Lowering the operating temperature of proton-conducting ceramic fuel cells (PCFCs) to the technologically critical 400-550 degrees C regime requires a fundamental rethinking of electrolyte design beyond conventional bulkdoping strategies. Here, we introduce surface doping of Ceria with low-content of Co-Al strategy as a distinct materials paradigm, in which ion-transport functionality is preferentially enhanced within a chemically modified near-surface region while preserving the bulk fluorite structure. It is found that the low-level doping of CeO2 with Co, Al, and Co-Al (5%) substantially improves ions transport in electrolyte, as verified through multiscale characterizations, cell performances evaluations, and proton conduction analysis. In Particular, Co-Al (5%) doped-CeO2 ((CeO2|5(Co-Al)s)) simultaneously promotes oxygen-vacancy formation and stabilizes lattice distortion, resulting in comparatively superior cell power densities of 1093 mW cm- 2 at 520 degrees C and 296 mW cm- 2 at 370 degrees C, along with reduced polarization losses and enhanced transport kinetics, as revealed by impedance spectroscopy and distribution relaxation time (DRT) analyses. Further investigation confirms the proton-ions conduction and fuel-cell stability operation of the CeO2|5(Co-Al)s electrolyte-based cell. Moreover, the CeO2|5(Co-Al)s electrolyte achieves a current density of 0.962 A cm- 2 at 1.4 V in electrolysis mode, which preliminary demonstrates the reversibility and potential applicability of the developed electrolyte under electrolysis conditions. These results establish that low-content of surface doping can be employed as a generalizable strategy for electrolyte in low-temperature SOFCs and reversible PCFCs.
Globally, wind is one of the fastest growing renewable energy sources, requiring innovative computational methods across the spectrum of wind energy engineering tasks to boost wind energy production. Recent advancements in generative artificial intelligence (AI) models have led to the integration of the models into wind energy engineering to develop solutions. Previous surveys primarily focused on the general applications of AI in wind energy. The objectives of this survey are to: (i) review modified generative AI models in wind energy engineering tasks, (ii) develop taxonomy linking model variants to tasks, (iii) develop performance evaluation metrics taxonomy, (iv) analyze the core concepts and limitations of the modified generative AI models, (v) examine data sources, (vi) present real-world case studies, (vii) identify emerging trends and challenges. This is the first comprehensive survey exclusively for modified generative AI models across different aspects of wind energy engineering. The survey examines the modifications of generative AI models for wind energy applications, explaining the core idea behind each modification, its suitability for specific task and the limitations identified in the corresponding model. New taxonomies were introduce to support synthesis and analysis. The survey extends beyond theoretical discussion by highlighting real-world case studies where generative AI models have been deployed in real-world commercial wind farms. Additionally, emerging open challenges are identified and future research directions are proposed from new perspectives. This survey provide a fundamental reference for early career researchers, a guide to industry practitioners and a benchmark for innovations for expert researchers.
The recurrent outbreak of viral pathogens and the possibility of the new pandemics demand the transition to the predictive and integrative computational frameworks instead of reactive one. This review describes computational antiviralism as an integrated approach that uses artificial intelligence (AI), virtual screening, and molecular design tools to identify antiviral targets at the molecular level. We compare deep learning-based structure models with physics-based molecular dynamics (MD) with network pharmacology to describe virus-host interactome dynamics. We also evaluate systems virology strategies that combine the transcriptomic, proteomic, and metabolomic data in order to solve infection-induced cellular reprogramming. The framework is not confined to the molecular, but includes evolutionary phylogenomics, epidemiological modelling of the zoonotic spillover, and climate-guided forecasting of cross-species transmission of viruses. We consider such key issues as assay heterogeneity, interpretability of models, and management of autonomous laboratory systems. Importantly, we explicitly acknowledge that no true end-to-end validated multi-scale antiviral pipeline currently exists; the framework is presented as a forward-looking research agenda with clearly defined open challenges. Collectively, this synthesis will bring computational antiviralism as an anticipatory field of study that can catalyze the broad-spectrum antiviral discovery, as well as providing preemptive countermeasures to emergent viral challenges through coordinated molecular, cellular and ecosystem-level interventions.
The shift from reversible quantum dynamics to irreversible thermodynamics is one of the most complex and at the same time the most important issue in modern physics. A new and efficient method developed by studying black holes and quantum chaos gives the problem stance as one of information dynamics. In this paper, the authors explore the scenario whereby information scrambling is viewed as the micro-level power of quantum thermalization. They start by explaining the most basic paradigms of thermalizing systems that are typified by the eigenstate thermalization hypothesis (ETH) and their non-thermalizing opposites which are characterized by many-body localization. Following that, the authors engage in a comprehensive discussion of the out-of-time-order correlator as the primary tool for diagnosing scrambling, giving a detailed account of its connection with operator spreading, quantum chaos, and the butterfly effect. The central idea that unites this review is that fast, exponential scrambling dynamics bring about ETH and thermalization while a slowdown in scrambling results in localization. An extensive review of the pioneering experiments on trapped-ion, superconducting-qubit, and other quantum-simulation platforms that have clearly witnessed these phenomena is conducted. Ultimately, physicists point out the main unresolved questions and future research paths at the meeting point of quantum gravity, condensed matter, and quantum information science, positioning scrambling as a critical concept for the next few years in many-body physics.
This study explores the transformative potential of digital twin technology within the solar photovoltaic (PV) ecosystem, emphasizing its applications, performance metrics, challenges, and future directions. By synthesizing insights from cutting-edge research, the paper provides a holistic framework that integrates digital twin features—such as architecture, modeling, software, and IoT-based integration—with the core functionalities of solar PV systems. These features enable real-time simulation, predictive analytics, and system interoperability, thereby optimizing energy generation, streamlining maintenance processes, and extending lifecycle management. The review highlights critical performance metrics, including output efficiency, cost-effectiveness, fault detection, and sustainability, offering a data-driven perspective on enhancing system reliability and reducing environmental impact. The paper also addresses significant challenges confronting the adoption of digital twins in the solar PV sector. Technological hurdles, such as data synchronization and system scalability, are explored alongside financial barriers that impede widespread deployment. Regulatory complexities, including compliance with energy standards and cybersecurity protocols, are analyzed to offer actionable recommendations for policymakers. By identifying gaps in current research—such as the lack of scalable, real-time frameworks and cost-effective deployment strategies—the study lays the groundwork for future advancements. Furthermore, the review outlines promising trends and opportunities, including the integration of artificial intelligence, blockchain, and circular economy principles in PV system management. Emerging applications in predictive fault diagnosis and enhanced grid interaction underscore the potential of digital twins to revolutionize renewable energy. The paper concludes by presenting a roadmap for researchers, practitioners, and policymakers to collaboratively advance the digital twin-powered solar PV ecosystem. By bridging interdisciplinary domains, this study aims to accelerate the transition toward sustainable, resilient, and highly efficient solar energy systems, aligning with global energy and climate goals. This comprehensive review not only elucidates the current landscape but also serves as a catalyst for innovation, equipping stakeholders with a strategic vision to harness the full potential of digital twins in driving the next generation of solar PV technologies.
