Hasil untuk "Industrial electrochemistry"

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Yen-Chia Hsu, Despoina Touska

This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.

Yaohao Zhang, Zhaoju Yu, Wei Li et al.

The growing demand for green energy highlights the need for efficient hydrogen production through water splitting. Designing bimetallic electrocatalysts is crucial for this goal. Transition metal silicides are promising due to their abundance and high electrical conductivity. Here, a novel Ni2Si/FexSiy/SiOC composite was synthesized via a polymer‐derived ceramics (PDCs) route using a single‐source precursor (SSP). The SSP was prepared by modifying a high‐carbon polysiloxane (SPR‐684) with nickel and iron acetylacetonates. Fourier transform infrared spectroscopy (FT‐IR) analysis confirmed the formation of SiOM (M = Fe, Ni) bonds, indicating chemical incorporation of metals. During pyrolysis, in situ formed carbon coated the active sites, enhancing conductivity. The resulting Ni2Si/FexSiy/SiOC catalyst showed a low overpotential of 323 mV versus RHE at 10 mA cm−2disk (0.25 mg cm−2 loading) under alkaline conditions, attributed to the synergy of Fe and Ni in improving the oxygen evolution reaction (OER). X‐ray absorption spectroscopy (XAS) reveals surface reconstruction of NiFe silicide toward a hydroxide during OER. Compared to previous Ni2Si/SiOC systems, the dual‐metal design notably enhanced catalytic performance. This study presents the successful synthesis of NiFe silicide catalysts for OER via the PDCs approach, demonstrating tunability of properties by the nickel to iron ratio, promising potential for water‐splitting and broader electrocatalytic applications.

SoYoung Park, Hyewon Lee, Mingyu Choi et al.

Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse industrial scenarios. This highlights the need for flexible, context-aware prompting strategies. We propose Image-Aware Prompt Anomaly Segmentation (IAP-AS), which enhances anomaly segmentation by generating dynamic, context-aware prompts using an image tagging model and a large language model (LLM). IAP-AS extracts object attributes from images to generate context-aware prompts, improving adaptability and generalization in dynamic and unstructured industrial environments. In our experiments, IAP-AS improves the F1-max metric by up to 10%, demonstrating superior adaptability and generalization. It provides a scalable solution for anomaly segmentation across industries

Hai Dinh-Tuan

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 exposes the limitations of traditional, hierarchical architectures like ISA-95 and RAMI 4.0. Their inherent rigidity, data silos, and lack of support for cloud-native technologies impair the development of scalable and interoperable industrial systems. This paper addresses this issue by introducing CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum. Among other features, CRACI promotes a decoupled and event-driven model to enable flexible, non-hierarchical data flows across the continuum. It embeds cross-cutting concerns as foundational pillars: Trust, Governance & Policy, Observability, and Lifecycle Management, ensuring quality attributes are core to the design. The proposed architecture is validated through a two-fold approach: (1) a comparative theoretical analysis against established standards, operational models, and academic proposals; and (2) a quantitative evaluation based on performance data from previously published real-world smart manufacturing implementations. The results demonstrate that CRACI provides a viable, state-of-the-art architecture that utilizes the compute continuum to overcome the structural limitations of legacy models and enable scalable, modern industrial systems.

Alpana Dubey, Suma Mani Kuriakose, Nitish Bhardwaj

We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios

Mostafa Gaber Refaai, Ahmed M. El-Sherbeeny, Haifa A. Alqhtani et al.

Water quality management remains a critical challenge in arid and semi-arid regions, where limited freshwater resources are increasingly stressed by anthropogenic activities and natural constraints. This study provides a summer 2024 assessment of surface water quality in Egypt’s Fayoum Governorate, emphasizing spatial variability, dominant pollution drivers, and sectoral suitability. Ten sites across agricultural drains and wastewater discharge points were analyzed for 17 physicochemical parameters. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was applied to evaluate drinking, irrigation, industrial, and ecological uses, while spatial patterns were mapped using Inverse Distance Weighting (IDW) in a Geographic Information System. The results reveal critical exceedances in salinity (TDS up to 3,420 mg/L; EC up to 6,840 μS/cm), nutrient enrichment (PO43− up to 10.85 mg/L; NH3–N up to 10.78 mg/L), and turbidity (105 nephelometric turbidity units), mainly from untreated sewage, agricultural return flows, and limited dilution. WQI classification for drinking water showed 30% good, 50% fair, and 20% poor (<45), with S9 and S10 posing high health risks due to cumulative sewage, industrial discharges, and intensive farming runoff. For irrigation, 60% of sites were rated good, though elevated sodium, magnesium hazard, and potential salinity indicate risks of soil degradation. Industrial assessments revealed scaling (LI >0) and corrosion (RSI >8.5) in more than half the samples. Ecologically, 50% of sites recorded poor WQI (<45), reflecting eutrophication, organic load, and elevated temperatures. CCME-WQI/GIS mapping identified S9 and S10 as hotspots, concentrated near Lake Qarun where pollutant accumulation is intensified by weak hydrological flushing. The contrasting signatures of nutrient-enriched agricultural drains and salinity-dominated industrial reaches underscore the need for targeted interventions. Strengthening wastewater treatment, optimizing fertilizer use, enforcing standards, and enhancing public awareness are recommended. The integrated CCME-WQI/GIS framework offers a replicable tool for sustainable water management in arid, agriculture-dependent regions and supports progress toward Sustainable Development Goal 6.

Kah Chun Lau, Xiangbo Meng

While we are pursuing a fully electrified society, high-energy rechargeable batteries are undergoing intensive investigation. In this respect, atomic and molecular layer deposition (ALD and MLD) have been drawing increasing interest, due to their unmatched capabilities to precisely modify electrodes’ surfaces for better electrochemical performance. In this work, we reviewed the recent studies using ALD/MLD for interface engineering of several important electrode materials, including nickel (Ni)-rich metal oxide cathodes, silicon (Si), and lithium (Li) anodes in lithium-ion and lithium metal batteries. We particularly discussed the most promising coatings from these studies and explored the underlying mechanisms based on experiments and modeling. We anticipate that this work will inspire more studies using ALD/MLD as an important technique for securing new solutions for batteries.

Ali Raza Khan, Bhawana Kumari, Jan Wegner et al.

Electrocatalytic selectivity is generally explained in terms of atomic‐scale properties, i.e., active sites, overlooking the impact of macroscopic electrode geometry and structure, which affect the macroscopic mass transport. This study demonstrates how the geometry of additively manufactured (AM) nickel electrodes fabricated via laser powder bed fusion influences reaction selectivity and the conversion rate of the glycerol oxidation reaction. All six AM electrodes with different geometries exhibit formic acid selectivity above 80%, with the large grid electrode achieving 95%. The large grid has deeper cavities and confined structures that promote enhanced oxidation due to restricted diffusion of C2 and C3 intermediates toward the bulk of the solution. The highest glycerol conversion of 28.2% is achieved with a 99% carbon balance, confirming efficient mass utilization. While achieving 100% formic acid yield remains challenging, minor byproducts are limited to ≤5%. These results emphasize that electrode geometry can be strategically tailored to optimize selectivity and enhance conversion efficiency. The significance of structural effects in electrocatalytic reactions is highlighted, providing novel insights into electrode design.

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh et al.

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field.

Xuejiao Li, Cheng Yang, Charles Møller et al.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

Maryam Ahang, Todd Charter, Mostafa Abbasi et al.

Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.

Xiaomeng Zhu, Talha Bilal, Pär Mårtensson et al.

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset and code are publicly available.

Jing Xie, Yang Qiao, Zu'an Wang et al.

Pure waterjet surface treatment without abrasive particles has a promising application in the biomedical field, because it induces compressive residual stresses on a metal surface and never leaves the tiny hard particles. In this work, the influence of operation pressure, standoff distance and the number of paths of the waterjet on the surface topography as well as the hardness was studied using the Taguchi method. The results showed that the most essential parameter is the operation pressure. By tuning the operation pressure from 100 to 300 MPa, the surface of Ti6Al4V specimens can be smoothed, roughened or damaged; when the surface layer is eroded, the new-born surface exhibits a clear stochastic nature accompanied by microvoids. The standoff distance benefits finer controlling the height parameters, whilst the number of paths affects the waviness. The hardening effect generated by the waterjet impingement extends to a few hundred-micron depth of the specimens, and the peak value of microhardness was found at a depth of 70 μm, which is an increase by greater than 20 %. The roughness parameters of Arithmetical mean height (Sa), Skewness (Ssk), Auto-correlation length (Sal), and Developed interfacial area ratio (Sdr) as a set are recommended to characterize the biomaterial's surface. The present research results promote the application of waterjet treatment in the field of fine-tuning biomaterial surface morphology.

Ozan Öztürk, Prof. Dr. Emre Gür

Abstract Supercapacitor (SC) devices holds an important position between traditional capacitors and ion batteries in terms of energy density and power density values. In particular, SC′s greater power density values than Li‐ion batteries make them useful for some specific applications, such as storing energy in hybrid cars while the car slows down. Increasing energy density values is one of the key challenges for the SC community. Transition metal dichalcogenides (TMDCs), are one of the new developing important material systems to have this potential, compared to their counterparts’ transition metal oxides and conductive polymers. This review is about giving insight into the electrochemical performances of two‐dimensional (2D) layered transition metal sulfides (TMS) such as MoS2, WS2, TaS2, NbS2, VS2, TiS2 and ZrS2 materials. On the other hand, the methods mostly used in synthesizing these materials are presented.

B. Prabadevi, N. Deepa, Nancy Victor et al.

With the advent of new technologies and endeavors for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. Furthermore, we are in the era of the fifth industrial revolution, where machines and humans collaborate to maximize productivity with the effective utilization of human intelligence and other resources. Hence, Industry 5.0 in the metaverse may have tremendous technological integration for a more immersive experience and enhanced communication.These technological amalgamations are suitable for the present environment and entirely different from the previous perception of virtual technologies. This work presents a comprehensive review of the applications of the metaverse in Industry 5.0 (so-called industrial metaverse). In particular, we first provide a preliminary to the metaverse and industry 5.0 and discuss key enabling technologies of the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twin, blockchain, and 6G communication networks. This work then explores diverse metaverse applications in Industry 5.0 vertical domains like Society 5.0, agriculture, supply chain management, healthcare, education, and transportation. A number of research projects are presented to showcase the conceptualization and implementation of the industrial metaverse. Furthermore, various challenges in realizing the industrial metaverse, feasible solutions, and future directions for further research have been presented.

Ben-Hur Francisco Cardoso, Eva Yamila da Silva Catela, Guilherme Viegas et al.

Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately depict Brazil's knowledge base and growth opportunities. Instead, promoting the sophistication of the heterogeneous regional industrial structures and development spillovers is a key to growth.

Yusuke Kato, Ryo Okumura, Tadahiro Taniguchi

The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked with placing an object at a specific location with high accuracy, it is important not only to have information about the location of the object to be placed, but also the posture of the object grasped by the robotic hand. Often, industrial box-packing requires the sequential placement of identically shaped objects into a single box. The robot's action should be determined by the same learned model. In factories, new kinds of products often appear and there is a need for a model that can easily adapt to them. Therefore, it should be easy to collect data to train the model. In this study, we designed a robotic system to automate real-world industrial tasks, employing a vision-based learning control model. We propose in-hand-view-sensitive Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to obtain in-hand postures of objects. We demonstrate that our model, trained for a single object-placement task, can handle sequential tasks without additional training. To evaluate efficacy of the proposed model, we employed a real robot to perform sequential industrial box-packing of multiple objects. Results showed that the proposed model achieved a 100% success rate in industrial box-packing tasks, thereby outperforming the state-of-the-art and conventional approaches, underscoring its superior effectiveness and potential in industrial tasks.

Preeti Kumari, Aditya Kumar

Pollutants are big threat for the environment. Their imperil nature have disturbed the balance in the ecosystem and have also endangered the existence of the life on the earth. Advanced oxidation process is one of the emerging approach for the treatment of recalcitrant pollutants in nature. The process mainly involves the generation of a strong oxidant, which can easily degrade the pollutants produced from different sources. This review manifests the need of advanced oxidation process, and gives an outline of the different types of advanced oxidation process. It discusses the mechanism, advantages and disadvantages of the various forms of advanced oxidation process. Parameters for a sustainable technology such as technical and economical feasibility of the process, toxicity level and degradation effectiveness of the medium have also been discussed in detail to profess the sustainability of the process. It has potential and efficacy in removal of organic, toxic and non-biodegradable pollutants with minimum harmful effects. The future perspectives provide the room for modification and development and motivates us to overcome the present challenges and achieve better outcomes in future.

Giulia Gemme, Gian Marcello Andolina, Francesco Maria Dimitri Pellegrino et al.

We investigate a Dicke quantum battery in the dispersive regime, where the photons trapped in a resonant cavity are much more energetic with respect to the two-level systems embedded into it. Under such off-resonant conditions, even an empty cavity can lead to the charging of the quantum battery through a proper modulation of the matter–radiation coupling. This counterintuitive behaviour has its roots in the effective interaction between two-level systems mediated by virtual photons emerging from the fluctuations of the quantum electromagnetic field. In order to properly characterize it, we address relevant figures of merit such as the stored energy, the time required to reach the maximum charging, and the averaged charging power. Moreover, the possibility of efficiently extracting energy in various ranges of parameters is discussed. The scaling of stored energy and power as a function of the number <i>N</i> of two-level systems and for different values of the matter–radiation coupling is also discussed, showing, in the strong coupling regime, performances in line with what is reported for the Dicke quantum battery in the resonant regime.