Advances and Future Prospects of Micro‐Silicon Anodes for High‐Energy‐Density Lithium‐Ion Batteries: A Comprehensive Review

Advanced Functional Materials, Год журнала: 2024, Номер unknown

Опубликована: Апрель 9, 2024

Abstract Silicon (Si), stands out for its abundant resources, eco‐friendliness, affordability, high capacity, and low operating potential, making it a prime candidate high‐energy‐density lithium‐ion batteries (LIBs). Notably, the breakthrough use of nanostructured Si (nSi) has paved way commercialization anodes. Despite this, challenges like processing costs, severe side reactions, volumetric energy density have impeded widespread industrial adoption. Micron‐scale (µSi) always faced setbacks compared to nSi due greater volume expansion. However, recent years witnessed resurgence interest in µSi‐based Capitalizing on inherent advantages, including cost tap density, µSi once again captured attention both academic communities. This review begins by contrasting strengths weaknesses nSi, then outline potential solutions enhance performance, covering aspects structural regulation, composite anodes, binder design, electrolyte exploration. Additionally, this work explores application machine learning‐assisted high‐throughput screening. Concluding review, provides insights into future prospects LIBs, outlining proposing integrated coping strategies. anticipates that will provide valuable perspectives commercial Si‐based

Язык: Английский

---

A Review on Engineering Transition Metal Compound Catalysts to Accelerate the Redox Kinetics of Sulfur Cathodes for Lithium–Sulfur Batteries

Nano-Micro Letters, Год журнала: 2024, Номер 16(1)

Опубликована: Янв. 29, 2024

Abstract Engineering transition metal compounds (TMCs) catalysts with excellent adsorption-catalytic ability has been one of the most effective strategies to accelerate redox kinetics sulfur cathodes. Herein, this review focuses on engineering TMCs by cation doping/anion doping/dual doping, bimetallic/bi-anionic TMCs, and TMCs-based heterostructure composites. It is obvious that introducing cations/anions or constructing can boost capacity regulating electronic structure including energy band, d / p -band center, electron filling, valence state. Moreover, doped/dual-ionic are adjusted inducing ions different electronegativity, ion radius, resulting in redistribution, bonds reconstruction, induced vacancies due interaction changed crystal such as lattice spacing distortion. Different from aforementioned two strategies, heterostructures constructed types Fermi levels, which causes built-in electric field electrons transfer through interface, induces redistribution arranged local atoms regulate structure. Additionally, lacking studies three comprehensively for improving catalytic performance pointed out. believed guide design advanced boosting lithium batteries.

Язык: Английский

---

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

ACS Catalysis, Год журнала: 2024, Номер 14(15), С. 11749 - 11779

Опубликована: Июль 24, 2024

This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field heterogeneous catalysis, presenting a broad spectrum contemporary methodologies innovations. We methodically segmented text three core areas: catalyst characterization, data-driven exploitation, discovery. In characterization part, we outline current prospective techniques used for HTE how AI-driven strategies can streamline or automate their analysis. The exploitation part is divided themes, strategies, that offer flexibility either modular application creation customized solutions. exploration present applications enable areas outside experimentally tested chemical space, incorporating section on computational methods identifying new prospects. concludes by addressing limitations within suggesting possible avenues future research.

Язык: Английский

---

From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

Advanced Functional Materials, Год журнала: 2024, Номер 34(34)

Опубликована: Апрель 25, 2024

Abstract The rapid advancement of high‐performance computing and artificial intelligence technology has opened up novel avenues for the development various metal electrocatalysts. In particular, dilute high‐entropy alloys have garnered significant attention owing to their unique electronic spatial structures, as well exceptional electrocatalytic performance. Commencing with exploration single‐atom alloy catalysts, latest advancements in machine learning (ML) techniques are presented efficient screening a broad spectrum spaces. Subsequently, review delves into prevailing trend research, focusing specifically on rare‐metal electrocatalysts, offers an overview progress outcomes achieved through application ML these domains. Finally, highlighted promising category electrocatalysts underscore importance potential applications addressing complex challenging research issues underscored.

Язык: Английский

---

AI in single-atom catalysts: a review of design and applications

Journal of Materials Informatics, Год журнала: 2025, Номер 5(1)

Опубликована: Фев. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Язык: Английский

---

Future prospects of high-entropy alloys as next-generation industrial electrode materials

Chemical Science, Год журнала: 2024, Номер 15(23), С. 8664 - 8722

Опубликована: Янв. 1, 2024

High-entropy alloys hold significant promise as electrode materials, even from industrial aspect. This potential arises their ability to optimize electronic structures and reaction sites, stemming complex adjustable composition.

Язык: Английский

---

High-Entropy Oxides as Energy Materials: From Complexity to Rational Design

Materials Futures, Год журнала: 2024, Номер 3(4), С. 042103 - 042103

Опубликована: Окт. 8, 2024

Abstract High-entropy oxides (HEOs), with their multi-principal-element compositional diversity, have emerged as promising candidates in the realm of energy materials. This review encapsulates progress harnessing HEOs for conversion and storage applications, encompassing solar cells, electrocatalysis, photocatalysis, lithium-ion batteries, solid oxide fuel cells. The critical role theoretical calculations simulations is underscored, highlighting contribution to elucidating material stability, deciphering structure-activity relationships, enabling performance optimization. These computational tools been instrumental multi-scale modeling, high-throughput screening, integrating artificial intelligence design. Despite promise, challenges such fabrication complexity, cost, hurdles impede broad application HEOs. To address these, this delineates future research perspectives. include innovation cost-effective synthesis strategies, employment situ characterization micro-chemical insights, exploration unique physical phenomena refine performance, enhancement models precise structure-performance predictions. calls interdisciplinary synergy, fostering a collaborative approach between materials science, chemistry, physics, related disciplines. Collectively, these efforts are poised propel towards commercial viability new technologies, heralding innovative solutions pressing environmental challenges.

Язык: Английский

---

Data-driven discovery of a formation prediction rule on high-entropy ceramics

Acta Materialia, Год журнала: 2023, Номер 253, С. 118955 - 118955

Опубликована: Апрель 30, 2023

Язык: Английский

---

Accelerating materials discovery for electrocatalytic water oxidation via center-environment deep learning in spinel oxides

Journal of Materials Chemistry A, Год журнала: 2024, Номер 12(30), С. 19362 - 19377

Опубликована: Янв. 1, 2024

Using DFT and machine learning, we evaluated 5329 spinel oxides identified 14 promising OER electrocatalysts. Experimentally, MoAg 2 O 4 showed superior performance, achieving 10 mA cm −2 at 284 mV overpotential, surpassing commercial RuO .

Язык: Английский

---

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

ChemElectroChem, Год журнала: 2024, Номер 11(13)

Опубликована: Апрель 11, 2024

Abstract Electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage global environmental pollution. Searching for efficient electrocatalysts HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning possible method screen excellent effectively. This will pave way high‐efficiency low‐price electrocatalyst findings. In this review, we comprehensively introduce workflow standard models reduction reactions. mainly illustrates how used in catalyst filtration descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) single/dual atom using electrocatalytic HER, are highlighted introduced. Finally, corresponding challenge perspective reactions concluded. We hope critical review can provide comprehensive understanding of design guide future theoretical experimental investigation

Язык: Английский

---