Research into alternative renewable energy generation is a priority, due to the ever-increasing concern of climate change. Microbial fuel cells (MFCs) are one potential avenue to be explored, as a partial solution towards combating the over-reliance on fossil fuel based electricity. Limitations have slowed the advancement of MFC development, including low power generation, expensive electrode materials and the inability to scale up MFCs to industrially relevant capacities. However, utilisation of new advanced electrode-materials (i.e. 2D nanomaterials), has promise to advance the field of electromicrobiology. New electrode materials coupled with a more thorough understanding of the mechanisms in which electrogenic bacteria partake in electron transfer could dramatically increase power outputs, potentially reaching the upper extremities of theoretical limits. Continued research into both the electrochemistry and microbiology is of paramount importance in order to achieve industrial-scale development of MFCs. This review gives an overview of the current field and knowledge in regards to MFCs and discusses the known mechanisms underpinning MFC technology, which allows bacteria to facilitate in electron transfer processes. This review focusses specifically on enhancing the performance of MFCs, with the key intrinsic factor currently limiting power output from MFCs being the rate of electron transfer to/from the anode; the use of advanced carbon-based materials as electrode surfaces is discussed.
Scandium oxide (Sc2O3) is considered as omnipotent “Industrial Ajinomoto” and holds promise in catalytic applications. However, rarely little attention is paid to its electrochemistry. Here, the first nanocasting design of high‐surface area Sc2O3 with abundant oxygen vacancies (mesoporous VO‐Sc2O3) for efficient electrochemical biomass valorization is reported. In the case of the electro‐oxidation of 5‐hydroxymethylfurfural (HMF) to 2,5‐furandicarboxylic acid (FDCA), quantitative HMF conversion, high yield, and high faradic efficiency of FDCA via the hydroxymethylfurancarboxylic acid pathway are achieved by this advanced electrocatalyst. The beneficial effect of the VO on the electrocatalytic performance of the mesoporous VO‐Sc2O3 is revealed by the enhanced adsorption of reactants and the reduced energy barrier in the electrochemical process. The concerted design, in situ and ex situ experimental studies and theoretical calculations shown in this work should shed light on the rational elaboration of advanced electrocatalysts, and contribute to the establishment of a circular carbon economy since the bio‐plastic monomer and green hydrogen are efficiently synthesized.
This study demonstrates a significant enhancement in reversible metal electrodeposition devices (RMEDs) through a systematic activation process using a silver‑copper electrolyte system. An electrode conditioning protocol employing a wider voltage range cyclic voltammetry (−3.5 V to 1.3 V) was developed to enable robust four-state optical switching beyond traditional transparent-mirror operation. The activation process resulted in a 63.6% increase in electrochemical activity and improved coloration efficiency, increasing from 43.2 to 54.7 cm2/C. Using an optimized step-voltage method, vivid red and blue color states were achieved, with transmittance modulation increasing from 26.2% to 58.9% for red color and from 56.9% to 63.0% for blue color after activation. The four-state device demonstrated excellent long-term stability, maintaining consistent optical performance over 6000 s of continuous cycling without degradation. This work establishes electrode activation as a key advancement for practical smart window applications, offering both aesthetic versatility through multicolor options and operational reliability for commercial use.
Jonas Weidner, Christian N. Tchassem, Debanjan Das
et al.
Abstract Electrochemical CO2 conversion is an important strategy to produce high‐value carbon‐containing molecules, such as ethylene and ethanol. Despite huge progress in recent years concerning CO2 reduction catalyst development with increased selectivity, high selectivity for C2+ products at high current densities is still a challenge. We report the development and optimization of a new surface Al‐rich Cu/CuOx catalyst with high selectivity for C2+‐products at high current densities of up to −800 mA cm−2. We integrated the corresponding catalyst‐modified gas‐diffusion electrode into a second flow‐through electrolyzer, which was connected to a first flow‐through electrolyzer comprising a highly CO‐selective Ni−Cu dual‐atom N‐doped carbon catalyst. The enrichment of the CO2 stream with CO generated at a current density of −400 mA cm−2 in the first electrolyzer increased the production rate of ethanol formation at the Al‐rich Cu/CuOx catalyst at a current density of −300 mA cm−2 by 28 %, while maintaining the production rate of ethylene. Thereby, the overall yield of C2+‐products obtained by CO2 reduction was significantly increased.
Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.
The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.
I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.
Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.
Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Grace Ma
Advanced machine learning algorithms are increasingly utilized to provide data-based prediction and decision-making support in Industry 4.0. However, the prediction accuracy achieved by the existing models is insufficient to warrant practical implementation in real-world applications. This is because not all features present in real-world datasets possess a direct relevance to the predictive analysis being conducted. Consequently, the careful incorporation of select features has the potential to yield a substantial positive impact on the outcome. To address the research gap, this paper proposes a novel hybrid framework that combines the feature importance detector - local interpretable model-agnostic explanations (LIME) and the feature interaction detector - neural interaction detection (NID), to improve prediction accuracy. By applying the proposed framework, unnecessary features can be eliminated, and interactions are encoded to generate a more conducive dataset for predictive purposes. Subsequently, the proposed model is deployed to refine the prediction of electricity consumption in foundry processing. The experimental outcomes reveal an augmentation of up to 9.56% in the R2 score, and a diminution of up to 24.05% in the root mean square error.
Stoica Dorel, Mohammed Gmal Osman, Cristian-Valentin Strejoiu
et al.
This paper presents a comparative analysis of different battery charging strategies for off-grid solar PV systems. The strategies evaluated include constant voltage charging, constant current charging, PWM charging, and hybrid charging. The performance of each strategy is evaluated based on factors such as battery capacity, cycle life, DOD, and charging efficiency, as well as the impact of environmental conditions such as temperature and sunlight. The results show that each charging strategy has its advantages and limitations, and the optimal approach will depend on the specific requirements and limitations of the off-grid solar PV system. This study provides valuable insights into the performance and effectiveness of different battery charging strategies, which can be used to inform the design and operation of off-grid solar PV systems. This paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. The main needs for off-grid solar photovoltaic systems include efficient energy storage, reliable battery charging strategies, environmental adaptability, cost-effectiveness, and user-friendly operation, while the primary limitations affecting these systems encompass intermittent energy supply, battery degradation, environmental variability, initial investment costs, fluctuations in energy demand, and maintenance challenges, emphasizing the importance of careful strategy selection and system design to address these factors. It also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Pedro Luis Camuñas García-Miguel, Jaime Alonso-Martinez, Santiago Arnaltes Gómez
et al.
Participation in the electricity market requires making commitments without knowing the real generation or electricity prices. This is problematic for renewable generators due to their fluctuating output. Battery energy storage systems (BESSs) integrated with renewable sources in a hybrid farm (HF) can alleviate imbalances and increase power system flexibility. However, the impact of battery degradation on long-term profitability must be taken into account when choosing the correct market participation strategy. This study evaluates the state-of-the-art on energy management systems (EMS) for HFs participating in day-ahead and intraday markets, incorporating both BESSs’ calendar and cycling degradation. Results suggest that efforts to attain additional profits in intraday markets can be detrimental, especially when the degradation effect is considered in the analysis. A new market participation strategy is proposed that aims to address the limitations of market overlapping and forecasting errors. The results demonstrate that the proposed method can enhance long-term benefits while also reducing battery degradation.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. In this study, we present a machine-learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past five years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can automatically capture critical features in the cycling curves of DRX cathodes under various conditions. Illustratively, the model gives rational predictions of the discharge capacity for diverse compositions in the Li--Mn--O--F chemical space as well as for high-entropy systems. As a universal model trained on diverse chemistries, our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.
Nicolas G. Hörmann, Simeon D. Beinlich, Karsten Reuter
Using the example of a proton adsorption process, we analyze and compare two prominent modelling approaches in computational electrochemistry at metallic electrodes - electronically canonical, constant-charge and electronically grand-canonical, constant-potential calculations. We first confirm that both methodologies yield consistent results for the differential free energy change in the infinite cell size limit. This validation emphasizes that, fundamentally, both methods are equally valid and precise. In practice, the grand-canonical, constant-potential approach shows superior interpretability and size convergence as it aligns closer to experimental ensembles and exhibits smaller finite-size effects. On the other hand, constant-charge calculations exhibit greater resilience against discrepancies, such as deviations in interfacial capacitance and absolute potential alignment, as their results inherently only depend on the surface charge, and not on the modelled charge vs. potential relation. The present analysis thus offers valuable insights and guidance for selecting the most appropriate ensemble when addressing diverse electrochemical challenges.
The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.