Electrolytes, as the lifeblood of energy storage systems, play a pivotal role in determining the performance metrics of supercapacitors and metal-ion batteries, including energy density, power output, cycle life, and safety. This review presents a comprehensive analysis of composition–performance relationships across aqueous, organic, ionic liquid, and solid-state electrolyte systems, with particular emphasis on their adaptability challenges and innovative solutions for electric double-layer capacitors, pseudocapacitors, and lithium/sodium/potassium/zinc-ion batteries. For supercapacitor applications, the breakthroughs have been achieved through high-concentration electrolytes (e.g., Water-in-Salt systems), by which the voltage window is extended to 3.0 V, and through solid-state electrolytes that enable flexible device integration. Regarding metal-ion batteries,the synergistic effects of novel fluorinated solvents, multifunctional additives, and composite solid-state electrolytes are elucidated in enhancing interfacial stability and energy density, with energy densities exceeding 500 Wh/kg being achieved and capacity retention rates > 90% being maintained after 200 cycles. Furthermore, the transformative impact of AI-driven high-throughput screening on accelerating electrolyte molecular design is analyzed and forward-looking perspectives are provided on emerging technologies, including all-solid-state devices, biodegradable electrolytes, and multivalent ion transport systems. This work offers both theoretical foundations and practical design guidelines for next-generation energy storage systems with superior safety and energy density.

With the urgent demand for high-performance energy storage materials in the global energy transition, traditional experimental trial and error methods are difficult to meet the rapid research and development needs owing to long cycles and high costs. In recent years, the deep integration of computational materials science and artificial intelligence (AI) technology has provided revolutionary tools for the rational design and performance optimization of energy storage materials. This article systematically reviews the progress of research on energy storage material computation and AI systems. At the traditional method level, quantum mechanics computation (such as VASP, Quantum ESPRESSO), molecular dynamics (such as LAMMPS, GROMACS), and high-throughput computing platforms (such as Materials Project) have achieved accurate predictions of material electronic structure, interface dynamics, and high-throughput screening. At the AI-driven level, generative models (GNoME, 3D-GPT), graph neural networks (MEGNet, CGCNN), and experimental computational closed–loop systems (such as the autonomous driving laboratory A-Lab) have significantly accelerated the discovery and reverse design of new materials. Further focusing on the integration trend of multi-scale modeling and AI, physical information-driven AI models (DPMD, PINNs) and cross-scale integration platforms (ASE, MedeA) are driving the collaborative improvement of material simulation accuracy and efficiency. However, data scarcity, computational bottlenecks caused by multi-physics coupling, and barriers to tool industrialization remain current challenges. In the future, sustainable design paradigms, open-source ecological construction, and human-machine collaboration models will lead the research and development of energy storage materials into the era of “digital priority.” This article aims to provide a technical roadmap reference for interdisciplinary research and call for collaboration between academia and industry to overcome key bottlenecks and accelerate the innovation breakthrough and large-scale application of energy storage materials.

In light of the escalating global prevalence of hair loss, there is an imperative to explore strategies for the prevention and promotion of hair growth. This article reviews the current situation, challenges, innovations, and prospects of cosmetics that promote anti-hair loss and hair growth. Firstly, the physiological and pathological mechanisms of hair loss, including androgenetic alopecia, telogen effluvium, and alopecia areata, as well as the influence of genetic, environmental, and lifestyle factors, are explored. Subsequently, a comprehensive analysis of the predominant product categories and ingredients currently available on the market was conducted, encompassing minoxidil, finasteride, plant extracts, growth factors, and peptides. Building on this, this article further explores the challenges of anti-hair loss and hair growth promotion cosmetics, including effectiveness and safety, consumer acceptance, and the complexity of regulations and standards. This was followed by an introduction to innovations in the field, such as gene therapy, stem cell technology, and microneedling, as well as advanced delivery systems and personalized care options. Finally, this paper looks forward to future technologies’ development trends and market prospects. It emphasizes the importance of multidisciplinary cooperation, including the combination of medicine and cosmetology and the integration of biotechnology and materials science. By synthesizing extant research and delineating prospective research directions, this paper establishes an indispensable reference point for the research and development of cosmetics designed to promote hair growth and prevent hair loss. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .