With the global shift towards sustainable energy sources, the demand for more efficient battery systems becomes increasingly urgent. Current lithium-ion batteries, while widely adopted, face significant challenges, including resource scarcity and limited energy density. These factors drive the need for viable alternatives, such as calcium-ion batteries, which offer a promising solution. CIBs leverage abundant materials on Earth and possess an electrochemical window that could potentially rival that of traditional lithium-ion batteries. However, to date, they have struggled with issues related to efficient cation transport and consistent performance over extended use.

Led by Professor Yoonseob Kim, Associate Professor of the Department of Chemical and Biological Engineering at HKUST, the research team embarked on a mission to address these pressing challenges by developing redox covalent organic frameworks. These materials serve as QSSEs, enhancing the ionic conductivity of the battery system. Remarkably, the QSSEs exhibited an ionic conductivity of 0.46 mS cm⁻¹ and a Ca²⁺ transport capability exceeding 0.53 at room temperature. This breakthrough in material science opens new avenues for achieving stable, high-performance CIB technology.

During the experimental phase, the researchers conducted a comprehensive analysis combining both experimental data and simulation studies. The investigation revealed that Ca²⁺ ions move rapidly along the aligned carbonyl groups embedded within the ordered covalent organic framework’s pores. This understanding is crucial for optimizing the performance of calcium-ion batteries and illustrates the unique advantages presented by the new materials in comparison to traditional electrolytes.

The innovative work culminated in the successful fabrication of a complete calcium-ion cell which demonstrated a reversible specific capacity of 155.9 mAh g⁻¹ at a current density of 0.15 A g⁻¹. Additionally, after enduring 1,000 cycles at 1 A g⁻¹, the battery retained over 74.6% of its capacity, showcasing the potential longevity and reliability of this new battery design. This performance marks a pivotal step towards making CIBs a competitive alternative to existing lithium-ion systems, potentially transforming the energy storage landscape.

“By harnessing the unique characteristics of redox covalent organic frameworks, our research illustrates the transformative potential of calcium-ion batteries as a sustainable counterpart to lithium-ion technology,” remarks Prof. Kim. This statement encapsulates the team’s vision of not just creating a functioning battery but contributing to a more sustainable energy future, capable of supporting the global transition towards greener alternatives.

The implications of this research extend far beyond laboratory confines. The enhanced performance and sustainability of calcium-ion batteries present opportunities for integration in various applications, from renewable energy storage systems to electric vehicles. As the world increasingly prioritizes reductions in carbon emissions and the adoption of clean energy sources, the role of efficient and economically viable energy storage systems becomes indispensable.

While the road to widespread adoption of calcium-ion batteries may still involve overcoming regulatory hurdles and market acceptance, the research undertaken at HKUST showcases the foundational innovations required to inspire confidence in alternative energy storage solutions. The collaboration between HKUST and Shanghai Jiao Tong University highlights the importance of international partnerships in tackling complex challenges facing global energy needs.

In conclusion, this groundbreaking research on quasi-solid-state calcium-ion batteries signifies a potential shift in energy storage paradigms. By leveraging new materials and innovative designs, researchers are paving the way for a future where sustainable energy solutions can effectively meet the growing demands of modern society. As developments continue, the excitement around CIB technology is palpable, and its eventual commercialization could herald a new era in energy storage.

Strong collaborations in academia and industry will be vital to the successful transition from research findings to practical applications. More research will undoubtedly follow, with teams around the world looking to capitalize on the discoveries made by Prof. Kim and his colleagues. The trajectory set by this research promises not just improvements in functionality, but also a broader impact on global energy sustainability.

The findings discussed pave the way for further investigations into the scalability of this technology and its integration into commercial products. With continued advancement in battery technology, we stand on the edge of a transformative era where energy storage systems can become more efficient, sustainable, and accessible for everyone.

As interest grows in this critical area of research, the implications extend to policy-makers, industry leaders, and consumers alike, all of whom stand to benefit from a global shift towards more sustainable and reliable energy solutions. The role of innovative research as a catalyst for change cannot be overstated, and the breakthroughs occurring at institutions like HKUST reinforce the necessity of continued investment in energy research and development.

The research team’s achievements not only contribute to academic literature but also underscore the importance of applied science in addressing the most pressing challenges of our time. Through their exploration of calcium-ion technology, they offer a glimpse into the future of energy storage that aligns with our collective aspirations for a cleaner, more sustainable planet.

Subject of Research: Calcium-ion battery technology
Article Title: High-Performance Quasi-Solid-State Calcium-Ion Batteries from Redox-Active Covalent Organic Framework Electrolytes
News Publication Date: 16-Nov-2025
Web References: Advanced Science
References: 10.1002/advs.202512328
Image Credits: Credit: HKUST

Keywords

Alternative energy, Energy resources, Applied sciences, Engineering

For decades, scientists have relied predominantly on detergent-based techniques to extract membrane proteins from cell membranes. While these methods have advanced our understanding to a considerable extent, they are not without significant drawbacks. Detergents can disrupt the natural state of membrane proteins by removing essential lipids, which are crucial for their proper functioning. This extraction process often results in suboptimal conditions for studying the proteins, leading to incomplete or misleading data regarding their structure and function.

Recognizing these challenges, Professor Dang and his research team embarked on a four-year systematic investigation to develop a more efficient and effective method for membrane protein extraction and analysis. By employing innovative techniques, they succeeded in generating vesicles that encapsulate membrane proteins directly from their native cell membranes. This vesicle-based approach eliminates the need for detergents, allowing for a more authentic representation of the proteins in their natural environment.

In their rigorous studies, the team established a comprehensive workflow designed to prepare, purify, and control the quality of the vesicle samples. This method’s versatility holds immense promise for investigating various membrane systems, expanding the scope of research possibilities. In addition, they integrated an artificial intelligence model with a micrograph-based sorting technique to specifically isolate high-quality membrane protein particles, effectively addressing the background signals that often hinder clarity in structural analysis.

The practical applications of this revolutionary method were quickly demonstrated. The team successfully resolved the structure of the overexpressed AcrB protein present in Escherichia coli cell membranes at an impressive resolution of 3.9 Å. Furthermore, they elucidated the structure of the respiratory chain complex III found in porcine heart mitochondrial inner membranes, achieving a remarkable resolution of 3.0 Å. These findings not only underscore the method’s efficacy but also highlight its potential to substantially advance the field of cryo-electron microscopy.

Liu Hang, a Ph.D. candidate and the first author of the study, emphasized the importance of this research, stating, “Leveraging Prof. Dang’s multidisciplinary approach, our team has successfully developed a comprehensive system for in-situ structural studies of membrane proteins.” This assertion reflects the rigorous effort taken to overcome previously insurmountable obstacles in membrane protein research, which for years have stymied scientists’ ability to fully decipher the complexities of membrane biology.

One of the most significant advantages of this vesicle-based approach is its cost-effectiveness and simplicity. Traditional detergent extraction methods often require extensive resource allocation and complicated procedures. In contrast, Professor Dang’s innovative technique streamlines the process, making it more accessible to researchers across various disciplines. By maintaining the natural conformation of membrane proteins as closely as possible, this method promises to yield more accurate and biologically relevant results, paving the way for more in-depth studies of membrane dynamics and their implications in disease states.

Prof. Dang poignantly remarked on the implications of their work, stating, “This vesicle-based platform preserves physiological lipid environments while eliminating the burdens of detergent screening. It provides an opportunity to study membrane proteins in their native environments.” He expressed optimism regarding future optimizations, which could facilitate structural proteomics of membrane proteins in specific biological membrane systems, such as mitochondria. This research is especially pertinent given the increasing recognition of the critical roles that membrane proteins play in various diseases, underscoring the potential to offer valuable insights that could inform therapeutic strategies.

The implications of this research extend beyond mere structural elucidation; they could significantly impact drug discovery and development processes. Membrane proteins are often the targets of pharmaceuticals due to their role in numerous signaling pathways and physiological processes. By advancing the ability to study these proteins in their native environments, researchers may identify new therapeutic targets or improve existing drug efficacy, eventually leading to better health outcomes for patients.

Moreover, as the method demonstrates versatility across different membrane proteins from various species and cellular structures, it holds the promise of broadening the scope of cryo-electron microscopy as a tool for structural biology. This methodology could enable researchers to explore previously understudied membrane proteins, contributing to a deeper understanding of a myriad of biological processes and potentially uncovering novel biological mechanisms linked to health and disease.

Published in the prestigious Proceedings of the National Academy of Sciences, this research has garnered significant attention as it propels the field of membrane protein research into a new era. With Professor Dang as the corresponding author and Liu Hang leading as the first author alongside undergraduate researcher Tse Chun Mong, the study not only exemplifies the spirit of innovation and collaboration in scientific research but also illustrates the critical pathway toward bridging gaps in understanding membrane biology.

This seminal work on vesicle-based studies punctuates the ongoing evolution of methodologies in the biological sciences, reminding researchers of the importance of adaptability and creativity in overcoming longstanding scientific challenges. As the world of structural biology continues to expand, this new approach heralds exciting possibilities for future discoveries that can profoundly impact our understanding of life at the molecular level.

Subject of Research: Cells
Article Title: Capturing the native structure of membrane proteins using vesicles
News Publication Date: 3-Sep-2025
Web References: Proceedings of the National Academy of Sciences
References: DOI 10.1073/pnas.2423407122
Image Credits: Credit: HKUST

Keywords

Life sciences

The Boltzmann distribution, a cornerstone of statistical mechanics, fundamentally governs the probability of states in physical systems at thermal equilibrium. Understanding this distribution is essential for elucidating a broad range of phenomena from phase transitions and chemical reaction mechanisms to the conformations of biomolecules. Yet, numerical methods have historically struggled with sampling the Boltzmann distribution effectively, especially for systems characterized by rugged energy landscapes or high energy barriers. Conventional approaches, such as molecular dynamics (MD) simulations and Markov Chain Monte Carlo (MCMC) techniques, are computationally intensive, often requiring prohibitively long timescales to converge ensemble averages with statistical confidence.

Inspired by recent breakthroughs in deep learning, particularly in generative models capable of crafting realistic data distributions, the HKUST team devised a novel method called Variational Temperature-Differentiable (VaTD) sampling. Unlike traditional techniques confined to fixed temperatures or discrete sampling points, VaTD uniquely treats temperature as a continuous variable within its generative framework. This allows the direct sampling of thermodynamic ensembles over a continuous temperature domain, streamlining the estimation of free energies, heat capacities, and related thermodynamic properties with unprecedented precision.

The VaTD framework is notably model-agnostic, accommodating a wide array of tractable density generative models including autoregressive architectures and normalizing flows. Through the integration of differentiable programming, the model exploits automatic differentiation to compute both first- and second-order temperature derivatives of thermodynamic observables. This approach effectively approximates an analytical partition function, a central yet elusive quantity in statistical mechanics that encapsulates all thermodynamic information about a system under study.

One of the most striking advantages of VaTD is its ability to transcend energy landscape barriers that traditionally hindered sampling efficiency. By integrating over continuous temperatures, the model naturally navigates between low- and high-energy conformations, enhancing the representational fidelity of sampled states. Theoretically, the method offers a guarantee of unbiased sampling, circumventing the systematic errors common in prior generative approaches. This breakthrough opens the door to exploring subtle phase behaviors and rare-event phenomena that are otherwise computationally inaccessible.

In contrast to prevailing generative modeling methods relying heavily on pre-existing datasets derived from extensive MD or Monte Carlo trajectories, VaTD requires only the potential energy function of the targeted physical system. This “first-principles” feature amplifies its applicability across diverse domains without the prohibitive cost of generating large training sets. The researchers rigorously validated their approach using classical models from statistical physics such as the Ising model and the XY model, demonstrating remarkable accuracy and efficiency gains in thermodynamic predictions.

Professor Pan Ding expressed enthusiasm about the broader implications of this method beyond physics: “This advancement offers a new lens to study emergent phenomena in complex statistical systems, which can benefit disciplines spanning chemistry, materials science, and even biological systems.” Indeed, the ability to precisely characterize canonical ensembles with integrated thermal derivatives promises to accelerate the design of novel materials and the understanding of biomolecular dynamics where temperature-dependent behavior is paramount.

Further enhancing the impact of their research, the HKUST team harnessed the computational prowess of the “Tianhe-2” supercomputer at the National Supercomputer Center in Guangzhou, enabling large-scale simulations crucial for method validation. Their work received financial support from prominent institutions including the Hong Kong Research Grants Council, the Croucher Foundation, and the National Natural Science Foundation of China’s National Excellent Young Scientists Fund, underscoring the strategic importance of blending artificial intelligence with foundational physics research.

Dr. Li Shuo-Hui, a co-first author on the study alongside PhD student Zhang Yaowen, highlighted the method’s versatility in potential applications: “By embedding the thermodynamic temperature as a differentiable parameter within a generative model, we unlock new computational pathways to probe systems where traditional simulations stall due to prohibitively slow dynamics or complex energy landscapes.” This innovation could revolutionize how scientists probe critical phenomena, catalysis, and materials phase stability, offering computational alternatives where experiments or classical simulations are challenging.

Fundamentally, the VaTD approach marks a conceptual shift by fusing variational inference techniques with thermal physics, bridging the gap between abstract mathematical models and physical interpretability. The ability to compute analytical derivatives concerning temperature not only accelerates thermodynamic informatics but also provides deeper insights into the thermal response functions governing system behavior. This positions VaTD as a compelling tool for understanding temperature-driven transitions and thermodynamic fine structure in multi-dimensional parameter spaces.

The publication of this work in the prestigious journal Physical Review Letters heralds a new era for computational statistical mechanics, showcasing how contemporary artificial intelligence methodologies can resolve decades-old obstacles in sampling efficiency. As the field continues to integrate machine learning with first-principles physics, such innovations promise to redefine the computational landscape for unraveling the complexity of natural and engineered systems.

Looking forward, the HKUST team aims to extend the capability of VaTD to quantum systems and more intricate molecular assemblies, envisioning an ecosystem where generative modeling seamlessly augments experimental and theoretical studies. The fusion of deep learning and statistical mechanics embodied by VaTD foreshadows a future where computationally tractable, high-fidelity simulations routinely inform breakthroughs in condensed matter physics, chemical engineering, and quantitative biology.

This advancement not only exemplifies the transformative potential of deep learning in physical sciences but also underscores the essential role of interdisciplinary collaboration. Bridging expertise in physics, chemistry, computer science, and applied mathematics has enabled the realization of a method that transcends traditional disciplinary boundaries, setting a paradigm for future research efforts aiming to merge analytical rigor with computational innovation.

In summary, the VaTD method introduced by HKUST researchers significantly enhances our capability to model canonical ensembles across continuous temperature spectra efficiently and accurately. By combining deep generative modeling with differentiable thermal parameters, it overcomes many computational challenges inherent to high-dimensional statistical systems with complex energy landscapes. This breakthrough promises widespread impact across the physical sciences, heralding a new frontier in computational thermodynamics that leverages the power of artificial intelligence to unravel nature’s intricate thermal behavior.

Subject of Research: Statistical mechanics; deep generative modeling; thermodynamics; Boltzmann distribution sampling

Article Title: Deep Generative Modeling of the Canonical Ensemble with Differentiable Thermal Properties

News Publication Date: 8-Jul-2025

Web References: https://doi.org/10.1103/8wx7-kyx8

Image Credits: HKUST

Keywords

Statistical mechanics, Boltzmann distribution, deep generative models, Variational Temperature-Differentiable (VaTD) method, thermodynamic sampling, molecular dynamics, Markov Chain Monte Carlo, partition function approximation, machine learning, autoregressive models, normalizing flows, computational physics