Hasil untuk "Industrial electrochemistry"

Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian et al.

The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.

Ana L. Azevedo Costa, Mareike Liebertseder, Tatiana Gambaryan-Roisman et al.

Silicon (Si) is a promising anode material for next-generation lithium-ion batteries (LIBs) due to its high theoretical capacity. However, its practical application is hindered by significant volume changes during cycling, leading to particle pulverization, loss of electrical contact, and rapid capacity fading. To address these challenges, we study the effect of dry particle coating with nanostructured fumed metal oxides (TiO2, MgO, ZrO2, and Al2O3) on enhancing the electrochemical performance of SiOx/C anodes. The dry coating process, a facile and scalable technique, effectively attaches the metal oxide nanoparticles onto the SiOx/C surface, forming a protective layer. The coated anode active materials (AAMs) exhibit improved cycling stability and rate capability compared to the uncoated SiOx/C, with the Al2O3-coated anode demonstrating the most promising overall performance. The effective and uniform distribution of the porous coating acts as a protective layer, reducing side reactions while simultaneously enhancing ion diffusion kinetics and improving electrolyte accessibility. Detailed characterization reveals that the Al2O3 coating promotes the controlled formation of a LiF-rich solid electrolyte interphase (SEI) layer, contributing to enhanced ionic conductivity and stability. This study highlights the potential of dry particle coating with different metal oxides as a promising strategy for developing high-performance Si-based anodes for next-generation LIBs.

Ziyue Song, Tianjie Feng, Donald W. Kirk et al.

This study investigated the electrochemical properties of supercapacitors by pre-modifying thick birch biochar monoliths with FeCl₃ or C₆H₅FeO₇ solutions prior to wood pyrolysis. The pre-modification introduced iron species to the surface, promoting the specific surface area, charge-stored species, and surface functionalities, which enhanced the gravimetric capacitance. X-ray diffraction confirmed the successful loading of Fe₃O₄ and Fe. SEM implied the wider distribution of iron-rich particulates and porous carbon via self-pyrolysis on the biochar surface modified with 1.0 M C₆H₅FeO₇. Contact angle measurements demonstrated the enhanced wettability of the biochar surfaces following pre-modification, with the C₆H₅FeO₇-modified samples exhibiting superior wettability compared to the other groups. The gravimetric capacitance of the supercapacitor was dramatically promoted and reached 210 F/g and 219 F/g, respectively, when modified with 1.0M C₆H₅FeO₇ and 1.0 M FeCl₃ at a 5 mA/g current density. Compared to the birch biochar modified with 1.0 M FeCl₃, the 1.0 M C₆H₅FeO₇ had a higher current response peak and capacitive behavior in the CV analysis, demonstrated better ion diffusion capacity, and had lower charge-transfer resistance in the EIS results. But, a slight irreversible process on the electrode of the 1.0 M C₆H₅FeO₇ group led to a lower level of the supercapacitor capacitance retention. The results using ferric solution pre-impregnation show how iron species doping can improve capacitance behavior, providing a feasible scheme for the modification of thick biochar monolith.

Adib Bin Rashid, Tasfia Saba, Sohag Das Sourav et al.

Electric Discharge Machining (EDM) is commonly used to machine hard materials. However, making small and precise features with EDM requires careful control of process parameters. This study presents an improved method to drill micro-holes in 1 mm-thick stainless steel (SS 316L) using a 0.6 mm copper electrode. The key novelty of this work lies in the integration of Response Surface Methodology (RSM) and Central Composite Design (CCD) for multi-response optimization, coupled with validation through experimental testing and microstructural analysis via Scanning Electron Microscopy (SEM). The influence of peak current (2–6 A), pulse-on time (10–40 μs), and pulse-off time (4–6 μs) was evaluated across response factors such as micro-hardness, edge deviation, overcut, material removal rate (MRR), taper angle, and tool wear rate (TWR). The optimal parameter combination is 2 A current, 16.577 μs pulse-on time, and 6 μs pulse-off time, yielded a high desirability score of 8.33, with corresponding results of 283.98 HV microhardness, 7.625 μm (entry) and 5.321 μm (exit) edge deviation, −43.691 μm (entry) and −166.271 μm (exit) overcut, 3.538 g/min MRR, taper angle of 1.877°, and 1.811 g/min TWR. Experimental validation showed strong concordance with the outcomes predicted by the RSM. SEM analysis revealed negligible recast layer and consistent taper geometry, affirming the reliability of the optimized conditions for high-precision micromachining.

Hekun Zhang, Zhendong Zhang, Yelin Deng et al.

Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs.

Claudio Pirrone, Stefano Fricano, Gioacchino Fazio

The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.

Sangbum Choi, Kyeongryeol Go, Taewoong Jang

Foundation models have revolutionized AI, yet they struggle with zero-shot deployment in real-world industrial settings due to a lack of high-quality, domain-specific datasets. To bridge this gap, Superb AI introduces ZERO, an industry-ready vision foundation model that leverages multi-modal prompting (textual and visual) for generalization without retraining. Trained on a compact yet representative 0.9 million annotated samples from a proprietary billion-scale industrial dataset, ZERO demonstrates competitive performance on academic benchmarks like LVIS-Val and significantly outperforms existing models across 37 diverse industrial datasets. Furthermore, ZERO achieved 2nd place in the CVPR 2025 Object Instance Detection Challenge and 4th place in the Foundational Few-shot Object Detection Challenge, highlighting its practical deployability and generalizability with minimal adaptation and limited data. To the best of our knowledge, ZERO is the first vision foundation model explicitly built for domain-specific, zero-shot industrial applications.

Sanjukta Ghosh

Industrial diagrams such as piping and instrumentation diagrams (P&IDs) are essential for the design, operation, and maintenance of industrial plants. Converting these diagrams into digital form is an important step toward building digital twins and enabling intelligent industrial automation. A central challenge in this digitalization process is accurate object detection. Although recent advances have significantly improved object detection algorithms, there remains a lack of methods to automatically evaluate the quality of their outputs. This paper addresses this gap by introducing a framework that employs Visual Language Models (VLMs) to assess object detection results and guide their refinement. The approach exploits the multimodal capabilities of VLMs to identify missing or inconsistent detections, thereby enabling automated quality assessment and improving overall detection performance on complex industrial diagrams.

Changmin Lim, Shinyeong Park, Jiwon Chang

The interfacial resistance between NiSi2 and n-type doped Si was investigated using density functional theory calculations with hybrid functionals. We explored the resistance of Si at different doping concentrations by assigning an effective potential to each Si atom. Then, the valley filtering effect at the NiSi2/Si interface was estimated by comparing the transmission spectra of NiSi2 and Si. We also examined the interfacial resistance between NiSi2 and hyperdoped Si with substitutional n-type dopants, including pnictogen (P, As and Sb) and chalcogen (Se and Te) atoms. Two types of substitutional dopant structures (a single dopant and a dopant dimer) were considered. The formation and binding energies of a single P/Te and a P/Te dimer were investigated to understand the stability in Si. The resistances of Si with a single dopant and with a dopant dimer at high doping concentrations were calculated to show that the resistance as low as ∼ 4×10−11Ω·cm2 can be achieved with a single dopant (P, As and Sb). However, at high doping concentration where a dopant dimer forms, a P dimer cannot effectively donate electrons, resulting in high resistance, while a Te dimer can still provide electrons, achieving a resistance of ∼ 2×10−10Ω·cm2. Therefore, the chalcogen deep donor atoms (Se and Te) can be effective n-type donors and lower the silicide contact resistance at the interface where Si is extremely highly n-type doped.

Benjamin R. Howell, Joshua W. Gallaway

Abstract All‐solid‐state Li batteries are desired for better safety and energy density than Li‐ion batteries. However, the lack of a penetrating liquid electrolyte requires a much different approach to the design of cathodes. The solid catholyte must enable good Li+ conduction, form good interfaces with active material particles, and have the strength to bind the cathode together during repeated volume changes. Catholyte formulation is often simply adapted from Li‐ion design principles, adding a Li salt to the PVDF binder. Here we show that such a PVDF binder at 10 wt % loading is a starved catholyte condition that compromises cell performance. By substituting a 70 : 30 blend of PVDF:PEO, performance is improved while maintaining nearly the same areal loading of LFP active material. Increasing the catholyte fraction to 16 % can also improve performance, but in this case the benefit of including PEO is lessened, with PVDF alone being an adequate catholyte. EIS analysis shows that PEO helps to form charge transfer interfaces at 10 % catholyte, but that its inclusion can degrade interfaces when there is ample catholyte at 16 %. It is also shown that catholyte agglomeration can impede bulk Li conduction, indicating that microstructural factors are of critical importance.

Kumaraswamy J

An effective way for creating superior-grade metal matrix composites (MMCs) using Al composites is the stir-casting technique. Stir-casting stands out among the array of available methods, being a frequently adopted technique. This study focuses on producing Al7075/B4C + Al2O3 hybrid MMCs by incorporating different weight proportions of Al2O3 (3–12%) while maintaining a consistent weight proportion of B4C (6%). The microstructural analysis demonstrates that the B4C/Al2O3 particles are evenly dispersed throughout the Al matrix. A complete investigation was carried out to evaluate the Run in and steady state wear mechanism and hardness throughout distinct phases. Notably, when contrasted with the interface and matrix phases, the particle phase (Top phase) of the hybrid composites achieves the highest hardness. The wear rate and coefficient of friction of the composites were both decreased by the addition of B4C/Al2O3 particles. The composites reinforced with B4C/Al2O3 showed the greatest decreases in wear rate and coefficient of friction. A comparison of the wear characteristics of the created composite and the conventional brake drum material, namely cast iron, was also done with regard to industrial sustainability. It was discovered that the wear rate of B4C/Al2O3-reinforced composites was comparable to that of automotive industry-used cast iron brake drums. According to SEM data, abrasive wear at lower stresses and adhesive wear at higher loads mostly helped in material removal.

Joshua W. Pinder, George H. Major, Donald R. Baer et al.

Despite numerous tutorials and standards written to the technical community on X-ray photoelectron spectroscopy (XPS), difficulties with data acquisition, analysis, and reporting persist. This work focuses on common errors in XPS that are frequently observed in the scientific literature and their sources. Indeed, this work covers: (i) XPS data collection, initial data analysis, and data presentation, (ii) Handling XPS backgrounds, (iii) Common errors in XPS peak fitting, and (iv) XPS data presentation and reporting. Graphical examples of errors and appropriate ways of handling data and correcting errors are provided. Additional readings are listed for greater in-depth exploration of the subjects discussed.

Valentina Zaccaria, Chiara Masiero, David Dandolo et al.

While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Chen Li, Ruijie Ma, Xiang Qian et al.

Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.

Hirotada Gamo, Atsushi Nagai, Atsunori Matsuda

All-solid-state batteries (ASSBs) are promising to be next-generation battery that provides high energy density and intrinsic safety. Research in the field of ASSBs has so far focused on the development of highly conductive solid electrolytes (SEs). The commercialization of ASSBs requires well-established large-scale manufacturing for sulfide SEs with high ionic conductivity. However, the synthesis for sulfide SEs remains at the laboratory scale with limited scalability owing to their air sensitivity. The liquid-phase synthesis would be an economically viable manufacturing technology for sulfide SEs. Herein, we review a chemical perspective in liquid-phase synthesis that offers high scalability, low cost, and high reaction kinetics. This review provides a guideline for desirable solvent selection based on the solubility and polarity characterized by the donor number and dielectric permittivity of solvents. Additionally, we offer a deeper understanding of the recent works on scalable liquid-phase synthesis using solubilizers and reactant agents. We present an outlook on a universal liquid-phase synthesis of sulfide SEs toward the commercialization of sulfide-based ASSBs.

Mahbubul Islam, Hossain Md. Mubashshir Jamil, Samiul Ahsan Pranto et al.

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the suitable device for the suitable usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. Moreover, most of them are wireless protocols due to the sheer number of device requirements for IoT. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of critical features, including power consumption, coverage, data rate, security, cost, and Quality of Service (QoS). This comparative study shows that Low Power Wide Area Network (LPWAN) based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M ) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable protocol for an industrial and production field.

Jiaqi Liu, Guoyang Xie, Jinbao Wang et al.

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

Yue Wang, Jinlong Peng, Jiangning Zhang et al.

2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code is available at https://github.com/nomewang/M3DM.

Paul J. A. Kenis, M. Inman

The Industrial Electrochemistry and Electrochemical Engineering (IE&EE) division was established in 1943. This session will feature a panel discussion where experts in the field will share their thoughts on the evolution of in industrial electrochemistry and electrochemical engineering over the years, as well as current trends and future opportunities in these fields. Confirmed panelists will be announced in this abstract before the meeting.