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Record W2025242609 · doi:10.1126/science.1257289

In situ visualization of Li/Ag <sub>2</sub> VP <sub>2</sub> O <sub>8</sub> batteries revealing rate-dependent discharge mechanism

2015· article· en· W2025242609 on OpenAlexfundno aff
Kevin Kirshenbaum, David C. Bock, Chia‐Ying Lee, Zhong Zhong, Kenneth J. Takeuchi, Amy C. Marschilok, Esther S. Takeuchi

Bibliographic record

VenueScience · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaBasic Energy SciencesAlberta Innovates - Technology FuturesCompute CanadaU.S. Department of Energy
KeywordsBattery (electricity)CathodeMetalIn situDiffractionMaterials scienceIonElectrical resistivity and conductivityTransition metalAnalytical Chemistry (journal)ConductivityChemistryPhysical chemistryMetallurgyThermodynamicsElectrical engineeringOpticsPhysics

Abstract

fetched live from OpenAlex

The functional capacity of a battery is observed to decrease, often quite dramatically, as discharge rate demands increase. These capacity losses have been attributed to limited ion access and low electrical conductivity, resulting in incomplete electrode use. A strategy to improve electronic conductivity is the design of bimetallic materials that generate a silver matrix in situ during cathode reduction. Ex situ x-ray absorption spectroscopy coupled with in situ energy-dispersive x-ray diffraction measurements on intact lithium/silver vanadium diphosphate (Li/Ag2VP2O8) electrochemical cells demonstrate that the metal center preferentially reduced and its location in the bimetallic cathode are rate-dependent, affecting cell impedance. This work illustrates that spatial imaging as a function of discharge rate can provide needed insights toward improving realizable capacity of bimetallic cathode systems.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.262
Teacher spread0.245 · 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

Citations115
Published2015
Admission routes1
Has abstractyes

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