Hasil untuk "Industrial electrochemistry"

Hyeong Seok Yu, Donghoon Han

Operando analysis enables real-time monitoring of chemical processes, with spectroelectrochemical methods offering direct mechanistic insight. Here, we present an electrochemical-fluorescence approach that couples time-resolved fluorescence detection with nanoparticle collision electrochemistry. Fluorescein-functionalized silver nanoparticles (AgNPs) serve as dual probes, providing simultaneous picoampere-level current and fluorescence readouts. At low electrode potentials, weak electrostatic attraction limits oxidation, yielding few correlated current-fluorescence events. Increasing the potential enhances both collision frequency and electron transfer, producing more coincident spikes. In contrast, fluorescence spikes without current signals were suggested to be associated with 11-Mercapto-1-undecanol (MUD) functionalization that may reduce electron transfer. By correlating electrochemical and optical outputs, this strategy distinguishes reactive from nonreactive collisions. More broadly, it establishes a versatile platform for resolving single-nanoparticle reactivity beyond electrochemistry alone, with implications for catalysis, biosensing, and nanoparticle tracking in complex media.

Flavien Armangeon, Thibaud Ehret, Enric Meinhardt-Llopis et al.

Aligning functional schematics with 2D and 3D scene acquisitions is crucial for building digital twins, especially for old industrial facilities that lack native digital models. Current manual alignment using images and LiDAR data does not scale due to tediousness and complexity of industrial sites. Inconsistencies between schematics and reality, and the scarcity of public industrial datasets, make the problem both challenging and underexplored. This paper introduces IRIS-v2, a comprehensive dataset to support further research. It includes images, point clouds, 2D annotated boxes and segmentation masks, a CAD model, 3D pipe routing information, and the P&ID (Piping and Instrumentation Diagram). The alignment is experimented on a practical case study, aiming at reducing the time required for this task by combining segmentation and graph matching.

Christophe Ponsard, Abiola Paterne Chokki, Jean-François Daune

Cyber-Physical Systems (CPS) play a critical role in modern industrial domains, including manufacturing, energy, transportation, and healthcare, where they enable automation, optimization, and real-time decision-making. Ensuring the robustness of these systems is paramount, as failures can have significant economic, operational, and safety consequences. This paper present findings from an industrial survey conducted in Wallonia, covering a wide range of sectors, to assess the current state of practice in CPS robustness. It investigates robustness from how it is understood and applied in relationship with requirements engineering, system design, test execution, failure modes, and available tools. It identifies key challenges and gaps between industry practices and state-of-the-art methodologies. Additionally, it compares our findings with similar industrial surveys from the literature.

Niklas Wolf, Ali Javed, Leander Treutlein et al.

ABSTRACT Conditioning of the membrane electrode assembly (MEA) is an important step to establish functionality and obtain a consistent performance of the proton exchange membrane electrolytic cell (PEMEC) when setting it into operation. On a laboratory scale in an academic context, conditioning encompasses primary pre‐treatment of the MEA by chemical or thermal procedures under defined mechanical conditions and, secondarily, the break‐in procedure, during which the PEMEC is subjected to initial electrical loads before actual operation. This study demonstrates the effect of MEA conditioning on the short‐term performance of PEMEC. The impact of mechanical, chemical and thermal conditions during pre‐treatment was investigated for Nafion N115‐based MEAs while keeping the break‐in procedure invariant for all pre‐treatment conditions. The electrochemical characterisation was performed using polarisation curves and electrochemical impedance spectroscopy. The impact of ex situ–before assembly of the cell–versus in situ–after assembly of the cell–conditioning resulted in markedly different mechanical conditions. The experimental results showed an improvement in PEMEC performance by pre‐treating the MEA after cell assembly. Compared to pre‐treatment with deionised water (DI water) at 60°C, treatment with acidic solution improved the performance, evidenced by a 21 mV reduction in cell voltage at 2 A·cm−2. When compared with DI water at 60°C, a pre‐treatment at 90°C with DI water reduced cell voltage by 23 mV.

Ruimin Ma, Sebastian Zudaire, Zhen Li et al.

Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textit{Industrial Metallic Dataset (IMD)}, tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.

Evgeny Katz

Aslan Achoh, Denis Bondarev, Elena Nosova et al.

This study focuses on the modification of ion-exchange membranes by incorporating a phosphorylated dendrimer into sulfonated polytetrafluoroethylene membranes to enhance the specific selectivity between mono-/divalent ions, using the Ca²⁺/Na⁺ pair as an example. This research employs mechanical, physicochemical, and electrochemical analyses to explore the effects of P-H20 incorporation on membrane properties. Bulk modification significantly increases membrane selectivity towards calcium ions (the specific permselectivity coefficient rises from 1.5 to 7.2), while maintaining the same level of the limiting current density. Other findings indicate that bulk modification significantly changes the transport-channel structure of the membrane and alters the mechanism of over-limiting mass transfer. The over-limiting current for the pristine membrane is mainly due to non-equilibrium electroconvection, while modified membranes actively participate in the water-splitting reaction, leading to the suppression of the electroconvection. Despite this drawback, the decrease of the over-limiting potential drop results in a decrease in specific energy consumption from 0.11 to 0.07 kWh/mol. In the underlimiting current mode, the specific energy consumption for all studied membranes remains within the same limits of 0.02–0.03 kWh/mol.

Leopold Lahn, Andrea M. Mingers, Alan Savan et al.

Abstract Anodic oxygen evolution reaction (OER) challenges large scale application of proton exchange membrane water electrolyzers (PEMWE) due to sluggish kinetics, high overpotential and extremely corrosive environment. While Ir oxides currently provide the best balance between activity and stability, the scarcity of Ir and corresponding high market price lead to poor cost‐benefit factors. Mixing Ir with more stable non‐precious Ti reduces the noble metal loading and may implicate stabilization, while addition of more catalytically active Ru ensures a high reaction rate. Here, we examine the activity‐stability behavior of Ru‐Ir‐Ti thin film material libraries with low Ti‐content under the OER conditions. The high sensitivity to the dissolution of the individual alloy components was achieved by using online and off‐line inductively coupled plasma mass spectrometry (ICP‐MS) analysis. Our data reveal that even low Ti additions improve the stability of Ru‐Ir catalysts without sacrificing activity. In particular, 5 at. % of Ti enable stability increase of Ir in the Ru‐Ir catalyst by a factor of 3. Moreover, this catalyst exhibits higher activity compared to the Ti‐free Ru‐Ir alloys with similar Ir content. Observed activity‐stability trends are discussed in light of X‐ray photoelectron spectroscopy data.

Dimitrios Kouzapas, Christos G. Panayiotou, Demetrios G. Eliades

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process using semantics for physical and cyber-physical components, a state estimation model, and agent interactions. A novel aspect is using communication semantics to ensure live interaction among distributed agents. Reasoning on the semantic description facilitates the configuration of the industrial process control loop. A Water Distribution Networks domain case study demonstrates iCPS-DL's application.

Wei Zhang, Xianfu Cheng, Yi Zhang et al.

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.

Jiyu Chen, Jinchuan Qian, Xinmin Zhang et al.

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.

Tomislav Duricic, Peter Müllner, Nicole Weidinger et al.

Many industrial sectors rely on well-trained employees that are able to operate complex machinery. In this work, we demonstrate an AI-powered immersive assistance system that supports users in performing complex tasks in industrial environments. Specifically, our system leverages a VR environment that resembles a juice mixer setup. This digital twin of a physical setup simulates complex industrial machinery used to mix preparations or liquids (e.g., similar to the pharmaceutical industry) and includes various containers, sensors, pumps, and flow controllers. This setup demonstrates our system's capabilities in a controlled environment while acting as a proof-of-concept for broader industrial applications. The core components of our multimodal AI assistant are a large language model and a speech-to-text model that process a video and audio recording of an expert performing the task in a VR environment. The video and speech input extracted from the expert's video enables it to provide step-by-step guidance to support users in executing complex tasks. This demonstration showcases the potential of our AI-powered assistant to reduce cognitive load, increase productivity, and enhance safety in industrial environments.

William Farlessyost, Shweta Singh

Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for modeling and assessing the resilience of coupled natural-industrial ecosystems under climate change scenarios. We develop a computationally efficient framework that integrates liquid time-constant (LTC) neural networks as surrogate models to capture complex, nonlinear dynamics of coupled agricultural and industrial systems. The approach is demonstrated through a case study of a soybean-based biodiesel production network in Champaign County, Illinois. LTC models are trained to capture dynamics of nodes and are then coupled and driven by statistically downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096. The framework enables rapid simulation of system-wide material flow dynamics and exploration of cascading effects from climate-induced disruptions. Results reveal non-linear behaviors and potential tipping points in system resilience under different climate scenarios and farm sizes. The RCP 8.5 scenario led to earlier and more frequent production failures, increased reliance on imports for smaller farms, and complex patterns of waste accumulation and stock levels. The methodology provides valuable insights into system vulnerabilities and adaptive capacities, offering decision support for enhancing the resilience and sustainability of coupled natural-industrial ecosystems in the face of climate change. The framework's adaptability suggests potential applications across various industrial ecosystems and climate-sensitive sectors

Markus Voggenreiter, Florian Angermeir, Fabiola Moyón et al.

In recent years, DevOps, the unification of development and operation workflows, has become a trend for the industrial software development lifecycle. Security activities turned into an essential field of application for DevOps principles as they are a fundamental part of secure software development in the industry. A common practice arising from this trend is the automation of security tests that analyze a software product from several perspectives. To effectively improve the security of the analyzed product, the identified security findings must be managed and looped back to the project team for stakeholders to take action. This management must cope with several challenges ranging from low data quality to a consistent prioritization of findings while following DevOps aims. To manage security findings with the same efficiency as other activities in DevOps projects, a methodology for the management of industrial security findings minding DevOps principles is essential. In this paper, we propose a methodology for the management of security findings in industrial DevOps projects, summarizing our research in this domain and presenting the resulting artifact. As an instance of the methodology, we developed the Security Flama, a semantic knowledge base for the automated management of security findings. To analyze the impact of our methodology on industrial practice, we performed a case study on two DevOps projects of a multinational industrial enterprise. The results emphasize the importance of using such an automated methodology in industrial DevOps projects, confirm our approach's usefulness and positive impact on the studied projects, and identify the communication strategy as a crucial factor for usability in practice.

H. Kühnelt, Francesco Mastropierro, Ningxin Zhang et al.

Hybrid electric propulsion is likely to play a more prominent role for regional aircraft with 40+ passengers in the future air transport system with reduced climatic impact. In IMOTHEP, two hybrid-electric regional (HER) aircraft concepts, a conservative and a radical, are developed. Energy dense battery technologies are needed to enable hybrid-electric propulsion of regional aircraft. Furthermore, these batteries need to become commercially available within the planned development time of the new aircraft, i.e. until 2030. This paper will discuss general requirements for the HER battery and aims at providing an overview on the most promising industrial approaches for energy dense battery cell technologies – from advanced Li-ion to all-solid-state – with view of their application in air transport and forecasted availability on the market. Furthermore, results will be presented from the study performed in IMOTHEP on hybrid polymer-ceramic all-solid-state battery electrochemistry that combines lab trials with electrochemical numerical simulation.

Olivier Darrigol

Tong Lin, Haoran Wang, Tianyang Shen et al.

Heavy metal contamination in soil, which is harmful to both ecosystem and mankind, has attracted worldwide attention from the academic and industrial communities. However, the most-widely used remediation technologies such as electrochemistry, elution, and phytoremediation. suffer from either secondary pollution, long cycle time or high cost. In contrast, in situ mineralization technology shows great potential due to its universality, durability and economical efficiency. As such, the development of mineralizers with both high efficiency and low-cost is the core of in situmineralization. In 2021, the concept of 'Super-Stable Mineralization' was proposed for the first time by Kong et al.[1] The layered double hydroxides (denoted as LDHs), with the unique host-guest intercalated structure and multiple interactions between the host laminate and the guest anions, are considered as an ideal class of materials for super-stable mineralization. In this review, we systematically summarize the application of LDHs in the treatment of heavy metal contaminated soil from the view of: 1) the structure-activity relationship of LDHs in in situ mineralization, 2) the advantages of LDHs in mineralizing heavy metals, 3) the scale-up preparation of LDHs-based mineralizers and 4) the practical application of LDHs in treating contaminated soil. At last, we highlight the challenges and opportunities for the rational design of LDH-based mineralizer in the future.

F. Bastkowski, A. Heering, E. Uysal et al.

The recently introduced unified pH ( $${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_{2}\mathrm{O}}$$ pH abs H 2 O ) concept enables rigorous pH measurements in non-aqueous and mixed media while at the same time maintaining comparability to the conventional aqueous pH scale. However, its practical application is hindered by a shortage of reference $${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_{2}\mathrm{O}}$$ pH abs H 2 O values. In order to improve this situation, the European Metrology Research Project (EMPIR) UnipHied (“Realisation of a UnipHied pH scale”) launched an interlaboratory comparison among highly experienced electrochemistry expert laboratories to assign the first such reference $${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_{2}\mathrm{O}}$$ pH abs H 2 O values by adopting an extensive statistical treatment of the reported measurement data: to phosphate buffer in water–ethanol mixture (50 wt% of ethanol) and ammonium formate buffer in pure ethanol. Two different measurement setups — one capable of being easily adopted in industrial applications — have been used to demonstrate the robustness of $${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_{2}\mathrm{O}}$$ pH abs H 2 O measurement. This is an important step towards wider adoption of the $${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_{2}\mathrm{O}}$$ pH abs H 2 O concept in practice, like liquid chromatography, biofuels analysis and electrocatalysis.

M. Naseer, Syed Asad Ali Zaidi, Kingshuk Dutta et al.

Microbial fuel cells (MFC) are attractive for the research community as a promising bioelectricity production technology using organic waste. However, due to low performance and erroneous reproducibility and replicability, MFCs lack industrial application. Additionally, the nonlinear dynamic behavior of MFCs, along with the involvement of electrochemistry and biology in mathematical models, makes them difficult to comprehend and simulate. To overcome these barriers, this study provides a simulation platform for conducting theoretical studies using a fundamental mathematical model of an MFC. This novel Simulink/MATLAB model is based on mass balance across both compartments of an MFC, and provides power density as a function of a wide range of performance-affecting parameters. Model validation depicts only a 2-10% error rate. This model can provide a stepping stone to perform theoretical optimization and industrial application studies in the future. By varying the values of different parameters, studies may be performed to spot optimum values of the most sensitive parameters. Therefore, using the proposed tool paves the path for further improvements in design, cost effectiveness, and performance efficiency that ultimately promises up-scaling of MFCs as a renewable and alternative energy resource.

Phillip Scherzl, Michael Kaupp, Wassima El Mofid et al.

Conventionally, cathode current collectors for lithium-ion batteries (LIB) consist of an aluminum foil generally manufactured by a rolling process. In the present work, a novel one-step manufacturing method of structured aluminum foil current collectors for lithium-ion batteries by electroforming is introduced. For this, a low-temperature chloride-based ionic liquid was used as an electrolyte and a rotating cylinder out of stainless steel as a temporary substrate. It was shown that the structure of the aluminum foils can be adjusted from dense and flat to three-dimensional by choosing an appropriate substrate rotation speed and current density. Scanning electron microscopy (SEM) and white light interferometry (WLI) were utilized to analyze the foils’ surface morphology, structure and topography. The SEM analysis of the aluminum foils showed that the rolling process produced a foil with small grains, while electrodeposition resulted in foils with different degrees of grain growth and seed formation. This was in total agreement with WLI results that revealed significant differences in terms of roughness parameters, including the peak-to-valley difference R_pv, the root-mean-square roughness R_q and the arithmetic mean roughness R_a. These were, respectively, equal to 6.8 µm, 0.35 µm and 0.279 µm for the state-of-the-art foil and up to 96.6 µm, 10.92 µm and 8.783 µm for the structured electroformed foil. Additionally, cyclic voltammetry (CV) of the aluminum foils was used to investigate their passivation behavior within the typical LIB cathode potential operation window. The strong decrease in the current density during the second cycle compared to the first cycle, where an anodic peak appeared between 4.0 and 4.4 V vs. Li/Li⁺, demonstrated that passivation occurs in the same manner as observed for commercial Al current collectors.