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Record W4390982969 · doi:10.1002/ente.202301268

Design, Synthesis, and Characterization of Carbon‐Supported β‐Ni(OH)<sub>2</sub> Nanosheets for Miniaturized Nickel–Metal Hydride Batteries

2024· article· en· W4390982969 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergy Technology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsQueen's University
KeywordsNanomaterialsMaterials scienceHydrideNickelCyclic voltammetryChemical engineeringNanotechnologyMetalCarbon fibersElectrochemistryInorganic chemistryElectrodeComposite numberChemistryComposite materialPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Carbon‐supported β‐Ni(OH) 2 nanosheets are prepared for miniaturized nickel–metal hydride batteries. The nanomaterial consists of thin and unfolded nanosheets, which possess a hexagonal crystallographic structure. Its unique structure gives rise to a remarkable specific capability, with 81.5% of the nanomaterial being used in electrical energy storage. Repetitive charge–discharge cycling between charge and discharge cut‐off potentials ( E cut‐off,ch = 1.50 or 1.65 V and E cut‐off,dis = 1.20 V) in aqueous NaOH solution is performed to examine the nanomaterial's specific charge and stability. The results show that the specific capacity gradually decreases upon repetitive charge–discharge cycling but still maintains a significant percentage of its initial value, 54% for E cut‐off,ch = 1.50 V and 43% for E cut‐off,ch = 1.65 V. A transmission electron microscopy analysis of the nanomaterial after the charge–discharge cycling demonstrates that the β‐Ni(OH) 2 nanosheets are less‐organized and distorted. The structural and morphological changes give rise to new features in cyclic voltammetry profiles and charge–discharge curves, and a decrease in the specific capacity of the nanomaterial. The degradation of the specific capacity is attributed to a loss of the initial structure, a reduction of the number of anchoring sites, and weakening of the bond between the β‐Ni(OH) 2 nanosheets and the carbon support.

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.

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 score0.007
Threshold uncertainty score0.756

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 head0.010
GPT teacher head0.205
Teacher spread0.196 · 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