MétaCan
Menu
Back to cohort
Record W4405644536 · doi:10.1021/acsaem.4c02248

To Be or Not to Be: Dendrite Growth Mechanism Adjacent to Pits for High Stability of Lithium Metal Anode

2024· article· en· W4405644536 on OpenAlexaff
Li Gao, Jiang Bin, Qianyi Ma, Zhansheng Guo

Bibliographic record

VenueACS Applied Energy Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsAnodeDendrite (mathematics)Lithium metalLithium (medication)Materials scienceMetalMechanism (biology)MetallurgyChemistryGeometryMathematicsPhysicsElectrodeMedicineInternal medicine

Abstract

fetched live from OpenAlex

The challenge of dendrite growth is a formidable obstacle that leads to short circuits when charging lithium (Li)-metal batteries. Implementing patterns with various pit structures on Li metal proves to be an effective strategy to alleviate dendrite growth. Nonetheless, the growth mechanism of Li dendrites near pits is still under debate, making it difficult to accurately propose patterns to enhance the stability of the Li metal. In this study, the growth mechanism of dendrites near the pits is successfully clarified for the first time using the in situ optical microscopy method and electrochemical modeling. A direct correlation is established between the formation of Li dendrites and factors. Areas with heightened electric field strength and localized current density are more susceptible to dendrite formation. Increased local surface roughness promotes dendrite nucleation and growth. Dendrite growth at the edge of pits leads to an insufficient supply of Li ions in the pit, thereby inhibiting dendrite formation inside the pit. By optimization of the pattern structure, the density of Li deposition can be significantly improved. The results provide a promising and practical approach to ensure safety and extend the lifetime of Li 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 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)0.001
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 score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.256
Teacher spread0.230 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueACS Applied Energy MaterialsSame topicAdvancements in Battery MaterialsFrench-language works237,207