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Record W3177853406 · doi:10.18280/acsm.450305

Morphology and Corrosion Behavior of Zn-Ni Layers Electrodeposited on Low Alloy Carbon Steel Substrate

2021· article· en· W3177853406 on OpenAlexvenueno aff
Fateh Chouia, Abdelouahad Chala, Lakel Abdelghani, Toufik Sahraoui

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsnot available
Fundersnot available
KeywordsMicrostructureMaterials scienceAlloyElectrolyteCorrosionMetallurgyCarbon steelSubstrate (aquarium)Scanning electron microscopePhase (matter)Morphology (biology)NickelChemical engineeringComposite materialChemistryElectrode

Abstract

fetched live from OpenAlex

The aim of this work is to improve the microstructure, the morphology, the mechanical and the corrosion behavior of Zn-Ni layers electrodeposited on low alloy carbon steel. Some factors such as the nickel concentration in the electrolyte, the electrolyte temperature and the current density are studied and optimized. The efficiency of the layers to protect the low alloy carbon steel against corrosion in HCl solution is also studied in this work. The electrodeposited Zn-Ni layers morphologies and microstructure were investigated by Scanning Electronic Microscopy (SEM) and X-ray diffraction (XRD) respectively. The best deposits of Zn-Ni layers are obtained with 50%-Ni in the electrolyte bath at a temperature of 40°C and a current density of j = 3 A dm-2. The XRD spectrum showed the coexistence of two phases: δ-phase (Ni3Zn22) and γ-phase (Ni5Zn21), and a pyramidal morphology is detected by SEM. The lost mass method results showed that the corrosion rate tacked a steady state between 10 and 50% of Ni, and it increased with the increase of Ni amount in the electrolyte bath.

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 score0.048
Threshold uncertainty score0.796

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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