Hasil untuk "Industrial electrochemistry"

Giovanni De Gasperis, Sante Dino Facchini

Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial intelligence. This study presents a comparison between these two methodologies, analyzing their respective strengths, limitations, and application scenarios, and proposes a basic framework to evaluate their key properties. Rule-based systems offer high interpretability, deterministic behavior, and ease of implementation in stable environments, making them ideal for regulated industries and safety-critical applications. However, they face challenges with scalability, adaptability, and performance in complex or evolving contexts. Conversely, data-driven systems excel in detecting hidden anomalies, enabling predictive maintenance and dynamic adaptation to new conditions. Despite their high accuracy, these models face challenges related to data availability, explainability, and integration complexity. The paper suggests hybrid solutions as a possible promising direction, combining the transparency of rule-based logic with the analytical power of machine learning. Our hypothesis is that the future of industrial monitoring lies in intelligent, synergic systems that leverage both expert knowledge and data-driven insights. This dual approach enhances resilience, operational efficiency, and trust, paving the way for smarter and more flexible industrial environments.

Eneko Osaba, Iñigo Perez Delgado, Alejandro Mata Ali et al.

This article explores the current state and future prospects of quantum computing in industrial environments. Firstly, it describes three main paradigms in this field of knowledge: gate-based quantum computers, quantum annealers, and tensor networks. The article also examines specific industrial applications, such as bin packing, job shop scheduling, and route planning for robots and vehicles. These applications demonstrate the potential of quantum computing to solve complex problems in the industry. The article concludes by presenting a vision of the directions the field will take in the coming years, also discussing the current limitations of quantum technology. Despite these limitations, quantum computing is emerging as a powerful tool to address industrial challenges in the future.

Ali Raza, Muhammad Farhan Khan, Zeeshan Alam et al.

This paper presents a joint framework that integrates reconfigurable intelligent surfaces (RISs) with Terahertz (THz) communications and non-orthogonal multiple access (NOMA) to enhance smart industrial communications. The proposed system leverages the advantages of RIS and THz bands to improve spectral efficiency, coverage, and reliability key requirements for industrial automation and real-time communications in future 6G networks and beyond. Within this framework, two power allocation strategies are investigated: the first optimally distributes power between near and far industrial nodes, and the second prioritizes network demands to enhance system performance further. A performance evaluation is conducted to compare the sum rate and outage probability against a fixed power allocation scheme. Our scheme achieves up to a 23% sum rate gain over fixed PA at 30 dBm. Simulation results validate the theoretical analysis, demonstrating the effectiveness and robustness of the RIS-assisted NOMA MIMO framework for THz enabled industrial communications.

Hyeon Hye Kim, Kay-Hyeok An, Byung-Joo Kim

The increasing concentration of carbon dioxide (CO₂) in the atmosphere necessitates the development of efficient carbon capture and storage (CCS) technologies. Among these, adsorption-based methods using porous carbon (PC) have attracted considerable attention due to their low energy requirements and cost-effectiveness. Biomass waste-derived porous carbon is particularly attractive as a sustainable alternative, offering environmental benefits and high-value applications with low costs. In this study, coffee grounds (CGs) were selected as a precursor due to their abundance and cost-effectiveness compared with other biomass wastes. To improve the pore characteristics of CG-derived carbon (CCG), boric acid treatment was applied during carbonization followed by steam activation to prepare boron-doped CG-derived porous carbon (B-PCG). The N₂/77K adsorption–desorption isotherms revealed a significant increase in the specific surface area and total pore volume of B-PCG from 1590 m²/g and 0.71 cm³/g to 2060 m²/g and 1.01 cm³/g, respectively, compared with PCG. Furthermore, high pressure CO₂ adsorption analysis at 298 K up to 50 bar showed an approximately 50% improvement in CO₂ adsorption capacity for B-PCG compared with PCG. These results suggest that boron doping is an effective strategy to optimize the pore structure and adsorption performance of biomass-derived porous carbon materials for CCS application.

Po-Hsun Chen, Nguyet.N.T. Pham, Pei-Cheng Huang et al.

This study investigated a combination of PbS quantum dots (QDs) and ZnO nanoparticles (NPs) layers in photodiodes for photodetection. Oxygen vacancies in ZnO NPs have been known to be recombination trap sites, hindering carrier transportation. We used various amines to passivate the oxygen vacancy of ZnO NPs. It is found that ethanolamine (EA) is the most effective in reducing the surface oxygen vacancies of ZnO, exhibiting a five-fold increase in electron mobility, enhancing PbS QD photodiode responsivity to 278.8 A/W and achieving an external quantum efficiency (EQE) of 36,700% under bias, and increasing the detectivity ∼ 15.5 folds to 8.14 × 10¹² Jones compared with the pure ZnO device. This demonstrates the potential of amines as passivation agents to improve PbS photodiode performance with ZnO NPs as the electron transport layer (ETL).

Radu Dorin Andrei, Giorgian Cosmin Ungureanu, Luisa Roxana Mandoc et al.

Solid State Batteries (SSBs) combine the significantly higher energy density (>450 Wh/kg) and enhanced safety required to expedite society's transition away from fossil fuels, making them the most potential ''next-gen'' chemistry for lithium-based batteries. However, issues with electrolyte performance at lower temperatures as well as issues with the effective deposition and stripping of metallic lithium anodes are now impeding their development. In this work, we suggest a completely new strategy to deal with these issues: creating a Solid Molecular Ionic Composite Electrolyte (SMICE), an entirely novel type of gel-polymer electrolyte. This new membrane exhibits outstanding ionic conductivity at room temperature (3.3 mS·cm-1). Following electrochemical tests, the symmetric lithium cells demonstrated long-term cycling stability of approximately 650 h at 25 °C and 1000 h at 60 °C. In half-cell configuration the specific discharge capacity reaches a maximum of 164 mAh·g-1 with a high retention of 95 % after 300 cycles, signifying the stability of SMICE membrane. These results reveal the proper compatibility between the SMICE membrane and the cell electrodes.

Curd Schade, Parinaz Aliasghari, Ruud Egging-Bratseth et al.

The electricity landscape is constantly evolving, with intermittent and distributed electricity supply causing increased variability and uncertainty. The growth in electric vehicles, and electrification on the demand side, further intensifies this issue. Managing the increasing volatility and uncertainty is of critical importance to secure and minimize costs for the energy supply. Smart neighborhoods offer a promising solution to locally manage the supply and demand of energy, which can ultimately lead to cost savings while addressing intermittency features. This study assesses the impact of different electric vehicle charging strategies on smart grid energy costs, specifically accounting for battery degradation due to cycle depths, state of charge, and uncertainties in charging demand and electricity prices. Employing a comprehensive evaluation framework, the research assesses the impacts of different charging strategies on operational costs and battery degradation. Multi-stage stochastic programming is applied to account for uncertainties in electricity prices and electric vehicle charging demand. The findings demonstrate that smart charging can significantly reduce expected energy costs, achieving a 10% cost decrease and reducing battery degradation by up to 30%. We observe that the additional cost reductions from allowing Vehicle-to-Grid supply compared to smart charging are small. Using the additional flexibility aggravates degradation, which reduces the total cost benefits. This means that most benefits are obtainable just by optimized the timing of the charging itself.

Wu Kerui

Diabetes mellitus has emerged as a major chronic disease threatening global health. While traditional glucometers enable point-of- care testing, their invasive nature induces patient resistance that significantly compromises disease management efficacy. Contemporary non-invasive technologies have achieved breakthrough progress, yet critical research gaps persist in multi-physics coupling mechanisms and personalized calibration model development. This comprehensive review analyzes principle innovations and industrial applications of non-invasive glucose monitoring technologies, employing technical evolution pathway analysis and clinical data benchmarking to evaluate seven methodological paradigms - including spectroscopy, electrochemistry, and microwave sensing - along with their translational achievements in wearable devices and healthcare systems. Key findings demonstrate: multi-modal sensing reduces detection errors to 8.7% through signal complementarity, with millimeter-wave radar technology achieving 5-minute continuous monitoring (r=0.912); flexible electronic skin breakthroughs 72-hour operational endurance (sensitivity 0.03nA/(mg/dL)); and intelligent closed-loop systems enhance glycated hemoglobin compliance rates by 42%. Current technical bottlenecks manifest as individual calibration variation coefficients exceeding 12% and blood-interstitial fluid glucose lag (8-15 minutes), with emerging solutions trending toward deep learning-based dynamic compensation models (83% error correction) and terahertz quantum cascade detection (0.1mmol/L detection limit).

Pietro Iob, Mauro Schiavo, Angelo Cenedese

This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

The growing connectivity of industrial devices as a result of the Internet of Things is increasing the risks to Industrial Control Systems. Since attacks on such devices can also cause damage to people and machines, they must be properly secured. Therefore, a threat analysis is required in order to identify weaknesses and thus mitigate the risk. In this paper, we present a systematic and holistic procedure for analyzing the attack surface and threats of Industrial Internet of Things devices. Our approach is to consider all components including hardware, software and data, assets, threats and attacks throughout the entire product life cycle.

Zongjie Li, Wenying Qiu, Pingchuan Ma et al.

Recent years have witnessed the rapid development of large language models (LLMs) in various domains. To better serve the large number of Chinese users, many commercial vendors in China have adopted localization strategies, training and providing local LLMs specifically customized for Chinese users. Furthermore, looking ahead, one of the key future applications of LLMs will be practical deployment in industrial production by enterprises and users in those sectors. However, the accuracy and robustness of LLMs in industrial scenarios have not been well studied. In this paper, we present a comprehensive empirical study on the accuracy and robustness of LLMs in the context of the Chinese industrial production area. We manually collected 1,200 domain-specific problems from 8 different industrial sectors to evaluate LLM accuracy. Furthermore, we designed a metamorphic testing framework containing four industrial-specific stability categories with eight abilities, totaling 13,631 questions with variants to evaluate LLM robustness. In total, we evaluated 9 different LLMs developed by Chinese vendors, as well as four different LLMs developed by global vendors. Our major findings include: (1) Current LLMs exhibit low accuracy in Chinese industrial contexts, with all LLMs scoring less than 0.6. (2) The robustness scores vary across industrial sectors, and local LLMs overall perform worse than global ones. (3) LLM robustness differs significantly across abilities. Global LLMs are more robust under logical-related variants, while advanced local LLMs perform better on problems related to understanding Chinese industrial terminology. Our study results provide valuable guidance for understanding and promoting the industrial domain capabilities of LLMs from both development and industrial enterprise perspectives. The results further motivate possible research directions and tooling support.

Davide Frizzo, Francesco Borsatti, Alessio Arcudi et al.

Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.

Yichun Shi, Yu Pei, Nicholas Lamothe et al.

Abstract Manganese is an emerging concern in drinking water, due to its potential health and aesthetic effects. Although accurate and sensitive, spectroscopic techniques for Mn2+ detection are costly and not capable of rapid detection. Electrochemical methods, such as cathodic stripping voltammetry, have been intensively explored as portable low‐cost methods for Mn2+ detection. Challenges of reliability and matrix interference are difficult to overcome with current electrochemical methods. Among the interference reagents, Fe2+ is one of the biggest challenges for Mn2+ detection. Herein, a new method based on multiplex chronoamperometry at potentials between 0.9 and 1.4 V by a multichannel potentiostat is explored for its ability for interference resistance and applicability for Mn2+ detection in real drinking water samples. Compared to conventional one‐channel electrochemical techniques, the multiplex method generates a reliable pattern that is unique to the sample components. The interference between Mn2+ and Fe2+ is investigated and the results are promising even at 100:1 Fe2+:Mn2+ concentrations. The detection limit determined for the multiplex method was 25.3 μM, and the optimum recovery rate in a real drinking water sample was 99.8%.

Danny Weyns, Thomas Vogel

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community.

T. Taleb, I. Afolabi, M. Bagaa

Industry 4.0 aims at shaking the current manufacturing landscape by leveraging the adoption of smart industrial equipment with increased connectivity, sensing, and actuation capabilities. By exploring access to real-time production information and advanced remote control features, servitization of manufacturing firms promises novel added value services for industrial operators and customers. On the other hand, industrial networks would face a transformation process in order to support the flexibility expected by the next-generation manufacturing processes and enable inter-factory cooperation. In this scenario, the 5G systems can play a key role in enabling Industry 4.0 by extending the network slicing paradigm to specifically support the requirements of industrial use cases over heterogeneous domains. We present a novel 5G-based network slicing framework which aims at accommodating the requirements of Industry 4.0. To interconnect different industrial sites up to the extreme edge, different slices of logical resources can be instantiated on-demand to provide the required end-to-end connectivity and processing features. We validate our proposed framework in three realistic use cases which enabled us highlight the envisioned benefits for industrial stakeholders.

Petr Kostka, Bruno Rossi, Mouzhi Ge

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods.

Markus Dahlmanns, Johannes Lohmöller, Jan Pennekamp et al.

The ongoing trend to move industrial appliances from previously isolated networks to the Internet requires fundamental changes in security to uphold secure and safe operation. Consequently, to ensure end-to-end secure communication and authentication, (i) traditional industrial protocols, e.g., Modbus, are retrofitted with TLS support, and (ii) modern protocols, e.g., MQTT, are directly designed to use TLS. To understand whether these changes indeed lead to secure Industrial Internet of Things deployments, i.e., using TLS-based protocols, which are configured according to security best practices, we perform an Internet-wide security assessment of ten industrial protocols covering the complete IPv4 address space. Our results show that both, retrofitted existing protocols and newly developed secure alternatives, are barely noticeable in the wild. While we find that new protocols have a higher TLS adoption rate than traditional protocols (7.2% vs. 0.4%), the overall adoption of TLS is comparably low (6.5% of hosts). Thus, most industrial deployments (934,736 hosts) are insecurely connected to the Internet. Furthermore, we identify that 42% of hosts with TLS support (26,665 hosts) show security deficits, e.g., missing access control. Finally, we show that support in configuring systems securely, e.g., via configuration templates, is promising to strengthen security.

Ali Ahmad Malik

Assembly is a key industrial process to achieve finished goods. Driven by market demographics and technological advancements, industrial assembly has evolved through several phases i.e. craftmanship, bench assembly, assembly lines and flexible assembly cells. Due to the complexity and variety of assembly tasks, besides significant advancement of automation technologies in other manufacturing activities, humans are still considered vital for assembly operations. The rationalization of manufacturing automation has considerably remained away from assembly systems. The advancement in assembly has only been in terms of better scheduling of work tasks and avoiding of wastes. With smart manufacturing technologies such as collaborative robots, additive manufacturing, and digital twins, the opportunities have arisen for the next reshaping of assembly systems. The new paradigm promises a higher degree of automation yet remaining flexible. This may result into a new manufacturing paradigm driven by the advancement of new technologies, new customer expectations and by establishing new kinds of manufacturing systems. This study explores the future collaborative assembly cells, presents a generic framework to develop them and the basic building blocks.

Jiawen Kang, Dongdong Ye, Jiangtian Nie et al.

The emerging industrial metaverses realize the mapping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby enhancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.

K.B. Kusuma, M. Manju, C.R. Ravikumar et al.

The present work deals with the synthesis of cerium oxide (CeO2) nanoparticles (CONPs) using sodium hydroxide and cerium nitrate as precursors via Sonochemical route. As synthesized, cerium oxide nano powders were calcined at 1000 °C to 1100 °C for 2 hrs. The obtained CONPs were characterised by using the advanced analytical methods. XRD (X-ray diffraction) analysis confirmed the pristine cubic fluorite structure for CeO2 NPs. The FTIR spectrum shows a strong broad band around 800 to 900 cm−1, for Ce-O stretching vibration to confirm the formation of pure CeO2. The particle size of CONPs was found to vary between 35 and 38 nm. Under UV light irradiation of the commercial dye methylene blue, these nano metal oxides demonstrated significant efficiency in photo degradation. A specific capacitance value of 59.78 Fg−1 was obtained from the cyclic voltammograms of the CeO2 electrode in 0.1N HCl solution with a scan rate of 10 to 50 mV/s. These electrodes have proved to be very sensitive towards the detection paracetamol. These findings revealed that CeO2 is an effective photocatalyst and a suitable electrode material for detecting paracetamol with high electrode reversibility.