Search Results (1,697)

Boron carbide (B₄C) ceramics suffer from poor sinterability and inherent brittleness, which severely limit their engineering applications. In this work, B₄C–graphene nanoplatelet (GNP) composites were fabricated by hot pressing using heterogeneously co-precipitated powder mixtures, with cetyltrimethyl ammonium bromide (CTAB) as a surfactant for achieving uniform dispersion of GNPs within the B₄C matrix. The formation mechanisms of B₄C–GNP hybrids were systematically elucidated. The results show that CTAB endows GNPs with positive charges, enabling electrostatic co-precipitation with negatively charged B₄C particles to construct layered hybrid architectures. The GNP content has a significant modulation effect on the microstructure and mechanical properties of B₄C composites. A maximum relative density of 99.65%, Vickers hardness of 33.5 GPa, and flexural strength of 488 MPa were obtained at 1 wt% GNPs, while the fracture toughness reached a peak value of 4.89 MPa·m^1/2 at 2 wt% GNPs, representing a 63.5% improvement over monolithic B₄C. The enhanced fracture toughness is attributed to multiple toughening mechanisms, including crack deflection, crack bridging, GNP pull-out, step-like fracture, and zigzag crack propagation. This study provides a feasible strategy for preparing uniformly dispersed ceramic–graphene composites with balanced mechanical properties. Full article

With the wide-scale deployment of battery packs in a variety of applications, some life cycle challenges are coming to light. These challenges include extending their operational life as a pack given their increasing cell-level imbalances and repurposing their cells into new battery packs to give them a second life, e.g., in grid storage. This paper presents a new circuit topology addressing both issues using active (controlled and nondissipative) cell-to-cell balancing for online operation, i.e., while the battery energy storage system is in use. The proposed circuit design is fast and safe for balancing, relying on current control to target each individual cell’s maximum charging and discharging current, while taking into account the pack current. The design has the lowest number of switches and circuit components compared to the state-of-the-art techniques, and is also flexible, allowing for the addition or replacement of cells in series. Its practical hierarchical control system enables speed, reliability, and reconfigurable limits in real-time operation on the individual cells. Experimental validation is carried out on homogeneous and heterogeneous packs in online operation, and the results demonstrate the speed and efficacy of the proposed technique. Full article

This single-site industrial case study compares a leased diesel shunting locomotive with a battery-electric shunting locomotive used for the same class of in-plant railway tasks at the Seza Cement Plant. The evidence base comprises plant leasing and fuel records, equipment specifications, site-reported electricity indicators, operator-reported operational observations, direct CO₂ calculations, and documented occupational safety and health (OSH) functions; it is not a controlled or statistically replicated time–motion experiment. The diesel system incurred a monthly lease cost of USD 10,000 and consumed approximately 1800 L/month, equivalent to 21,600 L/year. Cross-checking the direct CO₂ calculation with 2.692 and 2.683 kg CO₂/L factors gives 58.1 and 58.0 t CO₂/year, respectively. The approximately 24-month payback is treated as a plant-reported investment indicator and evaluated through a normalized sensitivity model because disaggregated costs for locomotive purchase, charging infrastructure, battery replacement, and historical maintenance are not available in the case-study dataset. Operational evidence is reported descriptively: the 20–40% reduction in task time is an operator-reported range rather than a statistical mean; the 7–9 min value refers to the complete 10-wagon weighing maneuver; and 25 loaded wagons (approximately 1450 t) represents the maximum documented field movement rather than a manufacturer-rated capacity. A force-balance check shows that this maximum movement is feasible only if total equivalent resistance remains below approximately 5.41 N/kN, using the 77 kN catalog tractive effort as an upper bound. The battery-electric locomotive produces no local exhaust emissions at the point of use and incorporates SIL 2 remote-control functions, a deadman function, emergency-stop controls, camera support, lighting, and warning systems; these features indicate risk-control capability but do not constitute a measured accident-rate reduction. The study therefore contributes facility-scale, evidence-bounded information for low-speed, repetitive industrial shunting within a defined operating area rather than a general proof of battery-electric superiority across railway applications. Full article

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

The decarbonization of maritime transportation requires shipboard energy systems to coordinate conventional generators, renewable energy sources, energy storage devices, and thermal energy units under voyage-dependent operating constraints. This paper develops a configurable hybrid multienergy ship system for coordinated electrical and thermal energy scheduling. The proposed framework functionally separates the propulsion subsystem from the service and thermal subsystem while retaining system-level coordination among photovoltaic generation, wind generation, diesel generators, micro gas turbines, energy storage batteries, and thermal energy units. A convolutional neural network is employed to provide short-term photovoltaic power forecasts for day-ahead scheduling. The resulting scheduling problem simultaneously considers voyage completion, power balance, equipment operating limits, ramp-rate constraints, battery charging and discharging restrictions, operating costs, and pollutant emission treatment costs. The nonlinear operating logic is reformulated as a mixed-integer optimization problem and solved using CPLEX. A representative coastal voyage case study is used to evaluate the proposed framework. The results demonstrate that the method can coordinate multiple shipboard energy sources, satisfy the prescribed electrical and thermal demands, and provide a set of Pareto-optimal solutions describing the trade-off between operating cost and emission-related cost. The proposed framework provides a system-level scheduling approach for supporting the economic and low-carbon operation of hybrid multienergy ships under increasingly stringent maritime emission reduction requirements. Full article

Upgrading biogas to pipeline-quality methane requires the efficient removal of CO₂, yet conventional physicochemical routes remain energy-intensive. Coupling a CO₂-selective membrane with microalgal photosynthetic fixation offers a green alternative, but is constrained by the low CO₂/CH₄ selectivity of common membranes and the poor adhesion of microalgae to hydrophobic membrane surfaces. Here, a dual-functional composite membrane was developed that simultaneously provides CO₂/CH₄ sieving and a biocompatible interface for microalgal attachment, and was integrated into a microalgae membrane bioreactor (MMBR). A cellulose acetate mixed-matrix membrane incorporating polyethyleneimine-grafted ZIF-8 (CA/PZIF-8(15)) achieved a mixed-gas CO₂ permeability of 122.3 Barrer and a CO₂/CH₄ selectivity of 41.17. An ionic-liquid-modified chitosan (CS/IL) coating, first optimized on a commercial flat-sheet polyethersulfone (PES) membrane used as a model surface for the adhesion study, reversed the surface charge from −30.8 to +3.75 mV, lowered the water contact angle to 51.2°, and increased the day-7 adhesion of Scenedesmus obliquus by ~108%. Transferring the coating onto CA/PZIF-8(15) further raised the permeability to 138 Barrer and the selectivity to 57.31, placing the composite above the 2008 Robeson upper bound. In the MMBR, CH₄ purity reached 95.13% after 48 h; a mass balance on the recirculating gas volume indicated that essentially all of the CO₂ removed from the gas phase permeated the membrane, of which an estimated 2% was fixed into microalgal biomass while the remainder was retained in the liquid phase. This work offers a membrane-design strategy that bridges gas-separation functionality and microalgal carbon fixation for sustainable biogas upgrading. Full article

Suppressing thermally driven morphological evolution while preserving efficient charge transport pathways remains a critical challenge for improving the long-term stability of organic photovoltaics (OPVs). Herein, a soft polymeric matrix strategy based on gel-related soft material concepts is demonstrated for stabilizing bulk heterojunction (BHJ) morphology and simultaneously improving the efficiency and thermal durability of OPVs. The incorporation of an optimal 5 wt% polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) as a soft polymeric matrix component into a PM6:Y6 blend modulates the nanoscale morphology and local packing characteristics of the acceptor phase. These changes improve charge-transport balance and charge collection, increasing the power conversion efficiency (PCE) from 14.27% to 15.22%, corresponding to a 6.7% relative enhancement over the control device. More importantly, after 10 days of thermal aging at 85 °C, the SEBS device retains 87.1% of its initial PCE, compared with 72.5% for the control device. Complementary morphological and spectroscopic analyses reveal suppressed thermally induced structural evolution and aggregation in the SEBS-containing films. These findings demonstrate that a gel-related soft polymeric matrix can regulate BHJ organization and mitigate thermally driven morphological evolution, providing a simple strategy for addressing the efficiency–stability trade-off and realizing thermally robust OPVs. Full article

Smart charging stations require mobile charging robots to respond to dynamically arriving charging requests with heterogeneous priorities, varying travel costs, and uneven workloads while maintaining online scheduling feasibility. Conventional single-layer approaches often optimize task assignment or route ordering separately, which limits their ability to coordinate allocation quality, route efficiency, and workload regulation under real-time constraints. This study proposes a hierarchical improved consensus-based bundle algorithm–ant colony optimization (ICBBA-ACO) framework for dynamic multi-robot task allocation. The upper ICBBA layer combines deterministic task clustering, intra-cluster greedy bundling, conflict resolution, and feedback-guided workload-aware reassignment, while the lower ACO layer refines the visiting order of unstarted tasks under fixed ownership using the same normalized four-objective scheduling cost. Complete decision time is evaluated separately against a $200\mathrm{ms}$ online requirement, and estimated motion energy is retained only as a distance-derived auxiliary indicator. In a five-method comparison over 100 paired scenarios, ICBBA-ACO achieves a mean composite objective of $J=0.663052$, a mean decision time of $33.07\mathrm{ms}$, and 100% deadline compliance. GA-MRTA obtains a lower unconstrained mean objective of $J=0.615790$, but requires approximately $2199.30\mathrm{ms}$ on average and satisfies the $200\mathrm{ms}$ requirement in only $8.89\%$ of the evaluated updates. Thus, ICBBA-ACO provides the lowest mean objective among the compared methods that maintain full deadline compliance, demonstrating a favorable quality–runtime trade-off within the tested operating range. ROS-based engineering verification further completes all 15 repeated trials and all 48 verification tasks with no recorded invariant violations. Full article

Gene delivery with cationic polymers requires balancing DNA compaction, colloidal stability, and intracellular release, yet for imine-linked low-molecular-weight polyethyleneimine (PEI) vectors, quantitative relationships connecting the N/P ratio with the full property–transfection cascade remain undefined. Here, two amphiphilic non-viral vectors were prepared by linking a hydrophobic benzene–siloxane core (TAS) to hyperbranched PEI (800 or 2000 Da) through reversible imine bonds and complexed with DNA across a broad N/P range (10–600). Polyplexes were characterized by atomic force microscopy (AFM), dynamic light scattering (DLS), ζ-potential, agarose gel electrophoresis, transfection via green fluorescent protein (GFP) imaging and luciferase assay in HeLa cells. Both vectors formed spherical nano-entities (AFM diameters ~30 nm for TAS-PEI800; ~100 nm for TAS-PEI2000). TAS-PEI2000 achieved complete DNA retardation at N/P ≈ 30 versus N/P ≈ 150 for TAS-PEI800, consistent with its higher charge density (ζ = +37.59 vs. +18.35 mV). Transfection efficiency was superior for TAS-PEI2000 across most N/P ratios; however, TAS-PEI2000 displayed an optimal transfection efficiency at N/P ≈ 100 (ζ ≈ 3.84 mV), beyond which efficiency declined, indicating a binding–release trade-off. Cell viability remained >77% across the N/P range for TAS-PEI800, but dropped below 25% at N/P ≥ 400 for TAS-PEI2000. A phenomenological logistic model identified characteristic transition thresholds (θ ≈ 60 for TAS-PEI800; θ ≈ 40 for TAS-PEI2000), capturing the onset of cooperative self-assembly; however, the post-optimum decline observed for TAS-PEI2000 requires additional inhibitory terms. These findings demonstrate that PEI molecular weight governs both the N/P threshold required for efficient transfection and the width of the therapeutic window, thereby providing structure–activity descriptors for the rational design of imine-linked polyplex systems. Full article

Real-time lactate monitoring is essential for clinical diagnostics, sports physiology, and industrial bioprocessing, yet conventional enzymatic sensors suffer from limited stability, narrow operational temperature range, and complex fabrication protocols. Herein, we report a robust non-enzymatic electrochemical sensor based on graphene fibers (GFs), featuring a GF/Au/Ni(OH)₂ composite electrode with controllable structure fabricated via sequential electrodeposition. Systematic optimization of deposition parameters established a quantitative relationship between surface architecture and electrochemical response, revealing a critical trade-off between active site density and charge transport efficiency. The sensor achieved optimal performance when both Au and Ni(OH)₂ were deposited for 900 s, exhibiting a high sensitivity of 1.24 mA mM⁻¹ cm⁻² and a remarkably broad operational temperature range of 0–100 °C. Moreover, the sensor demonstrates excellent repeatability, superior anti-interference capability against common electroactive species, and outstanding long-term durability with 97.8% response retention after 14 days. This work provides a rational design strategy for balancing catalytic activity and transport properties in metal–metal oxide composites, offering a reliable platform for advanced applications in next-generation wearable health-monitoring systems. Full article

Effective thermal management is a cornerstone of safe, long-life lithium-ion battery operation, especially under high-rate charge–discharge and dynamic driving conditions. Conventional active cooling technologies face inherent trade-offs between heat dissipation efficiency, system complexity, and temperature uniformity, while phase change materials (PCMs) provide a promising passive alternative by absorbing latent heat during phase transition to buffer temperature spikes, improve temperature uniformity, and delay thermal runaway propagation. This paper presents a comprehensive review of recent advances in PCM-based lithium-ion battery thermal management, systematically covering the full scope from fundamental battery heat generation mechanisms to material synthesis optimization and hybrid system integration. At the material level, we analyze state-of-the-art strategies to address the intrinsic drawbacks of organic PCMs—low thermal conductivity, mismatched phase transition temperatures, and high flammability—including the construction of carbon/metal conductive skeletons, compositional tuning of phase change behavior, and flame-retardant modifications. These approaches have yielded composite PCMs with significantly improved heat transport capability and fire safety, while preserving high latent heat storage capacity. At the system level, we evaluate the thermal performance of pure passive PCM configurations, which excel at peak temperature suppression and inter-cell temperature uniformity, as well as hybrid designs that combine PCMs with air or liquid cooling to resolve heat accumulation issues and maintain stable performance under prolonged, demanding operating cycles. Despite these advances, key challenges remain: balancing high thermal conductivity with high latent heat capacity, developing climate-adaptable phase transition temperatures, and integrating multiple functionalities without compromising core thermal storage properties. Looking forward, future research directions include multifunctional integrated composites, smart adaptive PCMs, cost-effective scalable manufacturing, and precision structural engineering. This review also summarizes quantified performance trade-offs and provides actionable design guidelines for both material development and system-level integration. Full article

Hydrogen (H₂) detection under practical operating conditions requires sensing materials that simultaneously provide accessible reaction sites, efficient gas diffusion pathways, and fast interfacial charge transfer. Here, a hierarchical star-sphere ZnCo₂O₄ (ZC) architecture was integrated with graphene oxide (GO) and Pt supported on graphitized carbon (Pt/C) to develop hybrid chemiresistive sensing layers for low-temperature hydrogen detection. The synthesized ZC-based material exhibited a hierarchical morphology consisting of porous microspheres and star-shaped assemblies, providing a multiscale framework for gas access and surface reactions. By varying the GO content from 0.1 to 1 wt% at a fixed Pt/C loading, the ZC-0.5G composite achieved the most balanced structure, with well-distributed GO sheets, preserved star–sphere morphology, the highest specific surface area (53.6 m²/g), and the largest pore volume (0.09 cm³/g). The optimized sensor gave responses of 12.96%, 19.20%, 22.87%, and 26.43% for 500, 4000, 8000 and 10,000 ppm H₂ concentrations, respectively, with measurable response down to 50 ppm. The highest sensing performance was achieved at 50 °C and 60% relative humidity (RH), where the hierarchical oxide framework, GO-assisted interfacial pathways, and Pt catalytic sites acted in concert. The sensor also showed repeatable cyclic behavior and preferential response to H₂ compared to methanol, isopropanol, ethanol, acetone, and dimethylformamide. The improved sensing performance is attributed to the synergistic combination of the hierarchical ZC framework, GO-assisted interfacial pathways, and Pt-assisted catalytic activation, which together facilitate gas diffusion, surface reactions, and resistance modulation. Full article

Active distribution networks (ADNs) require battery energy storage system (BESS) scheduling strategies that reduce operating costs while preserving electrical feasibility and battery lifetime. This paper proposes a parallel adapted JAYA-based methodology for the day-ahead coordinated active and reactive power dispatch of BESS units in this type of grid. The novelty of this research lies in four key contributions: (i) the coordinated optimization of active and reactive power from BESS converters, exploiting their full capabilities for both energy management and voltage support; (ii) the integration of battery degradation costs within the optimization framework, preventing short-term economic strategies that accelerate aging; (iii) the implementation of a parallel adapted JAYA algorithm (AJAYA) with stagnation control and population reactivation mechanisms to enhance solution quality and convergence; and (iv) a comprehensive assessment under both deterministic and uncertainty-based operating conditions, providing a realistic validation of the proposed approach. Our model minimizes conventional generation, DER operation and maintenance, and BESS degradation costs while subject to power balance, distributed energy resource limits, voltage and current constraints, converter capacity, and state of charge (SoC) requirements. Each solution is encoded as BESS active/reactive power setpoints and evaluated through a multi-period AC power flow based on the successive approximations method, including SoC verification and a penalized fitness function. The methodology was validated in modified 33- and 69-node ADNs under deterministic and uncertainty scenarios (based on the conditions observed in Colombia), and it was benchmarked against the population-based genetic algorithm (PGA), the multiverse optimizer (MVO), the salp swarm algorithm (SALPS), the grey wolf optimizer (GWO), and the vortex search algorithm (VSA). According to the results, AJAYA outperformed the comparison methods, providing the best economic performance and exhibiting a robust behavior, with standard deviations below 0.06% and processing times below 0.05 h within a 24-h scheduling horizon. These findings demonstrate that the proposed framework constitutes an AC-feasible and degradation-aware academic contribution and a practical decision-support tool for operators and BESS owners, enabling a cost-effective and reliable BESS scheduling that preserves battery lifetime while improving network operation. Therefore, this research addresses the critical need for advanced energy management strategies that balance short-term economic benefits, technical feasibility, and long-term asset sustainability in modern distribution networks. Full article