Robust Nanocomposite Coatings Inspired by Structures of Nacre
Bibliographic record
Abstract
Carbon steel piping can be exposed to environments that contain various chemical and organic elements that induce corrosion and cracking events. This can lead to the loss of fluid into surrounding sensitive and remote environments. To minimize this inherent risk, various coating technologies have been utilized over the years in industry. These coatings typically suffer from complex application methods, high application cost, and vulnerabilities to environmental effects such as mechanical damage and cathodic disbondment. To overcome these challenges, a novel epoxy based composite coating that utilizes the properties of various nano-particulates such as graphene nanoplatelets (GnP), multi-walled carbon nanotubes (MWCNTs), chitosan, and hBN (Hexagonal boron nitride) is developed. These nanoparticles create a nano-scale “brick and mortar” type effect that is analogous to various natural structures such as the abalone shell (nacre). These nano-structures also enhance coating performance by increasing mechanical strength and anti-bacterial properties while simultaneously decreasing gas permeability. This performance enhancement serves to reduce overall corrosion-induced disbondment area. The dispersion of nanoparticles is verified using various microscopy methods such as scanning election microscopy and an optical 3D profilometer. To confirm the role of nanoparticles in the epoxy composite, the samples undergo rigorous testing to determine both mechanical properties as well as the feasibility of coating application, in particular, for use on girth welds. Using a dynamic mechanical analysis (DMA), the material strength of each combination of nanocomposites is tested and used to determine the glass transition temperature. The testing also includes abrasion, and both long-term mechanical and thermal behaviors of the coating. To test the feasibility of the coating, cathodic protection tests in an accelerated corrosive environment, and gas permeability tests are carried out. The results show that the composite coating made from these nanomaterials had a decrease in cathodic disbondment area and gas permeability and an increase the glass transition temperature and scratch resistance. Therefore, the nanocomposite coatings are found to be a significant improvement over standard epoxy-based coating.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".