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Record W2887879599 · doi:10.1039/c8nr04655g

Reducing lithium deposition overpotential with silver nanocrystals anchored on graphene aerogel

2018· article· en· W2887879599 on OpenAlexaff
Xianshu Wang, Zhenghui Pan, Yang Wu, Guoguang Xu, Xiongwen Zheng, Yongcai Qiu, Meinan Liu, Yuegang Zhang, Weishan Li

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsL'Alliance Boviteq
FundersGuangdong Science and Technology Department
KeywordsAerogelOverpotentialGrapheneMaterials scienceNanocrystalLithium (medication)Deposition (geology)NanotechnologyChemical engineeringChemistryElectrochemistryElectrode

Abstract

fetched live from OpenAlex

Li metal as an anode for high-energy-density batteries is actively pursued due to its high specific capacity and ultralow electrochemical potential. Unfortunately, Li dendrite growth might induce a short circuit creating safety hazards that limit the practical applications of Li metal anode batteries. Herein, a novel anode of graphene aerogel (GA) decorated with silver nanocrystals (AgNCs@GA) is reported for effective suppression of lithium dendrite growth and improvement in coulombic efficiency at various current densities. This improved performance is attributed to AgNCs. This loaded AgNCs with high Li affinity serve as Li deposition sites, which deeply reduce the overpotential of Li nucleation and electrodeposition. Therefore, it successfully realizes stable Li deposition/stripping processes with enhanced coulombic efficiency at various current densities and areal capacities. The pre-lithiated AgNCs@GA is evaluated as an anode in a Li battery and demonstrates remarkable performance in comparison with a commercial lithium foil.

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 score0.011
Threshold uncertainty score0.782

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations53
Published2018
Admission routes1
Has abstractyes

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