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Record W3008912619 · doi:10.1038/s41598-020-59420-6

Influence of UV light irradiation on the corrosion behavior of electrodeposited Ni and Cu nanocrystalline foils

2020· article· en· W3008912619 on OpenAlexafffund
Shu-hao Deng, Hao Lü, D. Y. Li

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaShell CanadaChina Scholarship CouncilSuncor Energy Incorporated
KeywordsNanocrystalline materialMaterials scienceX-ray photoelectron spectroscopyCorrosionIrradiationFOIL methodOxideUltraviolet lightNon-blocking I/OUltravioletWork functionChemical engineeringMetallurgyComposite materialNanotechnologyMetalChemistryOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Influence of ultraviolet (UV) light irradiation on the corrosion behavior of electrodeposited Ni and Cu nanocrystalline foils in 3.5% NaCl solution was studied by means of electrochemical methods, electron work function (EWF) analysis, and characterization with atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). It was demonstrated that the influence of solar light on corrosion of the metals was non-negligible, which could be very different for different materials. The UV light irradiation resulted in an increase in corrosion resistance of the Cu foil but showed an opposite influence on that of the Ni foil. Based on surface state analysis, it was concluded that the UV irradiation altered the surface oxide films. The UV light induced the formation of Cu 2 O on Cu, which is more stable and compacted than naturally formed CuO film. However, the UV light accelerated the formation of Ni 2 O 3, which is loose, porous and brittle, compared to naturally formed NiO on Ni. The changes in oxide films were responsible for the opposite variations in the corrosion behavior of the Cu and Ni nanocrystalline foils caused by the UV light irradiation.

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 score0.004
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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