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Record W1966012775 · doi:10.1002/adfm.201302122

Electrochemical Properties of Si‐Ge Heterostructures as an Anode Material for Lithium Ion Batteries

2013· article· en· W1966012775 on OpenAlexaff
Taeseup Song, Huanyu Cheng, Kaitlin Town, Hyunjung Park, Robert W. Black, Sangkyu Lee, Won Il Park, Yonggang Huang, John A. Rogers, Linda F. Nazar, Ungyu Paik

Bibliographic record

VenueAdvanced Functional Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceNanowireAnodeElectrochemistryLithium (medication)HeterojunctionThermal diffusivityElectrochemical kineticsIonDiffusionGermaniumElectrodeNanotechnologyKineticsComposite materialSiliconOptoelectronicsThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

Si‐Ge composites have recently been explored as an anode material for lithium‐ion batteries due to their stable cycle performance and excellent rate capability. Although previous reports show the benefits of Si‐Ge composites on electrochemical performance, the specific mechanism and structural effects have been overlooked. Here, the structural effect of Si‐Ge heterogeneous nanostructures on both mechanics and kinetics is systematically studied through theoretical analysis and detailed experimental results. Si‐Ge and Ge‐Si core–shell nanowires are employed for this study. The Si‐Ge core–shell nanowires show a much improved electrochemical performance, especially cycle performance and rate capability, when compared to those of the Ge‐Si core–shell nanowires electrode. On the basis of the detailed experimental results and associated theoretical analysis, its is demonstrated that the strain distribution and Li diffusivity and/or diffusion path are significantly affected by the Si‐Ge heterostructure, which induce different mechanics and kinetics associated with lithium.

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 score0.007
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.220
Teacher spread0.209 · 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

Citations87
Published2013
Admission routes1
Has abstractyes

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