Hasil untuk "Industrial electrochemistry"

Marcos Kalinowski, Lucas Romao, Ariane Rodrigues et al.

Lean R&D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R&D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R&D's effectiveness in transforming ideas into meaningful business outcomes. Based on responses from 57 participants - including team members, managers, and sponsors - the assessment indicates that stakeholders find the structured phases of Lean R&D well-suited to innovation projects and endorse the approach. Although acknowledging that successful collaboration relies on various factors, this industrial experience positions Lean R&D as a promising framework for industry-academia projects focused on achieving rapid, impactful results for industry partners.

Azmine Toushik Wasi, Enjamamul Haque Eram, Sabrina Afroz Mitu et al.

Industry 5.0 marks a new phase in industrial evolution, emphasizing human-centricity, sustainability, and resilience through the integration of advanced technologies. Within this evolving landscape, Generative AI (GenAI) and autonomous systems are not only transforming industrial processes but also emerging as pivotal geopolitical instruments. We examine strategic implications of GenAI in Industry 5.0, arguing that these technologies have become national assets central to sovereignty, access, and global influence. As countries compete for AI supremacy, growing disparities in talent, computational infrastructure, and data access are reshaping global power hierarchies and accelerating the fragmentation of the digital economy. The human-centric ethos of Industry 5.0, anchored in collaboration between humans and intelligent systems, increasingly conflicts with the autonomy and opacity of GenAI, raising urgent governance challenges related to meaningful human control, dual-use risks, and accountability. We analyze how these dynamics influence defense strategies, industrial competitiveness, and supply chain resilience, including the geopolitical weaponization of export controls and the rise of data sovereignty. Our contribution synthesizes technological, economic, and ethical perspectives to propose a comprehensive framework for navigating the intersection of GenAI and geopolitics. We call for governance models that balance national autonomy with international coordination while safeguarding human-centric values in an increasingly AI-driven world.

Christian W. Frey

Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch

Olaf Sassnick, Georg Schäfer, Thomas Rosenstatter et al.

Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This paper introduces a generative model-based honeypot designed to mimic industrial OPC UA communication. Utilizing a Long ShortTerm Memory (LSTM) network, the honeypot learns the characteristics of a highly dynamic mechatronic system from recorded state space trajectories. Our contributions are twofold: first, we present a proof-of concept for a honeypot based on generative machine-learning models, and second, we publish a dataset for a cyclic industrial process. The results demonstrate that a generative model-based honeypot can feasibly replicate a cyclic industrial process via OPC UA communication. In the short-term, the generative model indicates a stable and plausible trajectory generation, while deviations occur over extended periods. The proposed honeypot implementation operates efficiently on constrained hardware, requiring low computational resources. Future work will focus on improving model accuracy, interaction capabilities, and extending the dataset for broader applications.

D. Salazar-Marín, Goldie Oza, J.A. Díaz Real et al.

In the evolving field of photocatalysis, heterojunction photocatalysts, especially Type II and S-scheme, the latter being also known as direct-Z scheme heterojunctions, are gaining increasing recognition for their pivotal role in enhancing photocatalytic efficiency. These heterojunctions, characterized by similar band alignments but distinct charge transfer mechanisms, play a crucial role in facilitating enhanced charge separation and transfer. This comprehensive review delves into the experimental methodologies essential for characterizing these heterojunctions, with a focus on understanding their unique charge transfer mechanisms. Key methods such as Electron Spin Resonance (ESR), radical trapping experiments, Photoluminescence (PL) probing, Nitro Blue Tetrazolium (NBT) transformation, Surface Photovoltage Spectroscopy (SPS), photodeposition of metals, and in-situ X-ray Photoelectron Spectroscopy (in-situ XPS) analysis are discussed in detail. Each technique is presented with necessary guidelines and accompanying information to ensure their appropriate and effective use in pinpointing the specifics of charge transfer processes. The review concludes that the right selection of experimental techniques is crucial in understanding the charge transfer mechanism in staggered type heterojunctions and achieving further advancements in the field of photocatalysis.

Yeqing Ling, Feifan Huang, Bin Wang et al.

Reversible proton ceramic electrochemical cells (R-PCECs) are of great interest as efficient energy conversion device. Optimization of structural design can enhance the mechanical properties and gas transport of the cells, resulting in improved electrochemical performance. In this study, we developed a 7-channel micro-monolithic R-PCEC for the first time, with uniform channel distribution and smaller gas diffusion pathway length using phase inversion/extrusion technique. The assembled cell with Ni-BaZr0.1Ce0.7Y0.1Yb0.1O3-δ (Ni-BZCYYb, fuel electrode support) | BaZr0.1Ce0.7Y0.1Yb0.1O3-δ (BZCYYb, electrolyte) | PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF, air electrode) structure showed a peak power density of 0.94 W cm−2 at 700 °C in fuel cell mode and electrolysis current density of 2.17 A cm−2 at 700 °C with an operating voltage of 1.3 V. Additionally, electrochemical impedance spectroscopy (EIS) further indicated that the diffusive polarization of the structured cell was effectively reduced compared to single-channel counterpart.

Michele Tribbia, Dr. Jens Glenneberg, Prof. Dr. Fabio La Mantia et al.

Abstract Increasing the electrodeposition efficiency of metallic zinc from quasi‐neutral aqueous electrolytes is one of the major key requirements for the commercialization of rechargeable aqueous Zn‐ion batteries. Several strategies have been recently reported in the literature. Unfortunately, electrochemical studies on the effect of different current densities on the zinc electrodeposition efficiency usually are not recorded in realistic experimental conditions: e. g. depth of discharges <1–10 %, use of negative electrodes with infinite reservoir of Zn2+, etc. Here, the effect of the current density on the zinc electrodeposition onto optimized bismuth‐indium substrates cycled with 33 % of depth of discharge in a ZnSO4‐containing aqueous solution has been investigated. It was found that low Zn plating/stripping current densities displayed higher electrodeposition efficiencies over 200 Zn electrodeposition/dissolution cycles, more homogeneous distribution of the zinc deposits and lower amounts of inactive zinc passivation products. When higher current densities were applied during the Zn plating/stripping cycles, lower electrodeposition efficiencies and a greater amount of inactive zinc hydroxides and dead zinc were observed on the electrode surface.

Juhi Priyani, Pankaj Nanglia, Paramjit Singh et al.

Biometric sensing technology has become a frequent element of everyday life as a result of the global demand for information security and safety legislation. In recent years, multimodal biometrics technology has become increasingly popular due to its ability to overcome the shortcomings of unimodal biometric systems. A HGSSA-Bi LSTM (Bi-directional long short-term memory) modal is presented in this paper for multimodal biometric identification. For removal of noise (unwanted) the pre-processing stage is used in the initial stage. An extended cascaded filter (ECF) is used with a combination of median and wiener filter in the pre-processing stage. Then, using the CNN model, feature extraction is utilized to extract features from the processed images. After feature extraction, fusing of feature is used with the aid of discriminant correlation analysis (DCA). Finally, the recognition process is performed by using the novel optimized hunger game search self-attention based Bi-LSTM model (HGSSA-Bi LSTM). The obtained outcome for the developed model is finally compared with other previous approaches such as CNN, RNN, DNN, and autoencoder models and the calculated performance based on accuracy 98.5%, precision 98%, F1-score 97.5%, sensitivity 98.5%, and specificity 99% accordingly.

Oluwatosin Ogundare, Gustavo Quiros Araya, Ioannis Akrotirianakis et al.

This paper proposes a study of the resilience and efficiency of automatically generated industrial automation and control systems using Large Language Models (LLMs). The approach involves modeling the system using percolation theory to estimate its resilience and formulating the design problem as an optimization problem subject to constraints. Techniques from stochastic optimization and regret analysis are used to find a near-optimal solution with provable regret bounds. The study aims to provide insights into the effectiveness and reliability of automatically generated systems in industrial automation and control, and to identify potential areas for improvement in their design and implementation.

Olav Lamberts, Konrad Wolsing, Eric Wagner et al.

Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward.

Mahmood Akbari, Razieh Morad, Malik Maaza

According to their antiviral and antibacterial capabilities, silver nanoparticles hold great promise in a wide variety of applications, including drug delivery carriers. The coating properties of silver nanoparticles (various sizes ranging from 1 to 5 nm) with the most commonly used anti-malarial drug, artemisinin, were investigated in this study using quantum mechanical and classical atomistic molecular dynamics simulations in order to determine their suitability for use as drug delivery in the treatment of malaria and COVID-19 diseases. Density functional theory (DFT) at the B3LYP/6-311++g(d,p) level of theory was used to simulate the optimal structure, frequency, charge distribution, and electrostatic potential maps of artemisinin. The adsorption of drugs on the Ag nanoparticle (55 silver atoms) was investigated using DFT simulations. Then, using molecular dynamics simulations, the coating of AgNPs (various sizes) with drug molecules was investigated. The influence of AgNPs’ size and composition on the coating with artemisinin was determined in order to identify the most suitable candidate for drug delivery. This type of modeling may aid experimental groups in developing effective and safe therapies.

Bingning Wang, Seoung-Bum Son, Pavan Badami et al.

In our initial study on the high-voltage 5 V cobalt-free spinel LiNi_0.5Mn_1.5O₄ (LNMO) cathode, we discovered a severe delamination issue in the laminates when cycled at a high upper cut-off voltage (UCV) of 4.95 V, especially when a large cell format was used. This delamination problem prompted us to investigate further by studying the transition metal (TM) dissolution mechanism of cobalt-free LNMO cathodes, and as a comparison, some cobalt-containing lithium nickel manganese cobalt oxides (NMC) cathodes, as the leachates from the soaking experiment might be the culprit for the delamination. Unlike other previous reports, we are interested in the intrinsic stability of the cathode in the presence of a baseline Gen2 electrolyte consisting of 1.2 M of LiPF₆ in ethylene carbonate/ethyl methyl carbonate (EC/EMC), similar to a storage condition. The electrode laminates (transition metal oxides, transition metal oxides, TMOs, coated on an Al current collector with a loading level of around 2.5 mAh/cm²) or the TMO powders (pure commercial quality spinel LNMO, NMC, etc.) were stored in the baseline solution, and the transition metal dissolution was studied through nuclear magnetic resonance, such as ¹H NMR, ¹⁹F NMR, scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS) and inductively coupled plasma mass spectrometry (ICP-MS). Significant electrolyte decomposition was observed and could be the cause that leads to the TM dissolution of LNMO. To address this TM dissolution, additives were introduced into the baseline electrolyte, effectively alleviating the issue of TM dissolution. The results suggest that the observed delamination is caused by electrolyte decompositions that lead to etching, and additives such as lithium difluorooxalato borate and p-toluenesulfonyl isocyanate can alleviate this issue by forming a firm cathode electrolyte interface. This study provides a new perspective on cell degradation induced by electrode/electrolyte interactions under storage conditions.

Xian Tao, Xinyi Gong, Xin Zhang et al.

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

Siddhartha Paul Tiwari

Soybean is the most globalized, traded and processed crop commodity. USA, Argentina and Brazil continue to be the top three producers and exporters of soybean and soymeal. Indian soyindustry has also made a mark in the national and global arena. While soymeal, soyoil, lecithin and other soy-derivatives stand to be driven up by commerce, the soyfoods for human health and nutrition need to be further promoted. The changing habitat of commerce in soyderivatives necessitates a shift in strategy, technological tools and policy environment to make Indian soybean industry continue to thrive in the new industrial era. Terms of trade for soyfarming and soy-industry could be further improved. Present trends, volatilities, slowdowns, challenges faced and associated desiderata are accordingly spelt out in the present article.

Tim Schreiter, Lucas Morillo-Mendez, Ravi T. Chadalavada et al.

Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here.

Chao Dong, Xiaoxiong Xiong, Qiulin Xue et al.

Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric - system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.

Cheng Feng

This study focuses on two important problems related to applying offline model-based optimization to real-world industrial control problems. The first problem is how to create a reliable probabilistic model that accurately captures the dynamics present in noisy industrial data. The second problem is how to reliably optimize control parameters without actively collecting feedback from industrial systems. Specifically, we introduce a novel cGAN ensemble-based uncertainty-aware surrogate model for reliable offline model-based optimization in industrial control problems. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on two representative cases, namely a discrete control case and a continuous control case. The results of these experiments show that our method outperforms several competitive baselines in the field of offline model-based optimization for industrial control.

Fangyuan Lei, Da Huang, Jianjian Jiang et al.

In deep learning area, large-scale image datasets bring a breakthrough in the success of object recognition and retrieval. Nowadays, as the embodiment of innovation, the diversity of the industrial goods is significantly larger, in which the incomplete multiview, multimodal and multilabel are different from the traditional dataset. In this paper, we introduce an industrial goods dataset, namely PatentNet, with numerous highly diverse, accurate and detailed annotations of industrial goods images, and corresponding texts. In PatentNet, the images and texts are sourced from design patent. Within over 6M images and corresponding texts of industrial goods labeled manually checked by professionals, PatentNet is the first ongoing industrial goods image database whose varieties are wider than industrial goods datasets used previously for benchmarking. PatentNet organizes millions of images into 32 classes and 219 subclasses based on the Locarno Classification Agreement. Through extensive experiments on image classification, image retrieval and incomplete multiview clustering, we demonstrate that our PatentNet is much more diverse, complex, and challenging, enjoying higher potentials than existing industrial image datasets. Furthermore, the characteristics of incomplete multiview, multimodal and multilabel in PatentNet are able to offer unparalleled opportunities in the artificial intelligence community and beyond.

Dominik Martin, Simon Heinzel, Johannes Kunze von Bischhoffshausen et al.

Deep learning methods have proven to outperform traditional computer vision methods in various areas of image processing. However, the application of deep learning in industrial surface defect detection systems is challenging due to the insufficient amount of training data, the expensive data generation process, the small size, and the rare occurrence of surface defects. From literature and a polymer products manufacturing use case, we identify design requirements which reflect the aforementioned challenges. Addressing these, we conceptualize design principles and features informed by deep learning research. Finally, we instantiate and evaluate the gained design knowledge in the form of actionable guidelines and strategies based on an industrial surface defect detection use case. This article, therefore, contributes to academia as well as practice by (1) systematically identifying challenges for the industrial application of deep learning-based surface defect detection, (2) strategies to overcome these, and (3) an experimental case study assessing the strategies' applicability and usefulness.

Marian Daun, Jennifer Brings, Marcel Goger et al.

The use of conceptual models to foster requirements engineering has been proposed and evaluated as beneficial for several decades. For instance, goal-oriented requirements engineering or the specification of scenarios are commonly done using conceptual models. Bringing such model-based requirements engineering approaches into industrial practice typically requires industrial training. In this paper, we report lessons learned from a training program for teaching industry professionals model-based requirements engineering. Particularly, we as educators and learners report experiences from designing the training program, conducting the actual training, and applying the instructed material in our day-to-day work. From these findings we provide guidelines for educators designing requirements engineering courses for industry professionals.