MétaCan
Menu
Back to cohort
Record W4285398972 · doi:10.1149/ma2022-011106mtgabs

Microcantilever: An Unique Apparatus to Revolve the Mechanical Stress in Batteries

2022· article· en· W4285398972 on OpenAlexaff
Keren Jiang, Thomas Thundat, Zhi Li

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnodeStress (linguistics)Materials scienceElectrodeBattery (electricity)Compressive strengthDegradation (telecommunications)VoltagePlateau (mathematics)Composite materialOptoelectronicsElectrical engineeringChemistryPower (physics)

Abstract

fetched live from OpenAlex

Although the generation of mechanical stress in the anode material is suggested as a possible reason for electrode degradation and fading of storage capacity in batteries, only limited knowledge of the electrode stress and its evolution is available at present. Here, we show real-time monitoring of the interfacial stress of a few-layer MoS 2 system under the sodiation/desodiation process using microcantilever electrodes. During the first sodiation with a voltage plateau of 1.0 to 0.85 V, the MoS 2 exhibits a compressive stress (2.1 Nm −1 ), which is substantially smaller than that measured (9.8 Nm −1 ) during subsequent plateaus at 0.85 to 0.4 V due to the differential volume expansion of the MoS 2 film. The conversion reaction to Mo below 0.1 V generates an anomalous compressive stress of 43 Nm −1 with detrimental effects. These results also suggest the existence of a separate discharge stage between 0.6 and 0.1 V, where the generated stress is only approximately one-third of that observed below 0.1 V. This approach can be adapted to help resolve the localized stress in a wide range of electrode materials, to gain additional insights into mechanical effects of charge storage, and for long-lifetime battery design. Figure 1

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.425

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)0.0000.000

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 head0.012
GPT teacher head0.229
Teacher spread0.216 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicNanowire Synthesis and ApplicationsFrench-language works237,207