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The critical role of interfaces in advanced Li-ion battery technology: A comprehensive review

2024· review· en· W4402629052 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Power Sources · 2024
Typereview
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsConcordia University
FundersConcordia University
KeywordsBattery (electricity)Materials scienceComputer scienceEngineeringPhysicsThermodynamicsPower (physics)

Abstract

fetched live from OpenAlex

The passivation layer in lithium-ion batteries (LIBs), commonly known as the Solid Electrolyte Interphase (SEI) layer, is crucial for their functionality and longevity. This layer forms on the anode during initial charging to avoid ongoing electrolyte decomposition and stabilize the anode-electrolyte interface. However, repeated charging and discharging can destabilize the SEI, thereby increasing internal resistance and reducing capacity. Similarly, the Cathode Electrolyte Interphase (CEI) layer is crucial for the performance, safety, and durability of LIBs. The formation, stability, and evolution of the SEI and CEI layers are critical for the efficiency and lifespan of LIBs. Research has focused on optimizing these interphases through advanced materials , interface modifications, and improved electrolyte formulations. Enhancing our understanding of SEI and CEI formation and degradation can lead to significant advancements in LIB performance, lifespan, and safety, thereby satisfying the demands for high-performance power storage in electronic appliances and electric vehicles. Future innovations promise to improve energy density , charge-discharge efficiency, and overall battery reliability.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.333
Teacher spread0.316 · 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