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Record W4386130627 · doi:10.1016/j.est.2023.108803

Progress in diamond-like carbon coatings for lithium-based batteries

2023· article· en· W4386130627 on OpenAlex
Abdul Wasy Zia, Syed Asad Hussain, Shahid Rasul, Dowon Bae, Sudhagar Pitchaimuthu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Energy Storage · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsDiamond-like carbonMaterials scienceCoatingLithium (medication)Carbon fibersMicrostructureDeposition (geology)DiamondComposite materialNanotechnologyEngineering physicsComposite numberThin filmEngineering

Abstract

fetched live from OpenAlex

NetZero drive is exploring new energy solutions around the globe. Carbon-based electrodes are receiving wider attention for energy storage applications. This work reviews the application of diamond-like carbon (DLC) coatings for lithium-based batteries (LBB). DLC atomic structure, the mechanisms at atomistic and microstructure levels, and the manufacturing of DLC coatings for LBB with plasma methods are explained. This work also describes the effects of DLC coating thickness, deposition temperature, coating architecture by layers, and doping on the performance of LBB. The application of DLC is reported to increase retention capacity by 40 % and cycle life by 400 % for LBB. This work emphasizes the full spectrum experimental study of process-structure-property blended with material informatics to develop high-performance DLC-based electrodes for LBB.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.531

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 head0.013
GPT teacher head0.250
Teacher spread0.236 · 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