New Highly Microbial-Induced Corrosion-Resistant Ni-P-Based Coating with Superior Mechanical Properties.
Bibliographic record
Abstract
Microbiologically Influenced Corrosion (MIC) has been recognized as a widespread problem in the oil and gas industries because it causes substantial economic losses. Sulfate reducing bacteria (SRB) is one of the essential microorganisms families that have always been linked to MIC mechanisms causing localized corrosion problems. Since the bacterial adhesion on the metal surfaces is a prerequisite condition for the biofilm formation, controlling, and prevention of the colonization/proliferation of the microbial biofilms is of the critical impact regarding the MIC prevention and ensuring the public safety. Titanium-nickel shape memory alloy (TiNi SMA) nanoparticles have several marvelous features, ranging from the physical and chemical properties to the biological performance. Our electroless-plated NiP-TiNi nanocomposite coating (NiP-TiNi NCC) is used to determine the suitability of using it as an inhibition system for SRB on carbon steel (CS). The influence of anaerobic bacteria SRB on the corrosion behavior of API X80 CS, NiP, and NiP-TiNi coatings in simulated seawater was studied by electrochemical impedance spectroscopy (EIS) after different periods of incubation time (7, 10, 14, 21, 28 days). The corrosion products and biofilm formation of the incubated specimens’ surfaces after 7, 10, and 28 days of incubation are checked using the scanning electron microscope (SEM) and X-ray photoelectron spectroscopy. EIS results revealed that the antimicrobial performance of the NiP-TiNi NCC is more efficient compared to the TiNi-free coating that has an inhibition efficiency of 87.5 % after 28 days of incubation time with SRB. Furthermore, SEM, EDS and XPS results confirm that negligible corrosion occurrs for the coated surfaces. Also, The mechanical properties of this coating was proved be superior compared to the Carabon steel and the NiP-based coatings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".