Fluoropolymer Coatings with Inhibitor-Laden Zinc Oxide Nanoparticles: Electrochemical Characterization and Monte Carlo Simulation
Bibliographic record
Abstract
In this study, we developed coatings of varying concentrations (2, 4, and 6 wt %) from zinc oxide (ZnO) nanoparticles coated with a layered phytic acid shell dispersed in poly(vinylidene fluoride- co -hexafluoropropylene) (PVDF-HFP) matrix. Comprehensive electrochemical, mechanical, thermal, and microscopic investigations were performed. Results from impedance measurement revealed that the coating with 6 wt % inhibitor-loaded ZnO had higher impedance and charge-transfer resistance than those with lower concentrations, indicating better corrosion resistance. Moreover, improved corrosion resistance was attributed to the passive barrier properties of PVDF-HFP and active corrosion inhibition via PO 4 3– ion released from the ZnO nanoparticles, as evidenced by spectroscopy and electrochemical results, whereas the 4 wt % formulation showed the best mechanical attributes, including surface hardness, adhesion strength, and tensile properties, due to uniform nanoparticle dispersion and interfacial interactions within the layered shell. Further, microscopy results showed enhanced nanoparticle dispersion, surface defects, and interfacial interactions. Thermal and mechanical analyses revealed enhanced thermal stability and segmental rigidity, indicative of stronger polymer–filler interactions within the coatings. Impedance trends at intermediate, untested nanofiller concentrations were predicted by propagating experimental uncertainty between measured data points using a Monte-Carlo-based stochastic interpolation methodology. This experimental and data-driven interpolation approach showed the coatings’ multifunctional protective action and rationally supports screening formulations for corrosion prevention.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".