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Record W4404383110 · doi:10.1021/acs.nanolett.4c04414

Metal-Hydroxide Organic Frameworks for Aqueous Nickel-Zinc Batteries

2024· article· en· W4404383110 on OpenAlexaff
Shixian Wang, Yichun Su, Zhaocheng Jiang, Zhenyang Meng, Tianyi Wang, Meifang Yang, Weijie Zhao, Han‐Yi Chen, Mohsen Shakouri, Huan Pang

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsNickelZincHydroxideAqueous solutionZinc hydroxideInorganic chemistryMetalMaterials scienceMetal-organic frameworkMetal hydroxideChemistryMetallurgyOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Hydroxides exhibit a high theoretical capacity for energy storage by ion release and are often intercalated with anions to enhance the ion migration kinetics. In this study, a series of metal-hydroxide organic frameworks (MHOFs) are synthesized by intercalating aromatic organic linkers into hydroxides using I-M/Ni(OH) 2 (where M = Co 2+, Cu 2+, Mg 2+, Fe 2+ ). The coordination environment and layer spacing (1.09 nm) of I-M/Ni(OH) 2 are explored by X-ray absorption fine structure and cryo-electron microscopy. The intercalation nanostructure improves the conductivity of the hydroxides and facilitates Zn 2+ migration by increasing the interlayer spacing, while enhancing the rate capability and cycling stability. Consequently, the I-Co/Ni(OH) 2 material exhibites a satisfactory specific capacity of 0.35 mAh cm –2 at 3 mA cm –2 and a high peak power density of 6.78 mW cm –2 . This study offers a novel perspective on the design of intercalated hydroxide and provides new insights into high-performance nickel-zinc batteres.

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 score0.429
Threshold uncertainty score0.975

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 integrity0.0000.001
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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