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Record W4221111381 · doi:10.1039/d1cs01014j

Porous organic polymers for Li-chemistry-based batteries: functionalities and characterization studies

2022· review· en· W4221111381 on OpenAlexafffund
Dan Luo, Matthew Li, Qianyi Ma, Guobin Wen, Haozhen Dou, Bohua Ren, Yizhou Liu, Xin Wang, Lingling Shui, Zhongwei Chen

Bibliographic record

VenueChemical Society Reviews · 2022
Typereview
Languageen
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsUniversity of Waterloo
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceGuangdong Science and Technology DepartmentNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Guangdong ProvinceChina Postdoctoral Science Foundation
KeywordsPolymerMicroporous materialCharacterization (materials science)NanotechnologyIonic bondingPorosityChemistryElectrochemistryMaterials scienceConjugated microporous polymerChemical engineeringOrganic chemistryElectrodeIonPhysical chemistry

Abstract

fetched live from OpenAlex

characterization studies are also discussed to highlight their importance and applicability for the structural investigation of POPs to reveal the underlying mechanism of POPs over the course of the electrochemical process. Although some revolutionary advances have been achieved, the development of POPs in Li-chemistry-based batteries is still in its infancy. Perspectives regarding future application and mechanistic insights of POPs in battery studies are outlined at the end.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.097
GPT teacher head0.339
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations176
Published2022
Admission routes2
Has abstractyes

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