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Record W4318832853 · doi:10.1126/sciadv.adf2398

Covalent organic frameworks with Ni-Bis(dithiolene) and Co-porphyrin units as bifunctional catalysts for Li-O <sub>2</sub> batteries

2023· article· en· W4318832853 on OpenAlexaff
Si‐Wen Ke, Wei Li, Yuming Gu, Jian Su, Yifan Liu, Shuai Yuan, Jing‐Lin Zuo, Jing Ma, Ping He

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of China
KeywordsBifunctionalBimetallic stripCatalysisMaterials scienceCathodeRational designChemical engineeringPorphyrinBattery (electricity)Density functional theoryIsostructuralBifunctional catalystNanotechnologyChemistryPhotochemistryPhysical chemistryOrganic chemistryComputational chemistry

Abstract

fetched live from OpenAlex

The rational design of efficient and stable catalysts for the oxygen reduction reaction and oxygen evolution reaction (ORR/OER) is the key to improving Li-O 2 battery performance. Here, we report the construction of ORR/OER bifunctional cathode catalysts in a covalent organic framework (COF) platform by simultaneously incorporating Ni-bis(dithiolene) and Co-porphyrin units. The resulting bimetallic Ni/Co-COF exhibits high surface area, fairly good electrical conductivity, and excellent chemical stability. Li-O 2 batteries with the Ni/Co-COF–based cathode show a low discharge/charge potential gap (1.0 V) and stable cycling (200 cycles) at a current density of 500 mA g −1 , rivaling that of PtAu nanocrystals. Density functional theory computations and control experiments using nonmetal or single metal–based isostructural COFs reveal the critical role of Ni and Co sites in reducing the discharge/charge overpotentials and regulating the Li 2 O 2 deposition. This work highlights the advantage of bimetallic COFs in the rational design of efficient and stable Li-O 2 batteries.

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 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 score0.030
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.236
Teacher spread0.225 · 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 teacher head, 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

Citations90
Published2023
Admission routes1
Has abstractyes

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