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Record W3014647780 · doi:10.1039/d0nr00866d

A new perspective of lanthanide metal–organic frameworks: tailoring Dy-BTC nanospheres for rechargeable Li–O<sub>2</sub> batteries

2020· article· en· W3014647780 on OpenAlexaff
Dan Liŭ, Xinmin Zhang, Yan-Jie Wang, Shuyan Song, Lifeng Cui, Hongbo Fan, Xiaochang Qiao, Baizeng Fang

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersState Key Laboratory of Rare Earth Resources UtilizationNational Natural Science Foundation of China
KeywordsMaterials scienceMetal-organic frameworkLanthanideElectrochemistryNanomaterialsNanotechnologyCathodeMetalChemical engineeringElectrodeChemistryIonMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Nanoscaled lanthanide metal-organic frameworks (NLn-MOFs) have emerged as attractive nanomaterials for photofunctional applications. To enhance the inherent properties and endow NLn-MOF materials with desired electrochemical performance for rechargeable Li-O2 batteries, rational design and synthesis of NLn-MOFs with tailored morphologies for high O2 accessibility and rich open metal sites to bind O2 molecules is highly desired and remains a grand challenge. Herein, we prepare Dy-BTC nanospheres, which are explored for the first time as an O2 cathode in Li-O2 batteries. Interestingly, the specific capacity and electrochemical stability of the Dy-BTC nanosphere-based electrode outperform significantly those of the bulk crystalline Dy-BTC. A full discharge capacity of 7618 mA h g-1 at 50 mA g-1 has been achieved by the Dy-BTC nanospheres. Furthermore, the Dy-BTC nanospheres stably deliver a discharge capacity of 1000 mA h g-1 at 200 mA g-1 for 76 cycles, which is remarkably longer than that of the bulk crystalline Dy-BTC with a cycling life of 26 cycles.

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 categoriesMeta-epidemiology (narrow)
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 score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.238
Teacher spread0.221 · 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.

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

Citations36
Published2020
Admission routes1
Has abstractyes

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