Hasil untuk "Industrial electrochemistry"

Lequn Fu, Yijun Zhong, Xiao Li et al.

Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/

R. Adcock, T. Chen, N. Click et al.

Metal separation and recovery are a key aspect of silicon solar module recycling. This paper provides a fundamental understanding of the leaching and electrowinning in hydrochloric acid of two critical metals in silicon solar cells: copper and tin. A leaching model for solder-coated copper wires was developed to reveal rate orders with respect to concentrations of leaching agents and stirring. Kinetic parameters for electrowinning of copper and tin were determined through Tafel and electrochemical impedance spectroscopy analysis. Cyclic voltammetry was used to determine redox potentials of copper and tin allowing their electrochemical separation. Finally high recovery rates and high metal purity, both over 99 %, were achieved for copper and tin through sequential electrowinning. Hydrochloric acid leaching and sequential electrowinning provide a simple and effective option for the recovery of copper and tin from silicon solar modules.

Md Imran Hasan, Gang Lei, Dylan Lu et al.

Electric vehicle (EV) adoption is generating a rapidly increasing stream of retired lithium-ion batteries for second-life deployment. However, thermal safety concerns continue to limit their reuse. This paper reviews second-life battery (SLB) thermal safety and management and organizes existing work through a mechanism-to-deployment framework linking four domains: degradation mechanisms, cell screening, pack configuration, and monitoring. Evidence indicates that thermal risk depends on the degradation pathway rather than capacity fade. In fact, cells with comparable capacity can exhibit substantially different trigger temperatures depending on whether lithium plating or solid-electrolyte interphase (SEI) growth dominates. Therefore, capacity-based screening is insufficient because cells that satisfy capacity thresholds may still remain thermally unstable. The four domains are tightly coupled: the degradation pathway determines screening requirements; screening outcomes constrain pack design; pack topology influences fault escalation; and together these factors determine what monitoring can reliably detect. This review highlights three gaps and outlines future research directions in the field of SLB thermal safety and management: limited aged-cell thermal characterization by degradation pathway, insufficient diagnostic validation under industrial-throughput conditions, and the incomplete translation of screening outputs into design rules.

L. Gubler

Looking for a textbook to help master electrochemistry? Interested in expanding your knowledge of the new technologies integrating electrochemistry? Don’t want to be discouraged by overbearing mathematical derivations? Look no further! Based on comprehensive course lecture notes, this textbook offers an in-depth exploration of the conceptual understanding of electrochemistry, spanning the equivalent of thirteen weeks of lectures. Designed for students with a fundamental understanding of physical chemistry, it covers essential topics such as terminology, thermodynamics and kinetics, alongside application-oriented subjects like industrial electrochemistry, energy storage and conversion. The reader can explore technologies of current relevance, such as lithium-ion batteries and fuel cells, and discover additional topics in the appendices if seeking further information. Each chapter provides a structured and progressive learning experience, without the need for complex mathematical derivations, making the book ideal for students and professionals alike. This book is all you need to equip you with the knowledge to excel in both academic and industrial settings.

Yifei Xu, Bingjun Xu

Jun Qi, Yadong Du, Qi Yang et al.

Hydrogen peroxide (H2O2) electrosynthesis through oxygen reduction reaction (ORR) is drawing worldwide attention, whereas suffering seriously from the sluggish oxygen evolution reaction (OER) and the difficult extraction of thermodynamically unstable H2O2. Herein, we present an electrosynthesis protocol involving coupling ORR-to-H2O2 with waste polyethylene terephthalate (PET) upcycling and the first H2O2 conversion strategy. Ni-Mn bimetal- and onion carbon-based catalysts are designed to catalyze ORR-to-H2O2 and ethylene glycol electrooxidation with the Faradaic efficiency of 97.5% (H2O2) and 93.0% (formate). This electrolysis system runs successfully at only 0.927 V to achieve an industrial-scale current density of 400 mA cm−2, surpassing all reported H2O2 electrosynthesis systems. H2O2 product is upgraded through two downstream routes of converting H2O2 into sodium perborate and dibenzoyl peroxide. Techno-economic evolution highlights the high gross profit of the ORR || PET upcycling protocol over HER || PET upcycling and ORR || OER. This work provides an energy-saving methodology for the electrosynthesis of H2O2 and other chemicals. The development of robust catalysts that could work under industrial-scale current densities bring promise but also a challenge for hydrogen production. Here, the authors report an in situ activation method to produce ferromagnetic ruthenium clusters that can catalyze the hydrogen evolution reaction at high current densities.

Leitao Xu, Yelin Yao, Wenjie Wu et al.

The synthesis of hydrazine from the electrooxidation of ammonia (NH3) presents a promising pathway, yet it is hindered by the strong thermodynamic preference for the oxidation of hydrazine. Here, we describe an electrochemistry-mediated strategy that reverses the thermodynamic order during NH3 oxidation, enabling sustainable hydrazine production. This process utilizes cyclohexanone (C6H10O), derived from the electrooxidation of cyclohexanol (C6H11OH), as a mediator to prevent the over-oxidation of NH3. The in situ generation of cyclohexanone (C6H10O) at the catalyst interface effectively prevents the over-oxidation of NH3, while cation effect accelerates NH3 capture during the reaction. Furthermore, the developed manganese (Mn) doped nickel hydroxide electrocatalyst (Mn-Ni(OH)2) not only improves NH3 tolerance of the catalyst but also promotes the conversion of nitrogen-containing intermediates. This scalable approach achieves gram-scale production at a constant current of 800 mA, offering economic advantages over industrial methods, paving the way for a sustainable transformation of the chemical industry.

Yiqi Zhou, Yifan Zheng, Peihu Yuan et al.

Low-density steels have gained increasing popularity in industrial applications. However, their corrosion mechanisms remain insufficiently studied. This study aims to investigate the influence of varying Cu content on the microstructure evolution and corrosion resistance of Fe-8.5Al-30Mn-1C low-density steel. Potentio-dynamic polarization tests were conducted to evaluate the critical pitting potential (Epit) at varying Cu contents (0–3 Wt.%). In addition, bipolar electrochemistry was used to investigate the corrosion evolution at different potentials and the susceptibility of pit nucleation sites in low-density steel. The results indicate that the Epit remains unaffected by Cu content, as pit nucleation predominantly occurs at the interfaces between micro-scale carbides and the steel matrix – a process independent of Cu concentration. Although Cu promotes the formation of a corrosion film on the low-density steel, the potential for film formation exceeds that of pitting initiation. Furthermore, the resulting corrosion films exhibit insufficient stability and thickness to enhance pitting resistance. Consequently, Cu alloying does not improve pitting corrosion in low-density steel. This study elucidates the influence of Cu on corrosion mechanisms in low-density steel. The findings provide valuable insights for selecting optimal alloying elements to enhance pitting corrosion resistance in low-density steels. Furthermore, the results establish fundamental guidelines for developing highly corrosion-resistant surface films through controlled alloy design.

Norica Godja, Saied Assadollahi, Melanie Hütter et al.

Nickel’s durability and catalytic properties make it essential in the aerospace, automotive, electronics, and fuel cell technology industries. Wastewater analysis typically relies on sensitive but costly techniques such as ICP-MS, AAS, and ICP-AES, which require complex equipment and are unsuitable for on-site testing. This study introduces a novel screen-printed electrode array with 16 chemically and, optionally, electrochemically coated Au electrodes. Its electrochemical response to Ni2+ was tested using Na2SO3 and ChCl-EG deep eutectic solvents as electrolytes. Ni2+ solutions were prepared from NiCl2·6H2O, NiSO4·6H2O, and dry NiCl2. In Na2SO3, the linear detection ranges were 20–196 mM for NiCl2·6H2O and 89–329 mM for NiSO4·6H2O. High Ni2+ concentrations (10–500 mM) were used to simulate industrial conditions. Two linear ranges were observed, likely due to differences in electrochemical behaviour between NiCl2·6H2O and NiSO4·6H2O, despite the identical Na2SO3 electrolyte. Anion effects (Cl− vs. SO42−) may influence response via complexation or ion pairing. In ChCl-EG, a linear range of 0.5–10 mM (R2 = 0.9995) and a detection limit of 1.6 µM were achieved. With a small electrolyte volume (100–200 µL), nickel detection in the nanomole range is possible. A key advantage is the array’s ability to analyze multiple analytes simultaneously via customizable electrode configurations. Future research will focus on nickel detection in industrial wastewater and its potential in the multiplexed analysis of toxic metals. The array also holds promise for medical diagnostics and food safety applications using thiol/Au-based capture molecules.

Shabdiki Chaurasia, Sundar Rajan Aravamuthan, Dayou Luo et al.

To decarbonize the global economy by 50% by 2030 and achieve net-zero emissions by 2050, electrifying industrial processes, particularly in cement manufacturing, is crucial. Cement production accounts for ~8% of global CO2 emissions, primarily due to the thermal decomposition of calcium carbonate in Portland clinker. This study presents a novel electrochemical strategy for converting calcium carbonate to calcium hydroxide, a key clinker precursor, using a fossil-free, flow-assisted electrolysis process. A scalable three-channel flow electrolyzer is employed, comprising an acidic anolyte, basic catholyte, and a neutral chemical flush, separated by a cation exchange membrane (CEM) and an anion exchange membrane (AEM). The performance of the system was assessed under varied operating conditions to elucidate electrochemical behavior and practical limitations. To the best of our knowledge, this is the first reported demonstration of a bench-scale three-channel flow electrolyzer achieving ~40% faradaic efficiency at 50 mA/cm2 using calcium carbonate as the input. Results indicate significant opportunities for performance gains through optimization of electrolyte composition and membrane stability. This work offers a promising step toward decarbonizing cement production while contributing to the broader advancement of flow-based electrolysis technologies for sustainable chemical manufacturing.

Qiyu Chen, Huiyuan Luo, Haiming Yao et al.

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.

Shuai Li, Chen Yizhe, Li Dong et al.

The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision language-action (VLA) models have been a key paradigm in AI to unify perception, reasoning, and control. Has the performance of the VLA models met the industrial requirements? In this paper, from the perspective of industrial deployment, we compare the performance of existing state-of-the-art VLA models in industrial scenarios and analyze the limitations of VLA models for real-world industrial deployment from the perspectives of data collection and model architecture. The results show that the VLA models retain their ability to perform simple grasping tasks even in industrial settings after fine-tuning. However, there is much room for performance improvement in complex industrial environments, diverse object categories, and high precision placing tasks. Our findings provide practical insight into the adaptability of VLA models for industrial use and highlight the need for task-specific enhancements to improve their robustness, generalization, and precision.

Jaxson Brown, Duc-Son Pham, Sie-Teng Soh et al.

Industrial Control Systems (ICSs) are complex interconnected systems used to manage process control within industrial environments, such as chemical processing plants and water treatment facilities. As the modern industrial environment moves towards Internet-facing services, ICSs face an increased risk of attacks that necessitates ICS-specific Intrusion Detection Systems (IDS). The development of such IDS relies significantly on a simulated testbed as it is unrealistic and sometimes hazardous to utilize an operational control system. Whilst some testbeds have been proposed, they often use a limited selection of virtual ICS simulations to test and verify cyber security solutions. There is a lack of investigation done on developing systems that can efficiently simulate multiple ICS architectures. Currently, the trend within research involves developing security solutions on just one ICS simulation, which can result in bias to its specific architecture. We present ICS-SimLab, an end-to-end software suite that utilizes Docker containerization technology to create a highly configurable ICS simulation environment. This software framework enables researchers to rapidly build and customize different ICS environments, facilitating the development of security solutions across different systems that adhere to the Purdue Enterprise Reference Architecture. To demonstrate its capability, we present three virtual ICS simulations: a solar panel smart grid, a water bottle filling facility, and a system of intelligent electronic devices. Furthermore, we run cyber-attacks on these simulations and construct a dataset of recorded malicious and benign network traffic to be used for IDS development.

Tom Maus, Asma Atamna, Tobias Glasmachers

Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.

Haotian Chen, Chenyang Huang, Alexander Rodríguez et al.

Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiment with theory. Electrochemical modeling has had advanced across empirical, analytical, numerical, and data-driven paradigms. Data-driven machine learning and physics based electrochemical modeling, however, have not been explicitly linked. Here we introduce Differentiable Electrochemistry, a mew paradigm in electrochemical modeling that integrates thermodynamics, kinetics and mass transport with differentiable programming enabled by automatic differentiation. By making the entire electrochemical simulation end-to-end differentiable, this framework enables gradient-based optimization for mechanistic discovery from experimental and simulation data, achieving approximately one to two orders of improvement over gradient-free methods. We develop a rich repository of differentiable simulators across diverse mechanisms, and apply Differentiable Electrochemistry to bottleneck problems in kinetic analysis. Specifically, Differentiable Electrochemistry advances beyond Tafel and Nicholson method by removing several limitations including Tafel region selection, and identifies the electron transfer mechanism in Li metal electrodeposition/stripping by parameterizing the full Marcus-Hush-Chidsey formalism. In addition, Differentiable Electrochemistry interprets Operando X-ray measurements in concentrated electrolyte by coupling concentration and velocity theories. This framework resolves ambiguity when multiple electrochemical theories intertwine, and establishes a physics-consistent and data-efficient foundation for predictive electrochemical modeling.

Viktoria Falkowski, Wilhelm Pfleging

Changing the topography of electrodes by ultrafast laser ablation has shown great potential in enhancing electrochemical performance in lithium-ion batteries. The generation of microstructured channels within the electrodes creates shorter pathways for lithium-ion diffusion and mitigates strain from volume expansion during electrochemical cycling. The topography modification enables faster charging, improved rate capability, and the potential to combine high-power and high-energy properties. In this study, we present a preliminary exploration of this approach for sodium-ion battery technology, focusing on the impact of laser-generated channels on hard carbon electrodes in sodium-metal half-cells. The performance was analyzed by employing different conditions, including different electrolytes, separators, and electrodes with varying compaction degrees. To identify key factors contributing to rate capability improvements, we conducted a comparative analysis of laser-structured and unstructured electrodes using methods including scanning electron microscopy, laser-induced breakdown spectroscopy, and electrochemical cycling. Despite being based on a limited sample size, the data reveal promising trends and serve as a basis for further optimization. Our findings suggest that laser structuring can enhance rate capability, particularly under conditions of limited electrolyte wetting or increased electrode density. This highlights the potential of laser structuring to optimize electrode design for next-generation sodium-ion batteries and other post-lithium technologies.

Ha Eun Kang, Seong-Do Kim, Young Soo Yoon et al.

Nickel-rich layered oxide cathodes, typified by compositions such as LiNi₁₋ₓ₋ᵧCoₓMnᵧO₂ (NCM) have garnered significant attention as high-energy-density candidates for next-generation lithium-ion batteries. However, their widespread deployment is hindered by a confluence of structural degradation, surface instability, and poor interfacial compatibility under high voltage cycling. To address these multifaceted limitations, this review comprehensively examines recent advances in surface coating and bulk doping strategies, which have emerged as pivotal approaches for enhancing the electrochemical stability and longevity of Ni-rich cathodes. Surface coatings including oxides, phosphates, and fluorides have been shown to effectively mitigate electrolyte-induced parasitic reactions and reinforce cathode–electrolyte interfaces. Simultaneously, elemental doping at transition-metal, lithium, and oxygen sites offer promising pathways to suppress cation disorder, stabilize layered frameworks, and facilitate Li⁺ transport. Emphasis is placed on site-specific doping mechanisms, the role of multi-site (co-)doping, and their synergistic interplay with surface modification layers. By synthesizing recent findings, this review delineates how the judicious integration of coating and doping techniques can enable the rational design of Ni-rich cathodes with enhanced structural integrity, rate capability, and cycle life.

B. Tiss, D. Martínez-Martínez, C. Mansilla et al.

L. T. Angenent, Isabella Casini, Uwe Schröder et al.

Our societies must reconsider current industrial practices and find carbon-neutral alternatives to avoid the detrimental environmental effects that come with the release of greenhouse gases from fossil-energy carriers. Using renewable...

Y. Budnikova, E. Dolengovski, M. V. Tarasov et al.