d) are adaptively adjusted based on available kinetic energy, ensuring its rational release. Subsequently, the switching timing from FSS to SRS is optimized using these coefficients to suppress the SFD and accelerate recovery. Finally, a fuzzy logic-based PI controller dynamically governs the SRS to restore rotor speed efficiently while further alleviating the SFD. Simulation results confirm the effectiveness of the proposed strategy under two wind speeds. It improves the initial frequency nadir by up to 0.197 Hz over no frequency control, reduces the secondary frequency drop by as much as 0.106 Hz compared to the stepwise method, and accelerates rotor speed recovery by over 30%, quantitatively validating its superior coordinated performance. Full article

Reliability of medium-voltage (MV) cable systems, for distribution to industrial and renewable plants, is becoming an issue for various reasons, among which are increased global aging, unconventional voltage waveforms, and insufficient commissioning tests. The major component undergoing premature failures is splices, and most of those failures can be associated with flaws in installation, commissioning and, in general, workmanship. One of the topics of an ongoing Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) project, GOPHURRS, is, indeed, to increase splice reliability through simpler design and installation procedures, which can minimize assembly and aging risks. This paper deals with design and testing techniques, which can allow scaling up to MV, a type of splice design and assembly that has been successful in low-voltage (LV) applications. A new design paradigm, the three-leg approach, is applied for the first time to LV splices to evaluate their operation likelihood and reliability up to 30 kV nominal voltage, allowing intrinsic life to reach the specified target (e.g., 30 years at a failure probability of 5%) and preventing extrinsic aging, namely, partial discharge occurrence. Design principles and validation, including accelerated aging and forensic observations, are presented and discussed. Full article

The global energy landscape is transitioning towards cleaner solutions, with hydrogen emerging as a key energy source. To unlock hydrogen’s potential, it is crucial to prioritize the development of a more efficient, cost-effective, and environmentally friendly production process. Enhancing the efficiency and scalability of these technologies will not only reduce their environmental impact but also accelerate the adoption of hydrogen as a viable alternative energy solution, fostering a cleaner and more sustainable future. This paper presents a study on simulating a heat recovery system in an alkaline electrolyser consisting of 30 cells, which integrates a plate heat exchanger to preheat the water entering the system, and assessing how it affects efficiency. The study uses a thermal model, employing the concept of lumped thermal capacitance, to analyze the impact of the heat recovery system utilization on the overall performance of the electrolyser. MATLAB/Simulink was used to simulate and provide a detailed visualization of how recovery systems affect the electrolyser’s efficiency. The results of the simulations confirmed that incorporating a heat recovery system significantly improves the efficiency of alkaline electrolysers up to 8%. The study provides a promising outlook for the future of hydrogen production, emphasizing the potential of waste heat recovery systems to make green hydrogen production more viable and sustainable. Full article

Rapid urbanization in China has driven annual food waste production to 130 million tons, posing severe environmental challenges for anaerobic digestate management. To resolve trade-offs among drying efficiency, resource recovery (fertilizer/fuel), and carbon neutrality by optimizing the biodried product (BDP) recycling ratio (0–15%), six BDP treatments were tested in 60 L bioreactors. Metrics included drying kinetics, product properties, and environmental–economic trade-offs. The results showed that 12% BDP achieved a peak temperature integral (514.13 °C·d), an optimal biodrying index (3.67), and shortened the cycle to 12 days. Furthermore, 12% BDP yielded total nutrients (N + P₂O₅ + K₂O) of 4.19%, meeting the NY 525-2021 standard in China, while ≤3% BDP maximized fuel suitability with LHV > 5000 kJ·kg⁻¹, compliant with CEN/TC 343 RDF standards. BDP recycling reduced global warming potential by 27.3% and eliminated leachate generation, mitigating groundwater contamination risks. The RDF pathway (12% BDP) achieved the highest NPV (USD 716,725), whereas organic fertilizer required farmland subsidies (28.57/ton) to offset its low market value. A 12% BDP recycling ratio optimally balances technical feasibility, environmental safety, and economic returns, offering a closed-loop solution for global food waste valorization. Full article

In the context of the digital twin engineering of large smart hub seaports, port path planning faces more complex challenges, such as efficient logistics scheduling, unmanned transportation, coordination of port automation facilities, and rapid response to complex dynamic environments. Particularly in applications like robotic inspection, how to effectively plan paths, improve inspection efficiency, and ensure that robots complete tasks within their limited energy capacity has become a key issue in the design and realization of digital and intelligent seaport systems. To address these challenges, a path planning algorithm based on an improved Rapidly-exploring Random Tree (RRT) is proposed, considering the complexity and dynamics of the port’s digital twin environment. First, by optimizing the search strategy of the algorithm, the flexibility and adaptability of path planning can be enhanced, allowing it to better accommodate changes in the environment within the digital twin model. Secondly, an appropriate heuristic function is constructed for the digital twin seaport environment, which can effectively accelerate the convergence speed of the algorithm and improve path planning efficiency. Finally, trajectory smoothing techniques are applied to generate executable paths that comply with the robot’s motion constraints, enabling more efficient path planning in practical operations. To validate the feasibility of the proposed method, a combination of virtual and real digital twin environments is used, comparing the path planning results of the improved RRT algorithm with those of the traditional RRT algorithm. Experimental results show that the proposed improved algorithm outperforms the traditional RRT algorithm in terms of sampling frequency, planning time, path length, and smoothness, further validating the feasibility and advantages of this algorithm in the application of intelligent seaport digital twin engineering. Full article

The accelerating low-carbon transition requires decision-support approaches capable of addressing complex, interdependent sustainability challenges across multiple sectors. While Multi-Criteria Decision-Making (MCDM) techniques are gaining popularity in assessing sustainability within energy and agricultural systems, their current application remains fragmented, sector-focused, and poorly aligned with the fundamental system characteristics of uncertainty, circularity, and social equity. This Perspective employs a systematized conceptual analysis to integrate different MCDM techniques, methodological trends, and integration challenges in energy and agricultural systems. Through a literature review, this work provides a critical view of the predominant structural deficiencies, which stem from methodological isolation, the use of disparate and heterogeneous datasets, ad hoc treatment of uncertainty, and the lack of incorporation of the circular economy (CE) and equity dimensions in the analysis. Given the presence of multifunctionality, circularity, climate sensitivity, and strong social characteristics, the analysis underscores that agriculture is a prime candidate to serve as a system-level testbed for the development of integrated MCDM frameworks. Based on this analysis, the paper articulates the fundamental characteristics of next-generation MCDM frameworks that are cross-sectoral, flexible, adaptive, uncertainty-resilient, and actionable. In doing so, it prioritizes integrated approaches that combine MCDM with life cycle assessment (LCA), data analytics, and nexus modelling. This paper stresses that structural deficiencies need to be addressed for MCDM to evolve from sectoral and fragmented analytical frameworks to cohesive decision-support systems that can guide energy and agricultural systems transitions towards equity, circularity, and climate change adaptation. As a perspective, this paper does not aim to provide empirical validation but instead articulates conceptual design principles for next-generation MCDM frameworks that integrate uncertainty, circularity, and social equity across energy and agricultural systems. Full article