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Record W4225124349 · doi:10.1002/cey2.193

A novel design of 3D carbon host for stable lithium metal anode

2022· article· en· W4225124349 on OpenAlexaff
Hang Liu, Jie Di, Ping Wang, Rui Gao, Han Tian, Pengfei Ren, Qingxi Yuan, Wanxia Huang, Ruiping Liu, Qiang Liu, Ming Feng

Bibliographic record

VenueCarbon Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesPeople's Government of Jilin ProvinceNational Natural Science Foundation of ChinaJilin Province Development and Reform Commission
KeywordsFaraday efficiencyAnodeMaterials scienceLithium (medication)Electrical conductorCarbon fibersPorosityLithium metalHost (biology)NanotechnologyChemical engineeringMetalComposite materialChemistryElectrodeMetallurgy

Abstract

fetched live from OpenAlex

Abstract Rational design of porous conductive hosts with high electrical conductivity, large surface area, and adequate interior space is desirable to suppressing dendritic lithium growth and accommodating large volume change of lithium metal anode during the Li plating/stripping process. However, due to the conductive nature of the conductive hosts, Li is easily deposited directly on the top of the hosts, which hinders it from fully functioning. To circumvent the issue, in this study, we designed a novel porous carbon host with a gradient‐pore‐size structure based on one‐dimensional (1D) carbon with different diameters. With this kind of host, stable cycling with high and stable Coulombic efficiency of ~98% is achieved at 0.5 mA cm−2 with an areal capacity of 1 mAh cm−2 over 320 cycles. In contrast, the normal three‐dimensional (3D) carbon nanotube host presents a moss‐like Li morphology with wildly fluctuating Coulombic efficiency after 100 cycles. The results reveal that the unique gradient‐pore‐size structure of the 3D conductive host greatly improves the performance of lithium metal 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 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

Citations84
Published2022
Admission routes1
Has abstractyes

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