Hasil untuk "Industrial electrochemistry"

E. Monmasson, M. Cirstea

This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic.

C. Léger, P. Bertrand

M. Pumera

Veronika Jančíková, Michal Jablonský

Deep eutectic solvents are a sustainable and chemically tunable class of solvents formed by strong hydrogen bonding between a hydrogen bond acceptor and a hydrogen bond donor. Their extreme versatility has established deep eutectic solvents in ten key applied areas, including the green extraction of bioactive compounds, CO2 capture, electrochemistry, and the catalytic media. Research is shifting towards highly innovative frontier trends, such as the role of deep eutectic solvents in dynamic covalent chemistry and as templates for advanced photocatalytic nanomaterials. Other innovative directions include artificial organelles for bioremediation, thermoacoustic deep eutectic solvents for smart drug delivery, and their use as multifunctional interfaces for 2D materials. The future of deep eutectic solvents lies in process engineering and scale-up, supported by computational chemistry, confirming their position as a central pillar of the circular economy. This trajectory marks the transition of deep eutectic solvents from laboratory curiosities to a scalable industrial reality.

Hyeona Park, A. Celeste, Shulin Wang et al.

ABSTRACT Lithium‐sulfur (Li‐S) batteries deliver gravimetric energy densities considerably higher than those of conventional lithium‐ion systems while relying on low‐cost, earth‐abundant materials. Despite decades of progress, their commercialization remains hindered by intrinsic challenges such as the insulating nature of sulfur and lithium sulfide (Li2S), formation and dissolution of soluble polysulfides, and instability of lithium‐metal anodes. Among these, the use of Li2S as a pre‐lithiated cathode has redefined the landscape of Li─S chemistry by offering a pathway toward lithium‐free and anode‐free architectures that are compatible with the existing manufacturing infrastructure. This perspective revisits the Li2S electrochemistry from a conceptual and design standpoint. The perspective emphasizes multiscale strategies for atomic‐level catalytic engineering, mesoscale electrode architectures, and electrolyte–interface control, which collectively determine Li2S activation and reversibility. The perspective also examines emerging approaches that integrate Li2S cathodes with graphite, silicon, and solid‐state configurations to enable safe, high‐energy, and manufacturable Li─S technologies. Finally, this perspective discusses the evolving roles of redox mediators, machine learning‐based discovery, and sustainable synthesis in bridging the gap between laboratory breakthroughs and industrial viability. Collectively, these insights frame Li2S not only as an alternative, cathode, but also as a platform for reimagining Li─S electrochemistry in the post‐lithium‐metal era.

Min Huang, Yanan Wang, Man Zhou et al.

To enhance photocatalytic activity, we focused on improving the efficiency of electron transport. A series of PCN-222/Zn3In2S6 composites was synthesized via combining a zirconium metal-organic framework (PCN-222) with Zn3In2S6 nanosheets with in situ growth methods. The optimized composite, designated as PCN-222/Zn3In2S6-2 (PCN/ZIS-2), showed the superior photocatalytic activity, achieving an 89% removal efficiency of chlortetracycline hydrochloride (CTC) in 180 minutes (kobs = 0.4748 min−1). Electron paramagnetic resonance (EPR) and free radical trapping experiments verified that superoxide anion (•O2−) and holes (h+) are the primary active species involved in the degradation process. Additionally, we analyzed the plausible degradation pathways of CTC through a liquid chromatograph-tandem mass spectrometer (LC–MS). An ecotoxicological assessment of byproducts from CTC implied that these degradation pathways can lower both acute and chronic toxicity. These results offer a promising approach for reducing antibiotic pollution in water and pave the way for developing efficient photocatalytic materials for environmental cleanup.

Francesco Ragusa, Rosario Leonardi, Michele Mazzamuto et al.

Understanding human behavior from complementary egocentric (ego) and exocentric (exo) points of view enables the development of systems that can support workers in industrial environments and enhance their safety. However, progress in this area is hindered by the lack of datasets capturing both views in realistic industrial scenarios. To address this gap, we propose ENIGMA-360, a new ego-exo dataset acquired in a real industrial scenario. The dataset is composed of 180 egocentric and 180 exocentric procedural videos temporally synchronized offering complementary information of the same scene. The 360 videos have been labeled with temporal and spatial annotations, enabling the study of different aspects of human behavior in industrial domain. We provide baseline experiments for 3 foundational tasks for human behavior understanding: 1) Temporal Action Segmentation, 2) Keystep Recognition and 3) Egocentric Human-Object Interaction Detection, showing the limits of state-of-the-art approaches on this challenging scenario. These results highlight the need for new models capable of robust ego-exo understanding in real-world environments. We publicly release the dataset and its annotations at https://fpv-iplab.github.io/ENIGMA-360/.

Annemarie Jutte, Uraz Odyurt

Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results in non-transparent operation. Rigorous evaluation is needed to prevent models from exhibiting unexpected behaviour on future, unseen data. Explainable AI (XAI) can be used to uncover model reasoning, allowing a more extensive analysis of behaviour. We apply XAI to improve predictive performance of ML models intended for an industrial CPS use-case. We analyse the effects of components from time-series data decomposition on model predictions using SHAP values. Through this method, we observe evidence on the lack of sufficient contextual information during model training. By increasing the window size of data instances, informed by the XAI findings for this use-case, we are able to improve model performance.

Jintai Li, Songqiang Chen, Shuo Jin et al.

Code summaries are essential for helping developers understand code functionality and reducing maintenance and collaboration costs. Although recent advances in large language models (LLMs) have significantly improved automatic code summarization, the practical usefulness of generated summaries in industrial settings remains insufficiently explored. In collaboration with documentation experts from the industrial HarmonyOS project, we conducted a questionnaire study showing that over 57.4% of code summaries produced by state-of-the-art approaches were rejected due to violations of developers' expectations for industrial documentation. Beyond semantic similarity to reference summaries, developers emphasize additional requirements, including the use of appropriate domain terminology, explicit function categorization, and the avoidance of redundant implementation details. To address these expectations, we propose ExpSum, an expectation-aware code summarization approach that integrates function metadata abstraction, informative metadata filtering, context-aware domain knowledge retrieval, and constraint-driven prompting to guide LLMs in generating structured, expectation-aligned summaries. We evaluate ExpSum on the HarmonyOS project and widely used code summarization benchmarks. Experimental results show that ExpSum consistently outperforms all baselines, achieving improvements of up to 26.71% in BLEU-4 and 20.10% in ROUGE-L on HarmonyOS. Furthermore, LLM-based evaluations indicate that ExpSum-generated summaries better align with developer expectations across other projects, demonstrating its effectiveness for industrial code documentation.

Liz Li, Wei Zhu

While retrieval augmented generation (RAG) has been swiftly adopted in industrial applications based on large language models (LLMs), there is no consensus on what are the best practices for building a RAG system in terms of what are the components, how to organize these components and how to implement each component for the industrial applications, especially in the medical domain. In this work, we first carefully analyze each component of the RAG system and propose practical alternatives for each component. Then, we conduct systematic evaluations on three types of tasks, revealing the best practices for improving the RAG system and how LLM-based RAG systems make trade-offs between performance and efficiency.

R. Speelman, Ezra J. Marker, F. M. Geiger

While water’s oxygen is the electron source in the industrially important oxygen evolution reaction, the strong absorber problem clouds our view of how the Stern layer water molecules orient themselves in response to applied potentials. Here, we report nonlinear optical measurements on nickel electrodes held at pH 13 indicating a disorder-to-order transition in the Stern layer water molecules before the onset of Faradaic current. A full water monolayer (1.1 × 1015 centimeter−2) aligns with oxygen atoms pointing toward the electrode at +0.8 volt and the associated work is 80 kilojoule per mole. Our experiments identify water flipping energetics as a target for understanding overpotentials, advance molecular electrochemistry, provide benchmarks for electrical double layer models, and serve as a diagnostic tool for understanding electrocatalysis.

C. Bloomquist, Daniel Naumov, Ahrin Yang et al.

Electrochemistry can enable sustainable chemical manufacturing but is limited by the reactions possible with conventional metal electrodes. Plasma electrochemistry, which replaces a conventional solid electrode with plasma in electrochemical cells, opens new avenues for chemical synthesis by combining Faradaic and non-Faradaic processes at the plasma-liquid interface. To understand how plasma electrochemistry differs from conventional electrochemistry, we investigated plasma reactions with acrylonitrile, an industrially relevant molecule used as the precursor in the well-characterized electrosynthesis of adiponitrile. We demonstrate that non-Faradaic processes dominate plasma-driven chemistry through systematic variation of plasma polarity, current, and reactant concentration, combined with comprehensive quantitative analysis of solid, liquid, and gas products. Most notably, we observed no adiponitrile formation (the desired electrochemical product), while total product yields exceeded the theoretical charge-transfer maximum by up to 32-fold. Substantial polyacrylonitrile formation occurred under all conditions, a product not typically seen in conventional electrochemistry. The plasma anode produced consistently higher yields than the plasma cathode, generating hydrogen and propionitrile at 21 and 2 times the charge-transfer maximum, respectively. Electron scavenger experiments confirmed these transformations occurred primarily through non-Faradaic processes rather than charge transfer. These results demonstrate that plasma electrochemistry with acrylonitrile is primarily driven by non-Faradaic processes at plasma-electrolyte interfaces, providing fundamental insights for harnessing these interactions in chemical synthesis.

Bo Wu, Ruihu Lu, Chao Wu et al.

Employing electrochemistry for the selective functionalization of liquid alkanes allows for sustainable and efficient production of high-value chemicals. However, the large potentials required for C(sp3)-H bond functionalization and low water solubility of such alkanes make it challenging. Here we discover that a Pt/IrOx electrocatalyst with optimized Cl binding energy enables selective generation of Cl free radicals for C-H chlorination of alkanes. For instance, we achieve monochlorination of cyclohexane with a current up to 5 A, Faradaic efficiency (FE) up to 95% and stable performance over 100 h in aqueous KCl electrolyte. We further demonstrate that our system can directly utilize concentrated seawater derived from a solar evaporation reverse osmosis process, achieving a FE of 93.8% towards chlorocyclohexane at a current of 1 A. By coupling to a photovoltaic module, we showcase solar-driven production of chlorocyclohexane using concentrated seawater in a membrane electrode assembly cell without any external bias. Our findings constitute a sustainable pathway towards renewable energy driven chemicals manufacture using abundant feedstock at industrially relevant rates. Large potentials required for C(sp3)-H bond activation and low water solubility make electrochemical functionalization of alkanes challenging. Here, the authors report that Pt/IrOx enables selective generation of Cl free radicals for chlorination of cyclohexane at industrially relevant rates.

Jingchen Na, Jun Chi, Senyuan Jia et al.

Seawater electrolysis cooperates well with renewable energy sources at arid coastal photovoltaic power parks and maritime mobile wind farms, thus minimizing hydrogen production expenditure. However, the deleterious electrochemistry of aggressive chloride ions proceeding in two‐electron processes reveals a kinetic preponderance compared to the sluggish four‐electron seawater oxygen evolution reaction (OER), restricting the efficiency and durability of seawater electrolyzers. In this work, the decoration of low electronegativity Cu2+/1+ cation substituents is proposed to trigger the dual‐site lattice oxygen mechanism (LOM), which can involve both the metal sites and anionic ligand defect sites to participate in seawater OER. Theoretical and experimental investigations indicate the modified band structure and electron interaction of the CuO@NiCoS NAs heterostructure, thus inducing the defect center formation and optimizing the anodic electrochemistry during seawater electrolysis. The operando electrochemistry techniques unveil the promoted anodic activation and LOM catalysis enabled by the inductive effect of Cu─O groups, which corresponds to the inhibited chloride corrosion and facilitated seawater oxidation. Hence, the non‐noble metal‐based electrolyzer consisting of CuO@NiCoS NAs/NF (anode) || NiCoS NAs/NF (cathode) exhibits a promising durability during the alkaline seawater electrolysis, exceeding 2000 h at an industrial‐scale current density of 1.0 A cm−2.

Yaoli Zhao, Kyle Leatt, K. Prabakar et al.

Real-time detection of hazardous chemicals in water that appear at parts-per-trillion level concentrations with high sensitivity and selectivity is challenging. Achieving ultrahigh sensitivity using techniques based on immobilized receptors and electrochemistry requires the use of preconcentrators for sample enrichment, which imposes a time penalty. In addition, the use of immobilized receptors results in sensor-to-sensor irreproducibility challenges. Here, we demonstrate a real-time standoff technique for the ultrasensitive detection of chemical species in water based on a nanomechanical photothermal technique. This method involves exciting trace amounts of molecules in water by using tunable infrared light and directing the scattered light onto a cantilever, inducing mechanical deflections. The wavelength-dependent nanomechanical deflections were then used to quantify the contaminants in the water. We demonstrate this technique by selective detection of 1 part-per-trillion perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), and fluorotelomer alcohol (FTOH) in distilled and deionized water without using preconcentrators, labels, or receptors and demonstrate its functionality in industrial surface water samples. The proposed method also does not require sample preparation and is amenable to deployment in field applications.

Long Zhang, Ziqi Ren, Li Sun et al.

The depletion of energy sources, worsening environmental issues, and the quantum limitations of integrated circuits for information storage in the post-Moore era are pressing global concerns. Fortunately, two-dimensional (2D) Janus materials, possessing broken spatial symmetry, with emerging non-linear optical response, piezoelectricity, valley polarization, Rashba spin splitting, and more, have established a substantial platform for exploring and applying modifiable physical, chemical, and biological properties in materials science and offered a promising solution for these energy and information issues. To provide researchers with a comprehensive repository of the 2D Janus family, this review systematically summarizes their theoretical predictions, experimental preparations, and modulation strategies. It also reviews the recent advances in tunable properties, applications, and inherent mechanisms in optics, catalysis, piezoelectricity, electrochemistry, thermoelectricity, magnetism, and electronics, with a focus on experimentally realized hexagonal and trigonal Janus structures. Additionally, their current research state is summarized, and potential opportunities and challenges that may arise are highlighted. Overall, this review aims to serve as a valuable resource for designing, fabricating, regulating, and applying 2D Janus systems, both theoretically and experimentally. This review will strongly promote the advanced academic investigations and industrial applications of 2D Janus materials in energy and information fields.

Nida Aydogdu Ozdogan, Ersin Demir, Sibel A. Ozkan

Abstract Daptomycin is a pioneer cyclic lipopeptide antibiotic introduced for clinical use. It is effective against gram‐positive bacteria, but its widespread use raises the problem of pollution in environmental samples. For this purpose, rapid, sensitive, selective, and applicable analytical methods for daptomycin in these environmental matrices are needed. In this work, electrochemical method was advanced with a glassy carbon electrode (GCE) and newly developed multi‐walled carbon nanotubes/titanium dioxide nanoparticles/titanium dioxide nanoparticles modified GCE (MWCNT/TiO2/TiO2/GCE) for the daptomycin detection using adsorptive stripping differential pulse voltammetry. The surface characterization of the supported sensor was researched. Under optimized conditions, the linear range for the unmodified electrode and MWCNT/TiO2/TiO2/GCE was 0.2–1.0 μM and 0.06–5.0 μM, with detection limits of 0.086 μM and 0.001 μM. The selectivity of the proposed sensor was investigated for organic and inorganic compounds that could affect the detection of daptomycin by interference studies. The accuracy of the methods proposed for determining daptomycin in different environmental (soil, natural spring, and tap water) samples was calculated as % recovery in recovery studies. A novel, fast, reliable, cost‐effective, eco‐friendly, sensitive, and highly selective sensor was developed for the first time to determine daptomycin in environmental samples, introducing a new analytical method to the literature.

Tim Wicke, Lukas Weymann, Christoph Neef et al.

Factory announcements for battery production are increasing in number as European demand for battery cells grows. Using a Monte Carlo simulation (108 projects as of October 2025) with risk factors for individual projects, the predicted theoretical production capacity for lithium-ion batteries in Europe will rise to 1.1–1.5 TWh, enabling a real production output of 0.8–1.0 TWh by 2030. Our analysis suggests necessary cumulative investments in battery cell gigafactories of 36–139 billion euros by 2030. The industrial output of LIB cells in 2030 will have a value of 35–99 billion euros, of which the market size of battery production is around 6–17 billion euros. Furthermore, 43,000–174,000 direct jobs could be created, with the strongest impacts seen in Eastern Europe by the end of the decade. The raw material demand generated by this industry rises steeply: lithium will rise from 14 kt in 2025 to 47–133 kt, and nickel from 83 kt to 226–640 kt by 2030, implying continued import dependencies. The energy demand of European cell production will be 8.4–19.9 TWh in 2030. Furthermore, CO₂ emissions of cell production will be 1.6 to 3.7 Mt CO₂-eq in 2030. The volume of production scrap is estimated at 160–398 kt in 2030, creating near-term demand for recycling capacities.

Chaozheng Wang, Zezhou Yang, Shuzheng Gao et al.

Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the emergence of Large Code Models (LCMs), this field has witnessed significant advancements. Due to the natural differences between open-source and industrial codebases, such as coding patterns and unique internal dependencies, it is a common practice for developers to conduct domain adaptation when adopting LCMs in industry. There exist multiple adaptation approaches, among which retrieval-augmented generation (RAG) and fine-tuning are the two most popular paradigms. However, no prior research has explored the trade-off of the two approaches in industrial scenarios. To mitigate the gap, we comprehensively compare the two paradigms including Retrieval-Augmented Generation (RAG) and Fine-tuning (FT), for industrial code completion in this paper. In collaboration with Tencent's WXG department, we collect over 160,000 internal C++ files as our codebase. We then compare the two types of adaptation approaches from three dimensions that are concerned by industrial practitioners, including effectiveness, efficiency, and parameter sensitivity, using six LCMs. Our findings reveal that RAG, when implemented with appropriate embedding models that map code snippets into dense vector representations, can achieve higher accuracy than fine-tuning alone. Specifically, BM25 presents superior retrieval effectiveness and efficiency among studied RAG methods. Moreover, RAG and fine-tuning are orthogonal and their combination leads to further improvement. We also observe that RAG demonstrates better scalability than FT, showing more sustained performance gains with larger scales of codebase.

Huajie Liang, Di Wang, Yuchao Lu et al.

With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal interactions, while also being affected by noise interference. These complexities make it challenging for traditional anomaly detection methods to extract key features, impacting detection accuracy and stability. Traditional machine learning approaches often struggle with such complex data due to limitations in processing capacity and generalisation ability, making them inadequate for practical applications. While deep learning feature extraction modules have demonstrated remarkable performance in image and text processing, they remain ineffective when applied to multi-source heterogeneous industrial data lacking explicit correlations. Moreover, existing multi-source heterogeneous data processing techniques still rely on dimensionality reduction and feature selection, which can lead to information loss and difficulty in capturing high-order interactions. To address these challenges, this study applies the EAPCR and Time-EAPCR models proposed in previous research and introduces a new model, Time-EAPCR-T, where Transformer replaces the LSTM module in the time-series processing component of Time-EAPCR. This modification effectively addresses multi-source data heterogeneity, facilitates efficient multi-source feature fusion, and enhances the temporal feature extraction capabilities of multi-source industrial data.Experimental results demonstrate that the proposed method outperforms existing approaches across four industrial datasets, highlighting its broad application potential.