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Record W2982675511 · doi:10.1115/icnmm2019-4209

Fabrication and Corrosion Performance of a Superhydrophobic Stainless Steel Surface

2019· article· en· W2982675511 on OpenAlexaff
Mona Amiriafshar, Xili Duan, Ali Nasiri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMaterials scienceWettingContact angleSuperhydrophobic coatingSurface roughnessCoatingCorrosionFabricationComposite materialSurface finishSurface energyAdhesionMetalNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

Abstract Fabrication of surfaces with hydrophobic and superhydrophobic property has drawn extensive interests as a solution to protect metal surfaces from corrosion attacks, with potential applications in cooling devices for electronics, microfluidic systems for controlled drug delivery, as well as anti-icing, and self-cleaning techniques. This study addresses the impact of surface wettability, i.e., hydrophobicity and superhydrophobicity, on corrosion resistance improvement of metal materials. Hydrophobic and superhydrophobic metal surfaces are desirable to minimize the adhesion between water droplets and the surface. This study aims to fabricate and investigate 17-4 PH stainless steel surfaces with lowered surface energies and modified wetting properties. Various micro- and sub-micro scale finished surfaces with different surface roughness, namely as-received, sandblasted, ground, and polished, were employed, followed by applying a low energy superhydrophobic coating to fabricate hydrophobic and superhydrophobic surfaces on 17-4 PH stainless steel base material. The specific impacts of the surface roughness on wettability and corrosion resistivity of the manufactured surfaces were examined. The ground and polished surfaces followed by applying a 30–50 μm thick superhydrophobic dip coating resulted in steady-state contact angles of up to 152° and 146°, respectively, while the non-engineered coated base metal exhibited the contact angle of 140°. The ground surface with the average surface roughness (Ra) of ∼ 0.03 μm has the optimal roughness. According to the Cassie-Baxter model, the coated ground surface can retain the entrapped air within its interstices more adequately than the other surfaces with either lower surface roughness, such as the polished surface with the roughness of 0.02 μm, or higher surface roughness, such as as-received and sandblasted surfaces with the Ra value of 5.52 μm and 11.98 μm, respectively. To study the corrosion performance and electrochemical stability of the fabricated surfaces, cyclic polarization testing (CPT) and electrochemical impedance spectroscopy (EIS) were performed in an aerated 3.5 wt.% NaCl solution that mimics seawater environment. The electrochemical measurements confirmed that the water-repelling property of the surface contributes to the anti-corrosion capability of the substrate. Data from the corrosion tests indicate that the lowest corrosion current density, highest corrosion potential, and highest pitting potential, were found for the coated ground surface followed by the coated polished surface. The EIS results also highlighted the significantly greater absolute value of impedance for the coated ground and coated polished surfaces even after 240 hrs of immersion in the electrolyte solution than the other fabricated surfaces at lower frequency ranges. The improvement in the 17-4 PH stainless steel corrosion performance was contributed to the size of the fabricated surface micro- and sub-micro scale features, capable of retaining the entrapped air within the roughened surface structure when fully immersed in a corrosive environment. This work demonstrates the effectiveness of a simple fabrication process to create hydrophobic and superhydrophobic stainless steel surfaces with improved corrosion resistivity.

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 categoriesInsufficient 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 score0.036
Threshold uncertainty score0.999

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)0.0010.000

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 head0.222
Teacher spread0.210 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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