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Record W4206030393 · doi:10.1002/celc.202101503

Cation‐/Anion‐Based Physicochemical Mechanisms for Anodically Coloring Electrochromic Nickel Oxide Thin Films

2022· article· en· W4206030393 on OpenAlexaff
Hui‐Ying Qu, Xiyang Wang, Ding Chen, Zhihao Bai, Nannan Wang, Yanqiu Zhu, Zhangfa Tong, Hongbing Ji, Gunnar A. Niklasson

Bibliographic record

VenueChemElectroChem · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversity of Waterloo
FundersNational Synchrotron Radiation LaboratoryNatural Science Foundation of Guangxi Province
KeywordsElectrochromismNickel oxideX-ray photoelectron spectroscopyNickelMaterials scienceThin filmOxideElectrolyteInorganic chemistryElectrochemistryChemical engineeringNanotechnologyChemistryElectrodePhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract The rapidly expanding field of intelligent ion‐based devices has increased interest in the use of anodically‐coloring electrochromic nickel oxide thin films. The degradation and coloration mechanisms of nickel oxide, especially in Li + ‐based electrolytes, are yet to be well understood. Herein we demonstrate that high potentials have a positive effect on the electrochromic performance of nickel oxide thin films. Our studies show that Cl − ions involved in the electrochromic process have been accumulated on the surface of the films upon extended electrochemical cycling, as confirmed by the X‐ray Photoelectron Spectroscopy. X‐ray Absorption Spectroscopy results indicate that the formation of Ni−Cl bonds influence the structural distortion and that the hybridization between Ni 3d and O 2p orbitals has been enhanced. Density functional theory calculations provide further insights for the band structures and how they change when Li + and Cl − are adsorbed. Our results have revealed the underlying physical and chemical origins associated with the coloration mechanism and the degradation of nickel oxide thin films and highlighted the key role of Cl − . These new understandings will advance the development of superior electrochromic materials and the designing of efficient and durable electrochromic devices, both experimentally and theoretically.

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), Insufficient payload (model declined to judge)
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 score0.013
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.237
Teacher spread0.225 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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