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Record W3124686147 · doi:10.1021/acsami.0c21455

Reversible Silicon Anodes with Long Cycles by Multifunctional Volumetric Buffer Layers

2021· article· en· W3124686147 on OpenAlexafffund
Tiansheng Mu, Shuaifeng Lou, Nathaniel Holmes, Changhong Wang, Mengxue He, Baicheng Shen, Xiaoting Lin, Pengjian Zuo, Yulin Ma, Ruying Li, Chunyu Du, Jiajun Wang, Geping Yin, Xueliang Sun

Bibliographic record

VenueACS Applied Materials & Interfaces · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaChina Scholarship CouncilCanada Foundation for Innovation
KeywordsAnodeMaterials scienceSiliconLithium (medication)Carbon fibersElectrochemistryNanotechnologyChemical engineeringBattery (electricity)Energy storageComposite numberElectrodeOptoelectronicsComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

Establishing a stable, stress-relieving configuration is imperative to achieve a reversible silicon anode for high energy density lithium-ion batteries. Herein, we propose a silicon composite anode (denoted as T-Si@C), which integrates free space and mixed carbon shells doped with rigid TiO2/Ti5Si3 nanoparticles. In this configuration, the free space accommodates the silicon volume fluctuation during battery operation. The carbon shells with embedded TiO2/Ti5Si3 nanoparticles maintain the structural stability of the anode while accelerating the lithium-ion diffusion kinetics and mitigating interfacial side reactions. Based on these advantages, T-Si@C anodes demonstrate an outstanding lithium storage performance with impressive long-term cycling reversibility and good rate capability. Additionally, T-Si@C//LiFePO4 full cells show superior electrochemical reversibility. This work highlights the importance of rational structural manipulation of silicon anodes and affords fresh insights into achieving advanced silicon anodes with long life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.216
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2021
Admission routes2
Has abstractyes

Explore more

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