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Record W2925086823 · doi:10.5006/c2018-11526

Investigation of Corrosion Properties of a High-phosphorus Ni-P Coating and Corrosion Resistant Alloys in 3.5 Wt.% NaCl Solution

2018· article· en· W2925086823 on OpenAlexaff
Chong Sun, Vahidoddin Fattahpour, Shuo Shuang, Mahdi Mahmoudi, Hongbo Zeng, Jing‐Li Luo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCorrosionMaterials scienceMetallurgyCoatingPhosphorusComposite material

Abstract

fetched live from OpenAlex

Abstract In this study, a high-phosphorus Ni-P coating was prepared by electroless deposition method. The characteristics and corrosion property of electroless Ni-P coating were investigated by surface analysis techniques and electrochemical measurements, and compared with L80 steel, corrosion resistant alloys such as 13Cr steel, 316L stainless steel, Inconel and 28Cr steel. The results showed that the high- phosphorus Ni-P coating with amorphous structure contained 88.3 wt.% Ni and 11.7 wt.% P, and improved the corrosion resistance of L80 steel substrate by more than 90 % in Cl-containing medium. The corrosion resistance of Ni-P coating was close to 13Cr steel but lower than Inconel, 316L stainless steel and 28Crsteel. Nevertheless Ni-P coating, similarto Inconel and 28Crsteel, had better resistance to pitting corrosion than 13Cr steel and 316L stainless steel in Cl-containing environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.033
GPT teacher head0.237
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes1
Has abstractyes

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