Hasil untuk "Industrial electrochemistry"

Yunlong Lu, Xiaohong Huang, Yueyue Dai et al.

The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.

A. Rafiq, M. Ikram, Shamsher Ali et al.

Abstract Textile industry represents a pollution problem worldwide due to the accidental discharge or dumping of polluted wastewater into the waterways, which is having a major influence on the quality of water resources. Around 17–20% of industrial water pollution arises from textile dyeing and treatment according to the World Bank report. This represents a large environmental challenge for textile manufacturers. With growing environmental awareness, there is a need for environmentally-friendly technology to remove dyes from the industrial and local wastewater. The photo decolorization of dyes is considered as a favorable technology for industrial wastewater treatment techniques owing to its environmentally friendly method, low cost, and lack of secondary pollution. The efficiency of photocatalysis system depends on the operational parameters that govern the adsorption and photodegradation of dye molecules. This comprehensive review examines the operational factors influencing the photo decolorization of dye molecules. Our study aims to review and summarize the previously published works and R&D progress in the field of photocatalysis of various water pollutants such as the toxic organic compounds (cationic and anionic dyes) using various semiconductor nanoparticles under visible, solar and UV irradiation. This paper examines the effects of operating parameters on the photocatalytic degradation of textile dyes using various photocatalysts. Our findings showed that various parameters, like initial pH of the solution to be degraded, photocatalyst concentration, reaction temperature, dye concentration and dopants content exert their individual influence on the photocatalytic degradation of any dye. By investigating previous research to elucidate the most significant active species for optimal photo decolorization reactions, this review provides guidelines that can be applied to the development of effective photodegradation systems. The results of our study will help determine the most effective and economical options for removal of dyes in industrial wastewater.

E. Sisinni, Abusayeed Saifullah, Song Han et al.

Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

M. Wollschlaeger, T. Sauter, J. Jasperneite

Guannan Qian, Youtian Zhang, Linsen Li et al.

Abstract Electro-mechanical degradation is commonly observed in various battery electrode materials, which are often prepared as polycrystalline particles consisting of nanoscale primary grains. The anisotropic volume change during lithium extraction/insertion makes these materials intrinsically vulnerable to grain-boundary (inter-granular) fracture that leads to rapid impedance growth and capacity decay. Here, guided by fracture mechanics analysis, we synthesize microsized single-crystal Ni-rich layered-oxide (NMC) cathode materials via an industrially-applicable molten-salt approach. Using single-crystal LiNi0.6Mn0.2Co0.2O2 as a model material, we show that the cycle performance of the Ni-rich NMC can be significantly improved by eliminating the internal grain boundaries and inter-granular fracture. The single-crystal LiNi0.6Mn0.2Co0.2O2 cathodes show high specific capacity (183 ​mAh g−1 ​at 0.1 ​C rate, 4.3–2.8 ​V) and excellent capacity retention (94% after 300 cycles at 1C/1C cycling). Further, it is confirmed for the first time that the single-crystal LiNi0.6Mn0.2Co0.2O2 particles are stable against intra-granular fracture as well under normal operating conditions but do crack if severely overcharged. Electrochemical-shock resistant single-crystal NMC reveals an alternative path towards developing better battery cathode materials, beyond the traditional one built upon polycrystalline NMC.

T. Noël, Yiran Cao, G. Laudadio

Conspectus In the past decade, research into continuous-flow chemistry has gained a lot of traction among researchers in both academia and industry. Especially, microreactors have received a plethora of attention due to the increased mass and heat transfer characteristics, the possibility to increase process safety, and the potential to implement automation protocols and process analytical technology. Taking advantage of these aspects, chemists and chemical engineers have capitalized on expanding the chemical space available to synthetic organic chemists using this technology. Electrochemistry has recently witnessed a renaissance in research interests as it provides chemists unique and tunable synthetic opportunities to carry out redox chemistry using electrons as traceless reagents, thus effectively avoiding the use of hazardous and toxic reductants and oxidants. The popularity of electrochemistry stems also from the potential to harvest sustainable electricity, derived from solar and wind energy. Hence, the electrification of the chemical industry offers an opportunity to locally produce commodity chemicals, effectively reducing inefficiencies with regard to transportation and storage of hazardous chemicals. The combination of flow technology and electrochemistry provides practitioners with great control over the reaction conditions, effectively improving the reproducibility of electrochemistry. However, carrying out electrochemical reactions in flow is more complicated than just pumping the chemicals through a narrow-gap electrolytic cell. Understanding the engineering principles behind the observations can help researchers to exploit the full potential of the technology. Thus, the prime objective of this Account is to provide readers with an overview of the underlying engineering aspects which are associated with continuous-flow electrochemistry. This includes a discussion of relevant mass and heat transport phenomena encountered in electrochemical flow reactors. Next, we discuss the possibility to integrate several reaction steps in a single streamlined process and the potential to carry out challenging multiphase electrochemical transformations in flow. Due to the high control over mass and heat transfer, electrochemical reactions can be carried out with great precision and reproducibility which provide opportunities to enhance and tune the reaction selectivity. Finally, we detail on the scale-up potential of flow electrochemistry and the importance of small interelectrode gaps on pilot and industrial-scale electrochemical processes. Each principle has been illustrated with a relevant organic synthetic example. In general, we have aimed to describe the underlying engineering principles in simple words and with a minimum of equations to attract and engage readers from both a synthetic organic chemistry and a chemical engineering background. Hence, we anticipate that this Account will serve as a useful guide through the fascinating world of flow electrochemistry.

S. Petrovic

Nigar Hashimzade, Haoran Sun

Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation.

Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas et al.

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

Mingchun Sun, Rongqiang Zhao, Zhennan Huang et al.

In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. There is no theoretical research or established estimation for the optimal sample size (OSS) in augmentation, nor is there an established metric to evaluate the accuracy of OSS or its deviation from the ground truth. To address these issues, we propose an information-theoretic optimal sample size estimation (IT-OSE) to provide reliable OSS estimation for industrial data augmentation. An interval coverage and deviation (ICD) score is proposed to evaluate the estimated OSS intuitively. The relationship between OSS and dominant factors is theoretically analyzed and formulated, thereby enhancing the interpretability. Experiments show that, compared to empirical estimation, the IT-OSE increases accuracy in classification tasks across baseline models by an average of 4.38%, and reduces MAPE in regression tasks across baseline models by an average of 18.80%. The improvements in downstream model performance are more stable. ICDdev in the ICD score is also reduced by an average of 49.30%. The determinism of OSS is enhanced. Compared to exhaustive search, the IT-OSE achieves the same OSS while reducing computational and data costs by an average of 83.97% and 93.46%. Furthermore, practicality experiments demonstrate that the IT-OSE exhibits generality across representative sensor-based industrial scenarios.

R. Rachana, M. Aswathy, N. Vinitha et al.

Perovskite CaTiO3 stands out as a strong candidate due to its diverse properties and applicability in vast areas of interest. This study highlights the structural and optical investigations in CaTiO3 perovskite nanoparticles synthesized through non-ceramic routes -Hydrothermal and Sol-gel methods. The structural characteristics were analysed using X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) spectroscopy. XRD patterns confirmed the orthorhombic crystal structure of CaTiO3 in both samples, with particle sizes of 22.6 nm for the hydrothermal method and 17.6 nm for the sol-gel method. Williamson-Hall analysis further supported these findings. FTIR spectra revealed the presence of metal oxide bonds corresponding to CaTiO3, confirming the formation of the material. Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDAX) were carried out to examine the morphology and elemental composition of the samples. Optical properties were evaluated through Diffuse Reflectance Spectroscopy (DRS), with band gaps estimated as 3.67 and 3.83 eV for hydrothermal and sol-gel synthesized samples, respectively. Additionally, the photocatalytic activity of the synthesized nanoparticles was assessed against the methylene blue dye, with degradation kinetics analysed using the Langmuir-Hinshelwood model.

Xiujuan Feng, Falong Qiu

In the past ten years, many coal mines have encountered the problem of a premature failure of anchor rod materials. Through field investigation and laboratory research, it was found that the premature failure of these bolt materials is mostly caused by mine water corrosion. In this paper, a Zn-Y₂O₃-Al₂O₃ composite coating was prepared by an electrodeposition method for the corrosion protection of underground anchors. Through the single-factor experiment method, the co-deposition process of Zn²⁺ nano-Y₂O₃ and nano-Al₂O₃ particles was studied. Microhardness was used as the index to determine the optimum preparation process for the composite coatings. Combined with FSEM and XRD tests, the results showed that the synergistic effect of nano-Y₂O₃ and nano-Al₂O₃ particles made the coating grain refined and reduced the coating defects. The hardness of the coating increased from 98.7 Hv to 347.9 Hv, and the hardness and wear resistance of the coating were improved. The hydrophobicity of the Zn-Y₂O₃-Al₂O₃ composite coating was improved, and its static contact angle was 93.28°. The corrosion resistance of the composite coating was studied through electrochemical impedance spectroscopy, the Tafel curve, corrosion morphology, and weight loss. Under the synergistic effect of nano-Y₂O₃ and nano-Al₂O₃ particles, the self-corrosion current density decreased from 4.21 × 10⁻⁴ A/cm² to 1.06 × 10⁻⁵ A/cm², which confirmed that the Zn-Y₂O₃-Al₂O₃ composite coating had better corrosion resistance and durability. After soaking in mine water for 63 days, the Zn-Y₂O₃-Al₂O₃ composite coating had no obvious shedding on the surface and was well preserved. The practical application results show that it has excellent corrosion resistance and durability. The Zn-Y₂O₃-Al₂O₃ nano-composite coating material has significant potential advantages in the field of corrosion resistance of underground anchor rods.

Kening Lang, Tianyi Liu, Rishi J. Patel et al.

Gas sensors are critical in detecting various gases across industrial, environmental, and healthcare applications. Among them, electrochemical gas sensors stand out due to their high sensitivity, selectivity, and portability. However, traditional electrochemical gas sensors have faced limitations regarding long-term stability and the ability to detect gases at low concentrations. This review paper explores the emerging materials and innovative approaches that promise to address these challenges and enhance sensor performance. The unique properties of novel materials, including metal and metal oxides, carbon materials, conducting polymers, their composites, and others, are discussed in detail. These materials exhibit vital features such as high surface area, enhanced conductivity, and improved gas adsorption capabilities, which are crucial for developing advanced electrochemical gas sensors. Our review emphasizes the critical relationship between material properties and sensing mechanisms, offering insights into optimal material selection and design strategies. In addition to the materials aspect, we also cover many advanced electrochemical techniques, including electrode design enhancements, surface functionalization strategies, and innovative electrolytes like ionic liquids and polymer electrolytes. Overall, this comprehensive overview of state-of-the-art developments in electrochemical gas sensing highlights the potential for transformative applications across diverse fields and emphasizes the importance of interdisciplinary collaboration to drive future innovations.

Juha-Matti Runtti, Usman Virk, Pekka Kyosti et al.

6G radio access architecture is envisioned to contain a network of short-range in-X subnetworks with enhanced capabilities to provide efficient and reliable wireless connectivity. Short-range communications in industrial environments are actively researched at the so-called mid-bands or FR3, e.g., in the EU SNS JU 6G-SHINE project. In this paper, we analyze omni-directional radio channel measurements at 10--12 GHz frequency band to estimate large-scale channel characteristics including power-delay profile, delay spread, K-factor, and pathloss for 254 radio links measured in the Industrial Production Lab at Aalborg University, Denmark. Moreover, we perform a comparison of estimated parameters with those of the 3GPP Indoor Factory channel model.

Aaditya Baranwal, Abdul Mueez, Jason Voelker et al.

Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app

Kessia Nepomuceno, Fabio Petrillo

Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of research solutions for ensuring fairness in software systems remains challenging. Objectives: This paper presents a systematic literature mapping to explore and categorize current advancements in fairness solutions within software engineering, focusing on three key dimensions: research trends, research focus, and viability in industrial contexts. Methods: We develop a classification framework to organize research on software fairness from a fresh perspective, applying it to 95 selected studies and analyzing their potential for industrial adoption. Results: Our findings reveal that software fairness research is expanding, yet it remains heavily focused on methods and algorithms. It primarily focuses on post-processing and group fairness, with less emphasis on early-stage interventions, individual fairness metrics, and understanding bias root causes. Additionally fairness research remains largely academic, with limited industry collaboration and low to medium Technology Readiness Level (TRL), indicating that industrial transferability remains distant. Conclusion: Our results underscore the need to incorporate fairness considerations across all stages of the software development life-cycle and to foster greater collaboration between academia and industry. This analysis provides a comprehensive overview of the field, offering a foundation to guide future research and practical applications of fairness in software systems.

Ruike Lyu, Anna Li, Jianxiao Wang et al.

In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labor deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.

Mariana C. O. Monteiro, M. Koper

Abstract Localized pH measurements are important in various areas of electrochemistry, from corrosion to bio-electrochemistry and electrocatalysis. Different techniques are available to perform these measurements and offer numerous possibilities in terms of spatial and temporal resolution, sensitivity, and precision. In this brief review we present the recent progress made and summarize the main techniques available for localized pH measurements in electrochemistry such as scanning probe techniques (SECM, SICM, SIET), laser (confocal) fluorescence microscopy, rotating ring-disc electrode (RRDE) voltammetry, and infra-red spectroscopy, among others.

O. Wahab, Minkyung Kang, P. Unwin

Abstract Scanning electrochemical cell microscopy (SECCM) is a robust and versatile scanning electrochemical probe microscopy technique that allows direct correlation of structure–activity at the nanoscale. SECCM uses a mobile droplet cell to investigate and visualize electrochemical activity at interfaces with high spatiotemporal resolution, while also providing topographical information. This article highlights diverse contemporary challenges in the field of single entity electrochemistry tackled by the increasing uptake of SECCM globally. Various applications of SECCM in single entity electrochemistry are featured herein, including electrocatalysis, electrodeposition, corrosion science and materials science, with electrode materials spanning particles, polymers, two-dimensional materials and complex polycrystalline substrates. The use of SECCM for patterning structures is also highlighted.

Tijana Mutić, Dalibor Stanković, Dragan Manojlović et al.

In this work, we successfully prepared a modified cobalt oxide (Co₃O₄) carbon paste electrode to detect Levofloxacin (LEV). By synthesizing Co₃O₄ nanoparticles through the chemical coprecipitation method, the electrochemical properties of the electrode and LEV were thoroughly investigated using CV, SWV, and EIS, while material properties were scrutinized using ICP-OES, TEM, SEM, and XRD. The results showed that the prepared electrode displayed a better electrocatalytic response than the bare carbon paste electrode. After optimizing SWV, the electrode exhibited a wide linear working range from 1 to 85 μM at pH 5 of BRBS as the supporting electrolyte. The selectivity of the proposed method was satisfactory, with good repeatability and reproducibility, strongly suggesting a potential application for determining LEV in real samples, particularly in pharmaceutical formulations. The practicality of the approach was demonstrated through good recoveries, and the morphology of the materials was found to be closely related to other parameters, indicating that the developed method can provide a cost-effective, rapid, selective, and sensitive means for LEV monitoring. Overall, this project has made significant progress towards developing a reliable method for detecting LEV and has opened up new opportunities for future research in this field.