Kun Shan University

For core materials with fast response times and adjustable power and energy output for energy storage, we herein report the electrochemical performance and latent hydrogen evolution reaction of sheaf-like carbon nanotube (s-CNT) based material electrodes, aiming for further low-cost synthesis and environmentally friendly supercapacitor applications. XPS scanning spectra show C, O, and a few Mo elements with estimated contents of 82.3, 17.2, and 0.5 at%, respectively. The maximum capacitance of 1287 F g-1 is achieved in the s-CNT-based electrochemical double-layer capacitor (EDLC) due to slow ion diffusion in the s-CNT-based electrode during charging and discharging. The Bode phase angle at 80.1 degrees of the s-CNT electrode is close to 90 degrees, and the prepared s-CNT electrode illustrates ideal EDLC capacitance behavior. In addition, the s-CNT electrode, after 2000 subsequent CV and GCD cycles at a current density of 0.95 A g-1, retains specific capacitance of 96.8% and 80.55%, respectively. The s-CNT electrode exhibits reduced ionic resistance and facilitates ion transport through its conductive architecture, demonstrating its potential as a high-performance material for prospective EDLC supercapacitor applications. 12 +/- 2 layers inside a single s-CNTMaximum capacitance of 1287 F g-1 was achieved in a concentrated 6 M KOH (aq) mediumExhibits an energy density of 161 Wh kg-1 at a power density of 0.14 kW kg-1Specific capacitance retention is 96.8% and 80.55% of CV and GCD cycles, respectively

In this study, a ternary Ni/Mg/g-C3N4 composite was synthesized via a controlled precipitation-calcination route and evaluated for its visible-light-assisted degradation of methylene blue (MB). The structural, morphological, and optical characteristics of the composites were systematically investigated using XRD, FT-IR, FESEM, BET, and UV-Vis analyses. The results confirmed the successful construction of Ni/Mg/g-C3N4 heterojunctions with strong interfacial coupling and enhanced surface porosity. Among all samples, the Ni/Mg/CN20 composite exhibited the highest activity, achieving 66% MB degradation within 180 min under visible light. This superior performance was attributed to synergistic effects arising from efficient interfacial charge transfer, broadened light absorption, and abundant active sites. The composite also displayed excellent thermal stability. This work demonstrates that the rational control of g-C3N4 loading plays a decisive role in tuning the physicochemical and catalytic properties of Ni/Mg/g-C3N4 composites. The findings provide new insights into the design of cost-effective, thermally stable, and high-performance photocatalysts for visible-light-driven wastewater treatment.

A cobalt-molybdenum bimetallic metal-organic framework (CoMo-MOF, denoted as CMBM) was successfully synthesized via a facile solvothermal approach, and was explored as a multifunctional catalyst for both electrocatalytic and photocatalytic applications. The rationale for integrating these functionalities lies in the synergistic interaction between Co and Mo, which effectively modulates the electronic structure, increases the density of active sites, and promotes efficient charge transport and separation. Electrochemical studies demonstrate that CMBM exhibits excellent bifunctional electrocatalytic activity, delivering low overpotentials of 198 mV for the hydrogen evolution reaction (HER) in 0.5 M H2SO4 and 232 mV for the oxygen evolution reaction (OER) in 1 M KOH at 10 mA cm- 2, along with favorable Tafel slopes and reduced charge-transfer resistance. In addition to its energy conversion capability, the catalyst was evaluated for the photocatalytic degradation of ciprofloxacin as a model pharmaceutical pollutant to assess its environmental applicability. Under UV-A irradiation (32 W), CMBM achieved 88.26% degradation of ciprofloxacin (10 mg L-1) within 100 min at pH 5, with a pseudo-first-order rate constant of 0.01125 min- 1. The enhanced performance is attributed to improved charge separation, an increased electrochemically active surface area, and the presence of heterometallic active centers. These findings suggest that CMBM is a promising and versatile catalytic platform with broad applicability in energy and environmental systems, including pollutant degradation and other redox-driven reactions, thereby bridging sustainable energy conversion and environmental remediation.

Fluctuations in photovoltaic (PV) power generation caused by rapidly moving clouds often lead to abrupt power ramps, posing challenges to stable grid operation. Developing an accurate ultrashort-term forecasting model is therefore essential for improving dispatch efficiency and maintaining system reliability. To address this problem, this paper proposes an ultrashort-term PV forecasting framework that jointly performs deterministic and probabilistic prediction, incorporating dynamic cloud tracking and sky-image-based correction. A cloud-coverage ratio forecasting module is developed using dense optical flow to estimate cloud movement, enabling early detection of potential power ramps. In addition, meteorological variables obtained from the weather research and forecasting model, together with historical PV power measurements, are integrated into a four-stage deep learning architecture enhanced with quantile regression to generate both point forecasts and calibrated prediction intervals. Comprehensive experimental evaluations, including comparisons with conventional and recent forecasting models as well as ablation analyses, demonstrate that the proposed framework consistently improves forecasting performance. Deterministic results show that the proposed model reduces NRMSE by 21–59% compared with conventional benchmark models and by 4.5–13% relative to the strongest baseline. For probabilistic forecasting, the method achieves coverage rates of 82–95% across confidence levels of 80–95%, while maintaining 22–31% narrower prediction intervals than the lower–upper bound estimation method, indicating sharper and more reliable uncertainty representation. These results verify the robustness of the proposed model for rapid PV fluctuations and demonstrate strong applicability for solar power scheduling.

Steel manufacturing requires high-throughput and high-reliability surface inspection to minimize safety risks, scrap rates, and downstream quality reductions. Conventional rule-based vision and manual inspection are often impeded in real production environments by variable illumination, complex textures, subtle defect morphology, and stringent latency constraints imposed by production-line operation. Deep learning (DL) has become a dominant paradigm for the detection and classification of defects when inspecting steel, but many previous studies have performed broad architectural overviews without explicitly connecting model and pipeline choices to deployment-critical factors such as processing speed, hardware availability, annotation cost, and robustness during domain shift. This review synthesizes ten representative case studies on DL-based defect inspection in steel manufacturing and closely related industrial settings, spanning classification, object detection, and segmentation workflows. To enable structured comparisons, we harmonized practical considerations across the ten studies, including task formulation, backbone design, training strategy (e.g., transfer learning), data augmentation, reported throughput, and commonly used performance indicators such as accuracy, precision/recall, mean average precision, and processing speed. Due to the heterogeneity in datasets, metrics, and hardware configurations across studies, we further introduce a transparent, review-oriented figure of merit as a heuristic summary of reported benefit–cost parameters (performance accuracy and processing speed vs. training burden and model complexity), while explicitly addressing the limitations associated with missing values and avoiding claims of statistically definitive ranking. Based on recurring patterns across the selected studies, we propose a conceptual hybrid framework blueprint—derived from the reviewed literature rather than newly experimentally validated results—that integrates transfer learning, real-time detection, and end-to-end learning principles as an engineering template for practical deployment. We conclude by providing actionable guidance for applications and outline future directions in label-efficient learning, cross-domain robustness, and standardized benchmarking and reporting to improve the reproducibility and industrial relevance of our approach.