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Record W2523636811 · doi:10.1002/celc.201600571

Nanoscale Measurements of Lithium‐Ion‐Battery Materials using Scanning Probe Techniques

2016· article· en· W2523636811 on OpenAlexafffund
Laurence Danis, Samantha Michelle Gateman, Christian Kuß, Steen B. Schougaard, Janine Mauzeroll

Bibliographic record

VenueChemElectroChem · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversité du Québec à MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanotechnologyLithium (medication)Nanoscopic scaleMaterials scienceScanning probe microscopyBattery (electricity)Energy storageLithium-ion batteryIonEngineering physicsChemistryEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

Abstract State‐of‐the‐art scanning probe microscopy (SPM) methods as applied to energy conversion and storage devices, specifically lithium‐ion batteries, are reviewed with an emphasis on the electroactive elements. The unique ability of SPM‐based methods to provide localized information has proven highly valuable for the in‐depth understanding of the fundamental mechanisms, processes, and degradation of lithium‐ion batteries (LIBs). As such, SPM analysis is poised to play a strong role in the competition for new higher performing LIBs, especially given the unprecedented choice and availability of SPM techniques tailored to provide physical and chemical information at the nanoscale.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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

Citations65
Published2016
Admission routes2
Has abstractyes

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