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Record W4317387800 · doi:10.1002/aenm.202203307

Lithium Batteries and the Solid Electrolyte Interphase (SEI)—Progress and Outlook

2023· article· en· W4317387800 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

VenueAdvanced Energy Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcMaster UniversityUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeHelmholtz AssociationHong Kong GovernmentUniversity of British ColumbiaSapienza Università di RomaScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsElectrolyteAnodeMaterials scienceLithium (medication)ElectrochemistryNanotechnologyElectrodeChemical engineeringChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Interfacial dynamics within chemical systems such as electron and ion transport processes have relevance in the rational optimization of electrochemical energy storage materials and devices. Evolving the understanding of fundamental electrochemistry at interfaces would also help in the understanding of relevant phenomena in biological, microbial, pharmaceutical, electronic, and photonic systems. In lithium‐ion batteries, the electrochemical instability of the electrolyte and its ensuing reactive decomposition proceeds at the anode surface within the Helmholtz double layer resulting in a buildup of the reductive products, forming the solid electrolyte interphase (SEI). This review summarizes relevant aspects of the SEI including formation, composition, dynamic structure, and reaction mechanisms, focusing primarily on the graphite anode with insights into the lithium metal anode. Furthermore, the influence of the electrolyte and electrode materials on SEI structure and properties is discussed. An update is also presented on state‐of‐the‐art approaches to quantitatively characterize the structure and changing properties of the SEI. Lastly, a framework evaluating the standing problems and future research directions including feasible computational, machine learning, and experimental approaches are outlined.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.988

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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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