Hasil untuk "Industrial electrochemistry"

Adam J. Greer, J. Jacquemin, C. Hardacre

Since their conception, ionic liquids (ILs) have been investigated for an extensive range of applications including in solvent chemistry, catalysis, and electrochemistry. This is due to their designation as designer solvents, whereby the physiochemical properties of an IL can be tuned for specific applications. This has led to significant research activity both by academia and industry from the 1990s, accelerating research in many fields and leading to the filing of numerous patents. However, while ILs have received great interest in the patent literature, only a limited number of processes are known to have been commercialised. This review aims to provide a perspective on the successful commercialisation of IL-based processes, to date, and the advantages and disadvantages associated with the use of ILs in industry.

N. I. Nayem, S. Ahmed, Md. A. Rashed et al.

ABSTRACT Dopamine (DA) plays a vital role as a neurotransmitter in the central nervous system (CNS), and its accurate quantification is essential for diagnosing neurological disorders. However, selective and sensitive detection of DA in complex biological matrices remains a challenge due to interference from coexisting biomolecules. In this study, a platinum nanoparticle‐decorated carbon nanotubes/polypyrrole‐carbon (Pt@CNTs/PPy‐C) nanocomposite was synthesized via a facile two‐step process involving ultrasonication and photo‐reduction, eliminating the need for stabilizers or dispersants. Structural and morphological analysis confirmed the uniform distribution of Pt nanoparticles within the CNTs/PPy‐C matrix, enhancing electrocatalytic activity. Electrochemical kinetic studies revealed that DA electro‐oxidation on the nanocomposite‐modified glassy carbon electrode (GCE) follows adsorption‐controlled kinetics, with a transfer coefficient (α) of 0.51 and a heterogeneous rate constant of 8.37 s−1. Differential pulse voltammetry (DPV) demonstrated a high sensitivity of 3.45 µA µM−1 cm−2 over a linear range of 2.0–24.0 µM with a detection limit of 0.034 µM. The sensor exhibited outstanding selectivity for DA in the presence of various interfering species, along with excellent reproducibility, repeatability and stability. Additionally, the sensor demonstrated high accuracy and reliability in detecting DA in a commercial pharmaceutical formulation, with recovery rates ranging from 96.72% to 101.40%. These findings highlight the potential of the Pt@CNTs/PPy‐C nanocomposite as a promising electrocatalyst for DA detection, contributing to the development of highly efficient electrochemical sensors for biomedical and pharmaceutical applications.

Dawei Xi, Yuheng Wu, Yuli Li et al.

Yuchen Lei, Fuhai Zhang, Wenbin Zhang et al.

Summary Biomass is the only renewable organic carbon resource in nature, and utilization of biomass is important for carbon neutrality. Currently, depolymerizing biomass macromolecules into small organic monomers via thermocatalytic pyrolysis is a well-established technique. Further valorization of these biomass small molecules to value-added products has attracted increasing attention, especially via electrochemistry coupling green electricity. Electrocatalytic hydrogenation (ECH) directly uses hydrogen from water and operates under mild conditions (e.g., ambient temperature and pressure), which plays an important role for upgrading biomass small molecules and avoids substantial CO2 emission. In this review, we will provide a summary of recent achievements in ECH of biomass small molecules, with a review focus on the research about pushing ECH toward industrial-scale productivities. We will also discuss the existing problems and challenges in this field and propose an outlook for the future developments.

N. Latif, T. Ahmed

Industrial wastewater coming from numerous industries is the major source of water pollution. Wastewater contains heavy metals, organic and inorganic contaminants, and insoluble materials. This contaminant poses a serious hazard to the environment. Removal of these pollutants requires innovative and novel methods and technologies. The treatment of wastewater can be done in a variety of ways. There are several techniques available, including adsorption, coagulation, and activated sludge. Water treatment also uses inexpensive adsorbents. A number of Nano-materials have been considered for use as potential pollutant removal candidates in recent years. For elimination of heavy metal, conventional methods include chemical precipitation, ion exchange, adsorption, the ion floatation, electrochemistry as well as coagulation/flocculation. Despite these advantages, however, these methods have some serious disadvantages. In addition to photo catalysis, electro dialysis, and membrane separation techniques, new adsorbents have been introduced to improve adsorption. Fenton processes for treating real industrial wastewater are critically reviewed in this article. To reduce contaminants in wastewater, this paper discusses possible wastewater treatment techniques.

Sheng Yang, Panpan Zhang, Ali Shaygan Nia et al.

2D materials are important building blocks for the upcoming generation of nanostructured electronics and multifunctional devices due to their distinct chemical and physical characteristics. To this end, large‐scale production of 2D materials with high purity or with specific functionalities represents a key to advancing fundamental studies as well as industrial applications. Among the state‐of‐the‐art synthetic protocols, electrochemical exfoliation of layered materials is a very promising approach that offers high yield, great efficiency, low cost, simple instrumentation, and excellent up‐scalability. Remarkably, playing with electrochemical parameters not only enables tunable material properties but also increases the material diversities from graphene to a wide spectrum of 2D semiconductors. Here, a succinct and critical survey of the recent progress in this research direction is presented, comprising the strategic design, exfoliation principles, underlying mechanisms, processing techniques, and potential applications of 2D materials. At the end of the discussion, the emerging trends, challenges, and opportunities in real practice are also highlighted.

Tetsuro Morooka, Tamao Shishido, Takuya Nakanishi et al.

Ethan Williams, David Burnett, Peter Slater et al.

Nickel‐rich layered oxide cathodes, such as LiNi0.9Mn0.05Co0.05O2 (NMC 90‐5‐5) exhibit high energy densities but face challenges related to capacity fade and structural instability that arise during the material synthesis procedure. This study explores their synthesis shorter firing times and lower firing temperatures via large‐scale hydroxide precipitation and boron‐based fluxes during sintering. After optimisation, critical insight into the effect of the fluxes, on the structural and electrochemical properties of NMC 90‐5‐5 are investigated. Boric acid reduced bulk cation mixing and surface lithium residues, improving initial capacity (200 mAh g−1), rate capability, and charge transfer resistance. However, it led to significant capacity fade due to the irreversible phase transition at 4.1 V vs Li/Li+ (25% after 200 cycles). Conversely, borax yielded slightly lower initial capacity (185 mAh g−1), however, it exhibited superior cycle life performance, (75% capacity retention after 200 cycles). Boron and sodium were shown to doped further into the cathode particles, delaying the on‐set of the high voltage phase transition. This study highlights the role of boron‐based fluxes in tailoring the performance and cycle life of Ni‐rich NMC cathodes produced under lower energy synthesis conditions. This results in a significant reduction of energy and cost at scale.

Zhe Lv, Zhonghao Sun, Lei Wang et al.

With the accelerating global transition toward sustainable energy, the role of battery energy storage systems (ESSs) becomes increasingly prominent. This study employs the isothermal battery calorimetry (IBC) measurement method and computational fluid dynamics (CFD) simulation to develop a multi-domain thermal modeling framework for battery systems, spanning from individual cells to modules, clusters, and ultimately the container level. Experimental validation confirms the model’s accuracy, with the simulated maximum cell temperature of 36.2 °C showing only a 1.8 °C deviation from the measured value of 34.4 °C under real-world operating conditions. Furthermore, by integrating on-site calibrated thermodynamic parameters of the container, a battery system energy efficiency model is established. Combined with the battery aging engineering model, a coupled lifetime–energy efficiency model is constructed. Six different control strategies are simulated and analyzed to quantify the system’s comprehensive lifecycle benefits. The results demonstrate that the optimized control strategy enhances the overall energy storage station revenue by 2.63%, yielding an additional cumulative profit of CNY 13.676 million over the entire lifecycle. This research provides an effective simulation framework and decision-making basis for the thermal management optimization and economic evaluation of battery ESSs.

Christopher J. Miller, Byunghoon Lee, Jacob A. Barrett et al.

Recent reports show [FeFe] hydrogenase mimics are active for the electrochemical reduction of CO2 to formate (HCOO−). Herein, the electrochemical reduction of CO2 with the [FeFe] hydrogenase mimic [Fe2(μ‐pdt)(CO)6, 1, where pdt = propane‐1,3‐dithiolate] in acetonitrile is reported. In the presence of the weak acid, methanol (MeOH), 1 reduces CO2 to both CO (Faradaic Efficiency maximum [FEmax] of 16 ± 6%) and HCOO− (FEmax = 20%) and produces H2 (FEmax = 56 ± 4%). Without added MeOH, 1 reacts with adventitious water to form H2 (FEmax = 85 ± 1%), HCOO− (FEmax = 7.8%), and CO (FEmax = 7 ± 3%) with CO32− being detected by infrared spectroscopy.  Product formation is potential dependent: more negative potentials increases selectivity for HCOO− over CO. The first reduction of 1 forms a pdt‐bridged dimer, 2. However, the reduction of 2 at the potentials required for electrochemical CO2 reduction leads to two new species. Using density functional theory, and infrared spectroelectrochemistry (IR‐SEC), these structures are identified to be [Fe(CO)4]2− (3) and a trinuclear Fe3 species (4). While these species can reduce CO2 to CO and HCOO−, the predominant formation of H2 reveals kinetic issues in  CO2 reduction. The work offers to consider alternate competing mechanistic pathways and explains the lack of product selectivity when using hydrogen evolution reaction catalyst for CO2 reduction to HCOO−.

Mohamed Jamal Saadh, Ammar Kadhim Wabdan, Mahmood Jasem Jawad et al.

A representative zinc (Zn)-doped nanocone (ZCON) scaffold was fabricated as a potential carrier for the drug delivery of propylthiouracil (PTU) along with density functional theory (DFT) calculations. Interactions of PTU and ZCON substances yielded formations of four bimolecular PTU@ZCON complexes indicated by P1, P2, P3, and P4. S⋯Zn and O⋯Zn interactions were dominant for obtaining the stabilized complexes. Additionally, the models were recognized based on the nature of interactions, in which the role of H⋯C interaction was also important for the formation of complexes. P1 was found as the strongest complex among the models regarding the existence of S⋯Zn and H⋯C interactions. The electronic specifications indicated the distinguishability of complexes from each other and also from the singular state. Additionally, the significant role of ZCON was especially found in P1 by adsorbing the whole molecular orbital patterns to the ZCON scaffold showing a managing role for the whole system. As a result, both of structural and electronic specifications demonstrated benefits of ZCON scaffold for working as a potential carrier in the drug delivery of PTU. Implementing the Zn-doped region in the nanocone was also found as an advantage of such a fabricated scaffold for approaching a desirable result.

Tom Maus, Nico Zengeler, Tobias Glasmachers

We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.

Jinwei Hu, Zezhi Tang, Xin Jin et al.

This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.

Dan Parii, Evelyne Janssen, Guangzhi Tang et al.

Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.

Mgcini Keith Phuthi, Pin-Wen Guan, Russell J. Hemley et al.

Metal hydrides can be tuned to have a diverse range of properties and find applications in hydrogen storage and superconductivity. Finding methods to control the synthesis of hydrides can open up new pathways to unlock novel hydride compounds with desired properties. We introduced the idea of utilizing electrochemistry as an additional tuning knob and in this work, we study the synthesis of binary metal hydrides using high pressure, electrochemistry and combined pressure-electrochemistry. Using density functional theory calculations, we predict the phase diagrams of selected transition metal hydrides under combined pressure and electrochemical conditions and demonstrate that the approach agrees well with experimental observations for most phases. We use the phase diagrams to determine trends in the stability of binary metal hydrides of scandium, yttrium and lanthanum as well as discuss the hydrogen-metal charge transfer at different pressures. Furthermore, we predict a diverse range of vanadium and chromium hydrides that could potentially be synthesized using pressure electrochemistry. These predictions highlight the value of exploring pressure-electrochemistry as a pathway to novel hydride synthesis.

Tong Nie, Jian Sun, Wei Ma

Networked urban systems facilitate the flow of people, resources, and services, and are essential for economic and social interactions. These systems often involve complex processes with unknown governing rules, observed by sensor-based time series. To aid decision-making in industrial and engineering contexts, data-driven predictive models are used to forecast spatiotemporal dynamics of urban systems. Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency due to computational demands. Hence, their applications in large-scale networks still require further efforts. This paper addresses this trade-off challenge by drawing inspiration from physical laws to inform essential model designs that align with fundamental principles and avoid architectural redundancy. By understanding both micro- and macro-processes, we present a principled interpretable neural diffusion scheme based on Transformer-like structures whose attention layers are induced by low-dimensional embeddings. The proposed scalable spatiotemporal Transformer (ScaleSTF), with linear complexity, is validated on large-scale urban systems including traffic flow, solar power, and smart meters, showing state-of-the-art performance and remarkable scalability. Our results constitute a fresh perspective on the dynamics prediction in large-scale urban networks.

Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

Soham Das, Soumya Kanti Biswas, Abhishek Kundu et al.

In this experimental investigation, a Physical Vapor Deposition (PVD) process was employed to deposit TiAlN coating onto a Si substrate. The nitrogen flow rate, bias voltage, and substrate-to-target distance were selected as input parameters, each with three different levels. The design of these input parameters was structured according to Taguchi's L9 Orthogonal Array (OA). Following deposition, the mechanical, microstructural, structural, and electrochemical properties of the TiAlN coating were meticulously characterized and analyzed to discern the influence of the selected parameters on its various properties. Microstructural analysis revealed a homogeneous structure throughout the film. Additionally, the mechanical properties of the film exhibited notable performance under the specified parameters. However, it was observed that no consistent trend could be identified across different properties concerning the applied parameters. To elucidate the complex relationships among these variables, the Least Squares Method (LSM) regression analysis technique was employed. This analytical approach facilitated the establishment of correlations among the diverse parameters, enhancing the understanding of their collective impact on the TiAlN coating properties. The understanding of analytical results will be useful for predicting the values between the two extremities to measure the performance parameters where the experimental results are not available.

M. Ramaprakash, A. Jerom Samraj, M.G. Neelavannan et al.

Ni–Mo–W ternary alloy coatings were electrodeposited on mild steel substrate using citrate electrolyte for hardness and corrosion resistance applications. The influences electrolyte pH, electrolyte temperature and sodium molybdate concentrations on deposit qualities were studied. The sodium molybdate concentrations were varied from 0.025 to 0.1 M for obtaining alloy coatings with various molybdenum (Mo) compositions. The deposition condition such as electrolyte concentration, pH and temperature were optimized for obtaining good aesthetic (bright) appearance, surface stability and microhardness properties. The maximum Mo composition (47.71 wt %) in alloy deposit was achieved is 47.71 wt % using 0.1 M sodium molybdate concentration. The microhardness properties were increased from 850 HV to 946 HV by introducing molybdenum (47.71 wt %) as third alloying element in to Ni–W alloy matrix. The obtained mechanical and corrosion resistance properties of these ternary alloy coatings were compared with binary alloy and metallic coatings viz., Ni–W and Ni–Mo and metallic Ni coatings. The superior corrosion rate was observed for Ni–Mo–W alloy deposits (5.8 mpy) compared to Ni–W (9.04 mpy) and Ni–Mo (8.89 mpy) alloy.

Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa et al.

Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.