Perspectives on Ultrafast, Precise Synthesis and Regeneration of Advanced Battery Materials

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(15), P. 13722 - 13736

Published: July 18, 2024

Language: Английский

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Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

Polymers, Journal Year: 2024, Volume and Issue: 16(23), P. 3368 - 3368

Published: Nov. 29, 2024

The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages production, enable the analysis complex data generated throughout identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due their reliance on variable bio-based feedstocks processing conditions. This review systematically summarizes current applications techniques aiming provide a comprehensive reference for future research while highlighting potential enhance efficiency, reduce costs, improve product quality. also shows role algorithms, including supervised, unsupervised, deep

Language: Английский

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Strongly tribocatalytic dye degradation of high-entropy powder through harvesting friction

Journal of Alloys and Compounds, Journal Year: 2024, Volume and Issue: 1005, P. 176023 - 176023

Published: Aug. 14, 2024

Language: Английский

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Accelerating Discovery and Design of High-Performance Solid-State Electrolytes: A Machine Learning Approach

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Solid-state batteries (SSBs) have the potential to fulfil increasing global energy requirement, outperforming their liquid electrolyte counterparts. However, progress in SSB development is hindered by conventional approach of screening solid-state electrolytes (SSEs), which relies on human knowledge, introducing biases and requiring a time-consuming, resource-intensive trial-and-error process. As result, wide range promising Li-containing structures remain unexplored. To accelerate search for optimal SSE materials, it crucial understand chemical structural factors that govern ion transport within crystalline lattice. We utilize logistic regression-based machine learning (ML) identify quantify key physio-chemical features influencing mobility NASICON compounds. The dopant-related influence ionic conductivity are further used design doped SSEs Li-ion batteries. Our innovative results with significantly improved migration barriers conductivity, validated through density functional theory-based calculations. Specifically, this successfully identifies two high conductivity: Li2Mg0.5Ge1.5(PO4)3 Li1.667Y0.667Ge1.333(PO4)3. has lowest barrier 0.261 eV, surpassing previously best-known material, Li1.5Al0.5Ge1.5(PO4)3 (LAGP), 0.37 eV. Additionally, Li1.667Y0.667Ge1.333(PO4)3 identified second-lowest height 0.365 By focusing training model specific class our reduces time, resources, size dataset required discover novel materials targeted properties. This methodology readily adaptable various other fields, including catalysis materials.

Language: Английский

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Octafluoroadipic acid grafted Ti3CN MXene with expended interlayer spacing and absorption capability for enhanced sodium-ion storage

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115423 - 115423

Published: Jan. 18, 2025

Language: Английский

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Controlling the All-Solid Surface Reaction Between an Li1.3Al0.3Ti1.7(PO4)3 Electrolyte and Anode Through the Insertion of Ag and Al2O3 Nano-Interfacial Layers

Materials, Journal Year: 2025, Volume and Issue: 18(3), P. 609 - 609

Published: Jan. 29, 2025

Solid-state lithium batteries are considered ideal due to the safety of solid-state electrolytes. The Na superionic conductor-type Li1.3Al0.3Ti1.7(PO4)3 (LATP) is a solid electrolyte with high ionic conductivity, low cost, and stability. However, LATP reduced upon contact metallic lithium, leading dendrite growth on anode during charging. In this study, was synthesized, relationship between crystallinity conductivity investigated at different heat treatment temperatures. Optimal sintering conditions were analyzed for temperatures from 800 1000 °C. To suppress reactions Li metal, 50 nm thick Ag 10 Al2O3 layers deposited via DC sputtering plasma-enhanced atomic layer deposition. electrochemical stability tested under three conditions: uncoated LATP, Al2O3-coated Ag+Al2O3-coated LATP. improved in following order: < Ag+Al2O3-coated. coating suppressed secondary phase formation by preventing direct Li, while mitigated charge concentration, inhibiting growth. These findings demonstrate that nano-layers enhance stability, advancing battery reliability commercialization.

Language: Английский

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Dual-confinement and oxygen vacancy strategies for ultra-stable sodium storage and mechanisms in high-entropy spinel oxides

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115641 - 115641

Published: Feb. 6, 2025

Language: Английский

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Descriptors Divide‐and‐Conquer Enables Multifaceted and Interpretable Materials Structure–Activity Relationship Analysis

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Abstract Machine learning (ML) is increasingly adopted to explore the dependence of properties on descriptors especially for materials with complicated structure–activity relationships. However, most current ML modeling strategies typically depend a single combination descriptors, which leads inaccurate and unilateral inferences. Here, divide‐and‐conquer method proposed machine (descriptors‐DCML) in rough set theory (RST) integrated domain knowledge select multiple optimal sets combinations thus diverse rule extraction are provided dig out mechanisms latent data. Its potential utility applications using sodium ion energy barrier prediction NASICION‐type solid‐state electrolyte compounds multifaceted influencing factors as an example demonstrated. A total 85 samples 45 derived from 72 published literature serve data foundation modeling. Not only does descriptors‐DCML exhibit accuracy 93.8% but also extract 9 relations mapping essential Na 5 ones conform existing understanding rest waiting validation. This work paves way reducing complexity analyzing relationships enhancing interpretability models.

Language: Английский

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Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces

Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

As nations worldwide intensify their efforts to achieve environmental goals and reduce carbon emissions, as the energy landscape continues evolve, importance of advanced battery technology becomes increasingly critical. Despite significant advancements, persistent challenges at interfaces—where electrode electrolyte interactions occur—are particular concern. These interfaces play pivotal roles in phenomena such dendrite growth formation solid–electrolyte interphases (SEI), which are crucial for performance, longevity, safety batteries. Machine learning (ML), a vital subset artificial intelligence, offers robust capabilities by autonomously identifying patterns complex datasets, thereby enhancing understanding these intricate interfacial processes. This review highlights recent progress ML‐assisted simulations predictions interfaces, illustrating how ML accelerates research development trajectory. By employing algorithms machine vision, lithium growth, SEI formation, dynamics can be performed. not only deepen comprehension but also serve foundation further material optimization predication property enhancement. The aim this is spur ongoing application address existing challenges, advancing state‐of‐the‐art technologies.

Language: Английский

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Knowledge-driven eutectic electrolyte design for Zn-ion batteries

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161712 - 161712

Published: March 1, 2025

Language: Английский

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