Bibliographic record
Abstract
Abstract In this work, benzotriazole (BTA) inhibitors were deposited on the surface of nano-SiO2 particles, which served as the inhibitor carriers. Polyelectrolytes were then adsorbed on the particle surface by a layer-by-layer method to prepare the nanocontainers to store inhibitors. The inhibitor-loading nanocontainers were characterized by a number of techniques, including scanning electron microscopy, energy-dispersive X-ray spectrum, Fast Fourier Infrared spectrum and thermal gravimetric analysis. The corrosion resistance and the nano-container doped epoxy coatings was evaluated by electrochemical impedance spectroscopy. Results demonstrate that the SiO2 nanoparticles based polyelectrolyte nano-containers are successfully fabricated to store BTA. The nano-containers added in NaCl solution are able to inhibit the corrosion of the steel. The inhibiting performance is improved with immersion time. The inhibiting efficiency is over 66% after 24 h of testing. It is expected that the inhibiting performance further increases with the continuous release of BTA from the nano-containers with time. When the steel coated with the BTA loaded nano-containers is immersed in NaCl solution, the corrosion inhibition is time dependent upon the release of encapsulated inhibitors from the containers. The change of solution pH upon the coating damage and generation of corrosive environment may trigger the opening of the nano-containers for inhibitor releasing.
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 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 | 0.001 |
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".