MétaCan
Menu
Back to cohort
Record W3134327736 · doi:10.1149/1945-7111/abe16a

Isothermal Calorimetry Evaluation of Metallurgical Silicon as a Negative Electrode Material for Li-Ion Batteries

2021· article· en· W3134327736 on OpenAlexafffund
Vincent Chevrier, Zilai Yan, Stephen Glazier, M. N. Obrovac, L. J. Krause

Bibliographic record

VenueJournal of The Electrochemical Society · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCalorimetryEnthalpyMaterials scienceIsothermal processCalorimeter (particle physics)Isothermal titration calorimetrySiliconThermodynamicsIonElectrodeParasitic extractionStandard enthalpy of formationAnalytical Chemistry (journal)MetallurgyChemical engineeringChemistryPhysical chemistryOptoelectronicsElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The structural evolution of Si during lithiation and delithiation is uniquely dependent on the cycling conditions and can show either reversible or path dependent behavior. In this paper, metallurgical Si (large crystalline particles of pure Si) is cycled to exhibit both reversible and path dependent cycling while in-operando calorimetry is performed with a high precision isothermal calorimeter. The enthalpy potential and waste heat are studied in both the reversible and path dependent regimes. The enthalpy of crystallization of Li 15 Si 4 is quantified to be 21 kJ mol Si −1 . The parasitics (heat of side reactions) of pure Si are studied, showing a stronger current than time dependence. The voltage dependence of the parasitic power is quantified, showing increasing parasitics below 170 mV. The results of this in depth calorimetric study of metallurgical Si provides valuable insights into the heat production and energy efficiency of Si as a negative electrode material in Li-ion batteries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicAdvancements in Battery MaterialsFrench-language works237,207