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Record W4385724613 · doi:10.1002/celc.202300348

Front Cover: An Insight into Synthesis of the Antifreeze Alkaline Hydrogel Electrolyte: Fine‐Tuning Chemistries for Efficient Ion Transport (ChemElectroChem 16/2023)

2023· paratext· en· W4385724613 on OpenAlexaff
Chun Keat Khor, Chance Coady Blackstone, Anna Ignaszak

Bibliographic record

VenueChemElectroChem · 2023
Typeparatext
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsElectrolyteIonic conductivityFront coverConductivityMaterials scienceIonNanotechnologyCover (algebra)Chemical engineeringEnvironmental scienceComputer scienceElectrodeChemistryMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The Front Cover illustrates the structure of a leakage-free polymer electrolyte that sustains its ionic conductivity and mechanical resilience at extremely cold temperature. This article evaluates chemical compatibility among the hydrogel components with a goal to develop temperature-resistant electrolyte that can satisfy the application requirements in wearable electronics. The flexible and stretchable electrolyte that endures strain without being damaged and yet is operational within the user's range of motion must reliably function under varied and extreme temperature conditions, particularly in persistent cold climates. Cover design was created by Ella Maru Studio. More information can be found in the Research Article by C. K. Khor et al.

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 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 score0.081
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.222
Teacher spread0.212 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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