Search Results (2,432)

In this study, copper–carbon material composites, Cu/CM (where CM is reduced graphene oxide (rGO), graphitic carbon nitride (g-C₃N₄), their mixture, and N-doped reduced graphene oxide (N-rGO)), were prepared using a simple method of chemical reduction of copper cations in the presence of CM related to molecular-level mixing methods. Additionally, copper cations from its oxides present in the composites were reduced in an electrochemical cell by depositing them on the surface of a horizontally positioned cathode. The structure and morphology of the Cu/CM composites were studied using electron microscopy and X-ray diffraction analysis. The thermal stability and elemental analysis were determined for the carbon materials. The resulting Cu/CM composites were used as electrocatalysts in the electrohydrogenation of the aromatic ketone, acetophenone. Cu/rGO and Cu/N-rGO composites with a 1:1 ratio exhibited catalytic activity in this process, increasing the rate of APh hydrogenation and its degree of conversion with the selective formation of a single product, methyl phenyl carbinol (or 1-phenylethanol), compared to the electrochemical reduction of APh on a cathode without a catalyst. The Cu/N-rGO composite demonstrated the highest electrocatalytic activity. Full article

This study evaluates the hydrogen embrittlement (HE) resistance of nickel-based electroplated coatings applied on cold-finished mild steel, with emphasis on their performance as diffusion barriers to impede hydrogen ingress. Nickel coatings were deposited using Watts plating bath under controlled electroplating parameters. Electrochemical hydrogen charging was performed in an alkaline medium at progressively increasing charging current densities to simulate varying levels of hydrogen exposure. Tensile testing was conducted immediately after charging to assess the mechanical response of both uncoated and nickel-coated specimens, focusing on key properties such as elongation, yield strength, ultimate tensile strength, and toughness. The results revealed a gradual degradation in ductility and toughness for the uncoated steel samples with increasing hydrogen content. In contrast, the nickel-coated specimens maintained mechanical stability up to a critical hydrogen threshold, beyond which a pronounced reduction in tensile response was observed. Fractographic analysis supported these trends, revealing a transition from ductile to brittle fracture characteristics with increasing concentrations of hydrogen. These findings highlight the protective capabilities and limitations of nickel-based coatings in mitigating hydrogen-induced degradation, offering insights into their application in industries where hydrogen embrittlement of structural materials is a major concern. Full article

Sluggish oxygen reduction reaction (ORR) remains a critical barrier to advancing intermediate-temperature electrochemical energy devices. Here, we demonstrate that strain engineering in two platforms, epitaxial thin films and freestanding membranes, systematically tunes ORR kinetics in Ruddlesden-Popper LaSrCoO₄. In epitaxial films, film thickness is varied to control in-plane tensile strain, whereas in freestanding membranes strain relaxation during the release step using water-soluble sacrificial layers produces flat or wrinkled architectures. Electrochemical impedance spectroscopy analysis reveals more than an order of magnitude increase in the oxygen surface exchange coefficient for tensile-strained films relative to relaxed films, together with a larger oxygen vacancy concentration. Wrinkled freestanding membranes provide a further increase in oxygen surface exchange kinetics and a lower activation energy, which are attributed to increased active surface area and local strain variation. These results identify epitaxial tensile strain and controlled wrinkling as practical design parameters for optimizing ORR activity in Ruddlesden-Popper oxides. Full article

This article focuses on studying the phase transformation of bauxite iron minerals during electrolytic reduction processes in alkaline solutions (400 g/L Na₂O), with the aim of improving aluminum extraction in the subsequent Bayer process. The research employs electrolytic reduction to convert the refractory minerals in unmilled bauxite (alumogoethite (Fe,Al)OOH, alumohematite (Fe,Al)₂O₃, chamosite (Fe²⁺,Mg,Al,Fe³⁺)₆(Si,Al)₄O₁₀(OH,O)₈) into magnetite, elemental iron (Fe) and to minimize aluminum (Al) extraction during electrolysis. Preliminary thermodynamic research suggests that the presence of hematite (α-Fe₂O₃) and chamosite in boehmitic bauxite increases the iron concentration in the solution. Cyclic voltammetry revealed that, in the initial stage of electrolysis, overvoltage at the cathode decreases as metallic iron deposited and conductive magnetite form on the surface of the particles. After 60 min, the reduction efficiency begins to decrease. The proportion of the current used for magnetization and iron deposition on the cathode decreased from 89.5% after 30 min to 67.5% after 120 min. After 120 min of electrolytic reduction, the magnetization rate exceeded 65%; however, more than 60% of the Al was extracted simultaneously. Al extraction after electrolysis and subsequent Bayer leaching exceeded 91.5%. Studying the electrolysis product using SEM-EDS revealed the formation of a dense, iron-containing reaction product on the particles’ surface, preventing diffusion of the reaction products (sodium ferrite and sodium aluminate). Mössbauer spectroscopy of the high-pressure leaching product revealed that the primary iron-containing phases of bauxite residue are maghemite (γ-Fe₂O₃), formed during the hydrolysis of sodium ferrite. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

Copper-based catalysts have emerged as promising materials for electrochemical carbon dioxide reduction reactions, owing to copper’s unique ability to facilitate multi-electron transfer processes and produce valuable products such as methanol and ethanol. In this study, novel trisferrocenyltrithiophosphite–copper(I) bromide composites with Cu-to-ligand molar ratios of 1:1 and 2:1 were synthesized and evaluated for their catalytic performance. The composites were characterized by a combination of techniques, including powder X-ray diffraction (PXRD), linear sweep voltammetry (LSV), potentiostatic testing, chromatographic analysis, scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). Electrochemical measurements demonstrated significant current enhancements in the presence of CO₂, highlighting the composites’ catalytic activity. Potentiostatic tests revealed excellent stability, with only a 9% decline in current density over 5 h of electrolysis. Product analysis via gas chromatography indicated the formation of methanol for the 1:1 composite and ethanol for the 2:1 composite with Faradaic efficiencies of 5.79% and 9.26%, respectively. While absolute efficiencies remain modest due to competitive hydrogen evolution, these results demonstrate a tunable catalytic performance based on the Cu-to-ligand ratio. SEM and XPS studies further supported the formation of active catalytic centers and changes in the oxidation states of copper during CO₂ reduction. PXRD analysis confirmed the retention of structural integrity for both composites before and after catalytic testing. Full article

Electrochemical CO₂ reduction (CO₂RR) is a promising route to transform a major greenhouse gas into value-added fuels and chemicals. However, its deployment is still hindered by the sluggish activation of CO₂, poor selectivity toward multielectron products, and competition with the hydrogen evolution reaction (HER). Single-atom catalysts (SACs) have emerged as powerful materials to address these challenges because they combine maximal metal utilization with well-defined coordination environments whose electronic structure can be precisely tuned through metal–support interactions. This minireview summarizes current understanding of how structural, electronic, and chemical features of SAC supports (e.g., porosity, heteroatom doping, vacancies, and surface functionalization) govern the adsorption and conversion of key CO₂RR intermediates and thus control product distributions from CO to CH₄, CH₃OH and C₂⁺ species. Particular emphasis is placed on selectivity descriptors (e.g., coordination number, d-band position, binding energies of *COOH and *OCHO) and on rational design strategies that exploit curvature, microenvironment engineering, and electronic metal–support interactions to direct the reaction along desired pathways. Representative SAC systems based primarily on N-doped carbons, complemented by selected examples on oxides and MXenes are discussed in terms of Faradaic efficiency (FE), current density and operational stability under practically relevant conditions. Finally, the review highlights remaining bottlenecks and outlines future directions, including operando spectroscopy and data-driven analysis of dynamic single-site ensembles, machine-learning-assisted DFT screening, scalable mechanochemical synthesis, and integration of SACs into industrially viable electrolyzers for carbon-neutral chemical production. Full article

AISI 1045 steel often undergoes premature failure under combined corrosive-wear conditions due to its insufficient surface durability. To address this, a hot-dip aluminum (HDA) coating was deposited on the steel substrate. The microstructure, corrosion behavior, and tribological properties of the coating were systematically characterized using scanning electron microscopy (SEM), electrochemical techniques, and tribometry. The results reveal that the coating exhibits a continuous triple-layer structure, consisting of the steel substrate, an intermediate Fe-Al intermetallic compound layer, and an outer aluminum-rich layer. In a 3.5 wt.% NaCl solution, the coating formed a protective Al₂O₃ film, demonstrating clear passivation behavior. It significantly enhanced the substrate’s performance, achieving an approximately 90% reduction in wear rate and a substantial increase in charge transfer resistance. The coated sample showed a lower friction coefficient (0.24) compared to the bare substrate (0.34). Herein, this work demonstrates that a straightforward and industrially viable hot-dip aluminizing process can effectively improve the corrosion and wear resistance of medium-carbon steel. The findings provide a practical surface-hardening strategy for such steels operating in aggressive environments. Full article

The electrochemical reduction of CO₂ to ethanol represents a sustainable alternative to recycle CO₂ into a value-added product, yet achieving high selectivity and efficiency remains a challenge. This work explores Cu-based catalysts supported on SiO₂ and ZrO₂, with and without ZnO doping, for ethanol production in a continuous flow-cell system. Gas diffusion electrodes are fabricated using commercial catalysts with varying Cu loadings (5–10%) and ZnO contents (2–3.5%). Comprehensive characterization by XPS confirms the presence of Cu²⁺ and Zn²⁺ species, while SEM reveals that ZnO incorporation improves surface uniformity and aggregate distribution compared to undoped samples. Electrochemical tests demonstrate that 10% Cu on SiO₂ achieves a Faradaic efficiency of 96% for ethanol at −3 mA cm⁻², outperforming both doped catalysts and previously reported materials. However, efficiency declines at higher current densities, indicating a trade-off between selectivity and productivity. ZnO doping enhances C₂⁺ product formation but does not surpass the undoped catalyst in ethanol selectivity. These results underline the importance of catalyst composition, support interactions, and operating conditions, and point to further optimization of electrode architecture and cell configuration to sustain high ethanol yields under industrially relevant conditions. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

Windows contribute 40–50% of envelope heat loss despite occupying only 1/8–1/6 of the surface area. Conventional frame retrofits rely on geometry optimization or cavity insulation yet remain limited by cost and invasiveness. This study introduces electrochemical polishing to reduce cavity surface emissivity of multi-cavity broken-bridge aluminum window frames to suppress radiative heat transfer, offering a non-invasive, low-cost retrofit strategy for existing building windows. Using a typical 75-series casement window, finite element analysis (MQMC) reveals that reducing cavity surface emissivity from 0.9 to 0.05 lowers frame U-values by 12.39–30.38% and whole-window U-values by 2.72–9.69%, with full-cavity treatment outperforming insulating-cavity-only by an average of 0.29 W/(m²·K). EnergyPlus simulations across multiple climate zones show 0.74–2.26% annual heating and cooling energy savings (with max reduction of 8.99 MJ/m²·yr) in severe cold and cold regions (e.g., Harbin, Beijing), but 1.25–3.04% penalties in mild and hot-summer zones due to impeded nighttime heat rejection. At an incremental cost of 62.5 CNY/window (6.6–7.4% increase), the static payback period is 4.1 years in Harbin. The approach mitigates thermal bridging more effectively than foam-filled frames in whole-window performance. This scalable, minimal-intervention technology aligns with low-carbon retrofit imperatives for existing aging windows, particularly in heating-dominated climates. Full article

In this study, we investigated the electrochemical reduction of carbon dioxide (CO₂) using a phosphorus-doped polycrystalline diamond (PDD) electrode and compared it with a conventional boron-doped diamond (BDD) electrode. In the reduction reaction of CO₂ in a 0.5 M KCl aqueous solution, the PDD electrode showed high selectivity for formic acid production over a wide potential range. This tendency was particularly evident at potentials below −1.8 V (vs. RHE). Moreover, at more negative potentials of −2.4 V and −2.6 V, formic acid remained the dominant product, while hydrogen evolution was the main reaction on the BDD electrode at the negative potentials. Similarly, in electrochemical reduction using an 0.1 M KClO₄ aqueous solution, carbon monoxide was confirmed to be a major product, and hydrogen generation was effectively suppressed in a more negative potential range. In other words, PDD is a promising electrode material that can efficiently convert CO₂ into valuable chemicals while suppressing hydrogen evolution, even in negative potential regions. Full article

The structure of self-assembled monolayers (SAMs) greatly influences electrochemical interface behavior. This study systematically examines how positional isomers of aromatic diamines (ADMs) assemble on a glassy carbon (GC) electrode and how such ordering affects the attachment and performance of electrochemically reduced graphene oxide (ERGO). SAMs of ortho-, meta-, and para-phenylenediamine (o-PDA, m-PDA, and p-PDA) were fabricated on GC and characterized using atomic force microscopy (AFM) and Raman spectroscopy. Among them, GC/p-PDA exhibited the most compact and homogeneous interfacial structure. ERGO was subsequently immobilized through the free amine functionalities of the SAM, as confirmed by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS), and cyclic voltammetry (CV). Strong covalent coupling and electrostatic interactions between the positively charged ERGO and terminal amines enabled stable attachment. Under optimized conditions, the modified GC/p-PDA/ERGO electrode demonstrated exceptional electrocatalytic activity toward nitrobenzene (NBz) reduction, achieving a high sensitivity of 1410 μA mM⁻¹ cm⁻² and a low detection limit of 0.040 μM. In addition, this sensor displayed outstanding anti-interference capability, stability, and recovery in a water sample. These results establish GC/p-PDA/ERGO sensor as a robust and efficient electrocatalytically active interface for nitroaromatic pollutants detection and sustainable environmental monitoring. Full article

Electrospinning has emerged as a powerful technique for fabricating customised nanofibrous materials with integrated functional nanostructures, offering significant advantages for electrochemical energy applications. This review highlights recent advances in using electrospun nanofibres directly as active components in devices such as batteries, supercapacitors, and fuel cells. The emphasis is on the role of composite design, fibre morphology and surface chemistry in enhancing charge transport, catalytic activity and structural stability. Integrating carbon-based frameworks, conductive polymers, and inorganic nanostructures into electrospun matrices enables multifunctional behaviour and improves device performance. The resulting nanofibrous composite materials, often after heat treatment, can be used directly as electrodes or self-supporting layers, eliminating the need for additional processing steps such as size reduction or preparation of slurries and inks for creating functional nanofibre-based deposits. The importance of composite nanofibres as an emerging strategy for overcoming challenges related to scalability, long-term durability, and interface optimisation is also discussed. This review summarises the key results obtained to date and highlights the potential of electrospun nanofibres as scalable, high-performance materials for next-generation energy technologies, outlining future directions for their rational design and integration. Full article

The transition to the 4th Industrial Revolution (4IR) in the electroplating industry necessitates intelligent, real-time monitoring systems to replace traditional, time-consuming offline analysis. In this study, we developed a cost-effective, automated measurement system for hexavalent chromium (Cr(VI)) in plating wastewater using an Arduino-based RGB sensor. Unlike conventional single-variable approaches, we conducted a comprehensive feature sensitivity analysis on multi-sensor data (including pH, ORP, and EC). While electrochemical sensors were found to be susceptible to pH interference, the analysis identified that the Red and Green optical channels are the most critical indicators due to the distinct chromatic characteristics of Cr(VI). Specifically, the combination of these two channels effectively functions as a dual-variable sensing mechanism, compensating for potential interferences. To optimize prediction accuracy, a systematic machine learning strategy was employed. While the Convolutional Neural Network (CNN) achieved the highest classification accuracy of 89% for initial screening, a polynomial regression algorithm was ultimately implemented to model the non-linear relationship between sensor outputs and concentration. The derived regression model achieved an excellent determination coefficient (R² = 0.997), effectively compensating for optical saturation effects at high concentrations. Furthermore, by integrating this sensing model with the chemical stoichiometry of the reduction process, the proposed system enables the precise, automated dosing of reducing agents. This capability facilitates the establishment of a “Digital Twin” for wastewater treatment, offering a practical ICT (Information and Communication Technology)-based solution for autonomous process control and strict environmental compliance. Full article