Efficient Li+ and electron transport are important for intercalation pseudocapacitive energy storage. Here, one-dimensional Magn & eacute;li-phase W18O49 nanorods are synthesized at 1350-1550 degrees C by hot-filament metal oxide vapor deposition (HFMOVD) method and investigated as intercalation pseudocapacitive electrodes. Increasing the synthesis temperature modifies the oxygen-defect characteristics, accompanied by enhanced Li+ diffusion kinetics and pseudocapacitive charge storage. The optimized W18O49 nanorod-based pseudocapacitor@1550 degrees C delivers specific capacitances of similar to 118.94 F g(-1) from cyclic voltammetry and similar to 41.32 F g(-1) from galvanostatic charge-discharge, while retaining similar to 98.37% of its initial capacitance after 5000 cycles. Spin-polarized DFT + U calculations for representative LixW18O49 configurations (x = 0, 1, 2) reveal progressive Li+-induced electronic reconstruction, including extended spin-density distribution, changes in W-5d/O-2p hybridization, and increased density of states near the Fermi level. With increasing Li+ content, the Fermi-level band velocity increases from similar to 8.6 & times; 10(5) to similar to 10.1 & times; 10(5) m s(-1), while the magnitude of the corresponding effective mass decreases from similar to 1.56 to similar to 0.58 m(0), indicating more dispersive electronic states and enhanced electron delocalization. These results connect synthesis-temperature-dependent oxygen-defect evolution and Li+ transport kinetics with Li+-induced electron delocalization, providing an electronic basis for the enhanced pseudocapacitive energy storage of W18O49 nanorods.
Rapid industrialization and coastal development have driven the widespread accumulation of heavy metals (HMs) in marine environments, particularly in sediments and surrounding waters. Heavy metal contamination poses a significant threat to benthic ecosystems and coastal environmental health, highlighting the need for a better understanding of their toxic effects on marine organisms. Perinereis linea is a sediment-dwelling marine polychaete recognized as a sensitive bioindicator of coastal environmental conditions. Through its burrowing activity, P. linea contributes to sediment bioturbation, promoting the mineralization and decomposition of organic matter and supporting the health and stability of benthic ecosystems. Despite its ecological importance, few studies have investigated P. linea as a potential model organism for toxicity assessment, particularly in terms of behavioral responses. In this study, we evaluated the potential of P. linea as a marine model for assessing the behavioral toxicity of HMs using the customized behavioral analysis platform. Behavioral screening of 23 HMs revealed significant locomotor impairments induced by most of the tested metals, characterized by reduced average speed and rapid movement time ratio, increased freezing behavior, and altered movement complexity. PCA and heatmap further identified distinct behavioral fingerprints among the tested metals, with copper exhibiting the strongest behavioral toxicity. Subsequent dose–response experiments demonstrated that copper exposure (1–1000 ppb) significantly reduced locomotor activity and fractal dimension, indicating impaired movement complexity. Co-exposure with chelating agents, including citric acid, chitosan, tannic acid, and sodium alginate, partially restored normal behavioral patterns. Furthermore, transcriptomic findings provided preliminary insights into the potential molecular mechanism underlying HM-induced effects in P. linea. However, given the limited biological replication, the transcriptomic findings should be considered exploratory and interpreted with caution. Collectively, these findings demonstrate the potential of P. linea as a promising marine model organism for behavioral toxicity assessment and environmental monitoring of metal contamination in marine ecosystems.
This study develops an integrated decision-support framework to advance green supply chain management (GSCM) by systematically linking Environmental, Social, and Governance (ESG) practices, environmental product innovation, corporate performance, and strategic alternatives. Employing the Analytic Network Process (ANP), the proposed model captures complex interdependencies and feedback relationships across life-cycle value chain stages, enabling a holistic evaluation of sustainability-oriented strategies. A Delphi panel comprising 15 experts from academia, industry, and government is used to validate the evaluation criteria and network structure. The empirical results indicate that eco-friendly design, energy and resource efficiency, and carbon-climate management are the most influential drivers shaping green supply chain performance. Moreover, operational and sustainability performance are found to exert greater strategic importance than short-term financial performance, highlighting GSCM as a long-term capability-building approach rather than a cost-centered initiative. To enhance analytical adaptability, this study proposes a conceptual extension integrating neural feature extraction (NFE) signals with ANP-based expert weights. The NFE module is not empirically trained or validated; rather, it illustrates a theoretically consistent mechanism for incorporating data-driven feature signals into structured multi-criteria decision frameworks. Empirical validation of the NFE component is proposed as a future research direction.
This study examines the effect of increased stock liquidity on the speed of corporate leverage adjustment toward the optimal leverage. We find that overleveraged firms with high liquidity reduce their leverages at a lower speed than that of their low-liquidity counterparts. In contrast, we find that underleveraged firms with high liquidity adjust leverage at a higher speed than that of their low-liquidity counterparts. These empirical results are attributed to the fact that both overleveraged and underleveraged firms with high liquidity face a lower cost of debt when managers can make more informed investment decisions from enhanced liquidity. Our empirical findings shed new light on the importance of stock liquidity in firms’ dynamic capital structure adjustments.
This work aims to investigate the rehabilitation effectiveness on the loading resistance of a locally-corroded reinforced concrete (RC) slab using the scaled experiment. It includes seven scaled RC slabs, including one uncorroded slab and six corroded slabs retrofitted with carbon fibre-reinforced polymers (CFRP) strip plates (CFRP-p), CFRP sheets (CFRP-s), and spot-welded wire mesh (WWM). Besides the retrofit work in the rehabilitation, this work adopts non-shrinkage cement mortar and lightweight epoxy mortar to do the patch repair of the corroded regions. The slab specimens are corroded using the electrical-chemical method. After the patch repair and retrofit, the monotonic loading tests are conducted to investigate their mechanical behaviour, especially focusing on the application of CFRP-p, which is more convenient in the site construction. Test results show CFRP-p improves loading resistance more than CFRP-s and WWM, reducing member deformation and maximum crack width. It also effectively enhances the stiffness of corrosion-damaged RC slabs under serviceability conditions. The moment strength of CFRP-p retrofitted specimens is calculated using the ACI 440.2 R code and shows good agreement with test results. This method effectively determines the optimal CFRP strip plate area to compensate for corrosion-induced strength loss. Finally, the work suggests construction guidelines for CFRP-p use in locally-corroded RC slab.
