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An experimental study of the robustness of electrochemical impedance spectroscopy measurements within consecutive cycles

2024· article· en· W4400946042 on OpenAlexaff
Wenlin Zhang, Ryan Ahmed, Saeid Habibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDielectric spectroscopyRobustness (evolution)Electrical impedanceMaterials scienceSpectroscopyAnalytical Chemistry (journal)ElectrochemistryOptoelectronicsElectronic engineeringElectrodeElectrical engineeringChemistryPhysicsEngineeringChromatography

Abstract

fetched live from OpenAlex

Diffusion of lithium ions into the electrodes (relaxation effect) was shown to affect the electrochemical impedance spectroscopy (EIS) measurements hours after the load is removed. Previous research primarily examined the influence of rest time following individual charge or discharge cycles while the robustness of the measurements remained mostly unexplored. This study experimentally examined the effects of rest periods between successive cycles. For the first time to the authors’ knowledge, it demonstrated that the repeatability of EIS measurements can be significantly affected by rest periods in between cycles and the SOC level. Additionally, it was discovered that repeatable impedance measurements can be obtained after a very short relaxation period of 15 minutes, supporting the reduction of relaxation period. While additional investigations are required to understand some of the observations and their implications on onboard SOH estimation strategies, this study serves to experimentally demonstrate that the impedance dispersion between successive cycles can be significant and should not be overlooked.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.297
Teacher spread0.272 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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