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Record W4405892017 · doi:10.1039/d4ee04847d

Designing a bridging solvation structure using recessive solvents for high energy density aqueous zinc-ion batteries with 88% depth of discharge zinc rechargeability

2024· article· en· W4405892017 on OpenAlexaff
Qingying Li, Dan Luo, Qianyi Ma, Zhuoyi Zheng, Shibin Li, Yihan Xie, Linjiang Xue, Yihang Nie, Haozhen Dou, Jiawen Chen, Xin Wang, Zhongwei Chen

Bibliographic record

VenueEnergy & Environmental Science · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceVacuum Interconnected Nanotech Workstation, Suzhou Institute of Nano-Tech and Nano-BionicsNational Natural Science Foundation of China
KeywordsSolvationZincBridging (networking)Aqueous solutionMaterials scienceEnergy densityIonBattery (electricity)Inorganic chemistryChemistryPhysical chemistryMetallurgyOrganic chemistryEngineering physicsComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

The recessive solvent is activated by bridging structure, and further self-assemble into nano-capsule solvation, which promotes rapid Zn deposition and generates a uniform SEI, thus achieving a stable Zn anode at high depth of discharge.

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 score0.426
Threshold uncertainty score0.872

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 studies0.0000.001
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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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

Citations65
Published2024
Admission routes1
Has abstractyes

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