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Record W4319969364 · doi:10.3390/coatings13020397

Antifouling Coatings Fabricated by Laser Cladding

2023· article· en· W4319969364 on OpenAlex
Shuwen Wang, Yang Chen, Chunxing Gu, Qingyi Sai, Tianyu Lei, John N. Williams

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCoatings · 2023
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsMcGill University
FundersScience and Technology Commission of Shanghai MunicipalityNatural Science Foundation of Shanghai
KeywordsBiofoulingMaterials scienceScanning electron microscopeDiffractometerCladding (metalworking)Composite materialLaserContact angleFabricationSurface energyLaser power scalingOpticsChemistry

Abstract

fetched live from OpenAlex

Laser surface treatment is a very useful technology for the fabrication of functional surfaces. In this study, novel antifouling surfaces are fabricated by laser cladding of TC4 and Ni60 mixed materials in various mass ratios on the surfaces of 316L stainless steel substrates. Parametric studies are carried out to investigate the effects of the mixed powder mass ratios and laser cladding parameters on the antifouling performance of the laser clad coatings (LCCs). The antifouling mechanism of the LCCs is investigated by using the water contact angle/surface energy measurement, scanning electron microscope (SEM) surface observation, and phase composition analysis via XRD (X-ray diffractometer) testing. The experimental results show that the LCCs with Ni60/TC4 mass ratio of 3/7 has better antifouling performance in this study. The antifouling performance of the LCC decreases with the increase in laser scanning speed. Surface energy and surface topography have a significant effect on the antifouling performance of LCCs. In order to get the optimal antifouling performance of LCCs, the Ni60/TC4 mass ratio and laser cladding parameters should be optimized.

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.

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 score0.025
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.195
Teacher spread0.188 · 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