Fluoropolymer Coatings with Inhibitor-Laden Zinc Oxide Nanoparticles: Electrochemical Characterization and Monte Carlo Simulation
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it