Inorganic interface engineering for stabilizing Zn metal anode

Shanghai Jiao Tong University Journal Center

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• A broad overview of the inorganic interface engineering strategies, along with deep analysis of the mechanisms on regulating the Zn2+ plating/stripping process.
• Identify the limitations of interface engineering strategies and provide our perspective on the future research, highlighting more comprehensive analysis of the interfaces.

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Credit: Shuguo Yuan, Wenqi Zhao, Zihao Song, Hai Lin, Xiangyang Zhao, Zhenxing Feng, Zhichuan J. Xu, Hongjin Fan, Qingli Zou*.

As lithium costs soar and safety incidents mount, aqueous zinc-ion batteries (AZMBs) promise a cheap, non-flammable alternative—yet zinc anodes still rot from within, sprouting dendrites that short-circuit cells in days while hydrogen bubbles swell the pack. Now, a multi-institute team led by Prof. Qingli Zou (Beijing University of Chemical Technology) and Prof. Hongjin Fan (Nanyang Technological University) has delivered a design manual in Nano-Micro Letters that turns commercial Zn foil into an ultra-stable anode through simple, low-cost inorganic coatings, pushing symmetric cells beyond 6500 h and pouch cells past 200 cycles at 10 mAh cm-2.

Why Inorganic Interface Engineering Matters

· Dendrite Suppression: Dense Al2O3, ZnO or TiO2 layers homogenize surface charge and provide zincophilic nucleation sites, forcing lateral (002)-textured growth instead of mossy filaments.

· Hydrogen Evolution Blockade: Phosphate, silicate or MXene barriers physically isolate water from the metal, cutting HER to <0.1 % per cycle and eliminating cell swelling.

· High Areal Capacity: Non-consumable, corrosion-resistant coatings tolerate >10 mAh cm-2 and 50 mA cm-2—meeting practical targets for grid storage and e-mobility.

Innovative Design & Features

· Material Palette: Metal oxides (TiO2, ZrO2, Nb2O5), nitrides (TiN, CrN), sulfides/selenides (ZnS, ZnSe), MXenes (Ti3C2Cl2) and acid salts (Zn3(PO4)2, sepiolite) are compared for ionic conductivity, adsorption energy and mechanical strength.

· Structure Engineering: Atomic-layer-deposited 10 nm Al2O3, micro-concave ZnO, hollow ZnSnO3 cubes and 45 nm zinc-phosphate SEI each demonstrate specific pathways to guide Zn2⁺ flux and suppress side reactions.

· Scale-Up Compatibility: All coatings are deposited by spray, dip, ALD or simple chemical bath—compatible with roll-to-roll processing of commercial Zn foil.

Applications & Future Outlook

· Grid-Scale Storage: A 1 Ah Zn||V2O5 pouch cell with Zn-phosphate interface retains 80 % capacity after 200 cycles at 10 mAh cm-2, projecting <$60 kWh-1 system cost.

· Flexible Devices: 10 nm MXene-coated Zn anodes survive 1 500 bends in Zn-I2 thread batteries, enabling wearable e-textiles.

· Next Steps: Team is integrating AI-guided lattice-matching models and in-line thickness monitoring to transfer the technology to 100 Ah modules by 2026.

This roadmap converts the zinc anode from a liability into a long-lived, high-energy asset, positioning AZMBs as the front-runner for safe, sustainable and low-cost energy storage.

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Journal

Nano-Micro Letters

DOI

10.1007/s40820-025-01922-x 

Method of Research

News article

Article Title

Inorganic Interface Engineering for Stabilizing Zn Metal Anode

Article Publication Date

21-Jan-2026

 

Data-driven approach unveils key trends in research talent evaluation at Chinese universities

Higher Education Press

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Credit: HIGHER EDUCATON PRESS

Researchers from Beihang University have conducted a comprehensive bibliometric analysis to identify evolving trends and challenges in evaluating research talent at Chinese universities. Their study, published in Frontiers of Computer Science on 15 December 2025, reveals a significant shift from theoretical policy discussions to practical, multidimensional evaluation frameworks, driven by China’s “Double World-Class” initiative. These findings provide critical insights for universities aiming to modernize talent assessment systems and reduce overreliance on traditional metrics like paper publications.
Current evaluation systems often struggle with outdated criteria, poor expert selection, and inconsistent standards. By applying data mining techniques—such as co-occurrence analysis and clustering—to 1,696 academic articles (2014–2024), the team uncovered five key research clusters, including “Educational Evaluation and Reform” and “Talent Cultivation.” Post-2020, research has increasingly focused on integrating industry-education collaboration and aligning career development with institutional goals, reflecting national efforts to build world-class universities. This data-driven approach addresses gaps in traditional methods, offering actionable strategies to enhance fairness, innovation, and societal impact in talent evaluation.
The team utilized advanced tools like Bicomb and CiteSpace to analyze keyword co-occurrence and cluster patterns in Chinese core journals. By merging synonyms and filtering high-frequency terms, they mapped the evolution of research priorities, validated by strong intra-cluster similarity metrics (ISim ≈ 0.07). Visualizations, including co-occurrence matrices and strategic coordinate diagrams, highlight the growing emphasis on interdisciplinary education and teacher development.
The study urges universities to adopt dynamic evaluation systems that reflect diverse contributions, from academic achievements to societal impact. As China accelerates its “Double World-Class” project, these insights could reshape global higher education practices, fostering innovation and equitable talent recognition.
 

 

Top 15 Keywords with the strongest citation bursts

Credit

HIGHER EDUCATON PRESS

Journal

Frontiers of Computer Science

DOI

10.1007/s11704-025-50029-4 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Bibliometric analysis of research talent evaluation in Chinese universities: data mining approach

 

Breaking through tropical cyclone intensity prediction: A foundation model Prithvi-TC

Higher Education Press

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A groundbreaking artificial intelligence model has achieved unprecedented accuracy in tropical cyclone intensity prediction, marking a significant advancement in weather forecasting technology. The new system, known as Prithvi-TC, addresses one of the most challenging aspects of meteorological forecasting - predicting tropical cyclone (TC) intensity and rapid intensification events. This advancement comes at a crucial time, as climate change continues to influence the frequency and intensity of tropical cyclones worldwide.

The new model represents a significant leap forward in weather prediction technology, demonstrating superior performance in both accuracy and computational efficiency. By leveraging state-of-the-art artificial intelligence techniques, Prithvi-TC has achieved a remarkable 40% reduction in forecast errors compared to existing methods, particularly in predicting rapid intensification events - a critical capability that has long challenged traditional forecasting systems.

At the heart of this breakthrough lies a sophisticated three-stage framework that combines comprehensive meteorological data from multiple sources. The system processes vast amounts of information, including satellite observations, atmospheric measurements, and historical cyclone data, to generate highly accurate predictions. This integration of diverse data sources, coupled with advanced machine learning techniques, enables the model to capture complex weather patterns and cyclone behavior with unprecedented precision.

The technology's success stems from its innovative approach to data processing and analysis. By utilizing a specialized attention mechanism, the system can simultaneously focus on both local cyclone features and broader environmental conditions that influence storm development. This dual-focus approach, combined with a multi-scale feature integration system, allows for more nuanced and accurate predictions than previously possible.

Performance evaluations have demonstrated the system's remarkable capabilities. In comprehensive testing against existing prediction methods, including both traditional numerical weather prediction systems and other AI-based models, Prithvi-TC consistently showed superior accuracy. Particularly noteworthy is its performance in predicting rapid intensification events - a critical aspect of tropical cyclone behavior that has historically been difficult to forecast accurately.

The implications of this technological breakthrough extend far beyond academic research. More accurate tropical cyclone predictions can significantly improve disaster preparedness and emergency response capabilities. For coastal communities and regions frequently affected by tropical cyclones, this advancement could translate into more effective early warning systems, better-planned evacuations, and ultimately, saved lives and reduced economic losses.

The system's success in tropical cyclone prediction also opens new possibilities for applying similar AI approaches to other extreme weather events. The technology demonstrates how modern artificial intelligence can effectively bridge the gap between pure data-driven approaches and traditional physics-based modeling, potentially revolutionizing various aspects of weather forecasting.

This breakthrough in prediction technology arrives at a critical time in climate science. As global weather patterns become increasingly volatile due to climate change, the ability to accurately predict extreme weather events becomes ever more crucial. The development of more accurate prediction systems like Prithvi-TC represents a significant step forward in our ability to understand and prepare for severe weather events.

Looking to the future, this advancement suggests a new era in weather forecasting where artificial intelligence plays an increasingly central role. The success of this model in handling the complexities of tropical cyclone prediction indicates potential applications in other areas of meteorological forecasting. As these technologies continue to evolve, they promise to enhance our understanding of weather systems and improve our ability to prepare for and respond to extreme weather events.

The development of this advanced prediction system represents a significant milestone in meteorological science, showcasing the potential of artificial intelligence to transform weather prediction. As climate change continues to affect weather patterns globally, such technological advancements become increasingly crucial for protecting communities and infrastructure from extreme weather events.

This research breakthrough exemplifies the growing role of artificial intelligence in solving complex environmental challenges and marks a new chapter in the evolution of weather forecasting technology. As these systems continue to develop and improve, they promise to enhance our ability to prepare for and respond to severe weather events, ultimately contributing to better disaster preparedness worldwide.

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Journal

Frontiers of Computer Science

DOI

10.1007/s11704-025-41268-6 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Breaking through tropical cyclone intensity prediction: a foundation model Prithvi-TC

 

Towards spatial computing: Recent advances in multimodal natural interaction for XR headsets

Higher Education Press

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 Taxonomy of Operation Types and Interaction Modalities

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Credit: HIGHER EDUCATON PRESS

Researchers have conducted the comprehensive review of recent advances in multimodal natural interaction techniques for Extended Reality (XR) headsets, revealing significant trends in spatial computing technologies. This timely review analyzes how recent breakthroughs in artificial intelligence (AI) and large language models (LLMs) are transforming how users interact with virtual environments, offering valuable insights for the future development of more natural, efficient, and immersive XR experiences.

A research team led by Feng Lu systematically reviewed 104 papers published since 2022 in six top venues and published their new review article on 15 December 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

With the widespread adoption of Extended Reality headsets like Microsoft HoloLens 2, Meta Quest 3, and Apple Vision Pro, spatial computing technologies are gaining increasing attention. Natural human-computer interaction is at the core of spatial computing, enabling users to interact with virtual elements through intuitive methods such as eye tracking, hand gestures, and voice commands.

The review classifies interactions based on application scenarios, operation types, and interaction modalities. Operation types are divided into seven categories, distinguishing between active interactions (where users input information) and passive interactions (where users receive feedback). Interaction modalities are explored across nine distinct types, ranging from unimodal interactions (gesture, gaze, speech, or tactile only) to various multimodal combinations.

Statistical analysis of the reviewed literature reveals significant trends. Hand gesture and eye gaze interactions, including their combined modalities, remain the most prevalent. However, there has been a notable increase in speech-related studies in 2024, likely driven by recent advancements in LLMs. Regarding operation types, pointing and selection remains the most focused area, although the number of studies has been decreasing annually, possibly due to the maturity of this research area. Conversely, research on locomotion, viewport control, typing, and querying has increased, reflecting growing attention on users' subjective experiences and the integration of LLMs.

The researchers also identified several challenges in current natural interaction techniques. For example, gesture-only interactions often require users to adapt to complex paradigms, which increases cognitive load. Eye gaze interactions face issues with the "Midas touch" problem, where users unintentionally select items they are merely looking at. Speech-based interactions struggle with latency and recognition accuracy.

Based on these findings, the research team suggests potential directions for future research, including:

1. Developing more accurate and reliable natural interactions through multimodal integration and error recovery mechanisms
2. Enhancing the naturalness, comfort, and immersion of XR interactions by reducing physical and cognitive load
3. Leveraging AI and LLMs to enable more sophisticated, context-aware interactions
4. Bridging interaction design and practical XR applications to encourage wider adoption

The paper includes detailed illustrations of various interaction techniques, such as gesture-based drawing, gaze vergence control, and LLM-based speech interactions, providing a valuable reference for researchers and practitioners in the field.

This review offers important insights for researchers designing natural and efficient interaction systems for XR, ultimately contributing to the advancement of spatial computing technologies that could transform how we interact with digital information in our daily lives.

Application Scenarios of Multimodal Natural Interaction

Credit

HIGHER EDUCATON PRESS

Journal

Frontiers of Computer Science

DOI

10.1007/s11704-025-41123-8 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Towards spatial computing: recent advances in multimodal natural interaction for Extended Reality headsets