ZIF8/hydroxyapatite sheets: A sustainable triple-ligand MOF-based nanocarrier for enhancing the self-healing capabilities of the epoxy composite
Bibliographic record
Abstract
A triple ligand metal organic framework (MOF) (named mix@HA/Z8) was designed and used as a smart anti-corrosion filler benzotriazole (BTA) and zinc nitrate were loaded into the pH-sensitive, highly resistant, and thermally stable Z8 nanocarriers. These nanocarriers were subsequently applied to the hydroxyapatite sheets to enhance the self-healing capabilities of the epoxy coating. This addition not only improved the barrier capability of the coating but also enhanced its self-healing nature. In order to validate the successful synthesis of the smart inhibitor, several tests such as Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and Transmission electron microscopy (TEM) were conducted. Additionally, Brunauer, Emmett, and Teller (BET) and Inductively Coupled Plasma (ICP) experiments were carried out to verify the appropriate loading and release of the inhibitor. The polarization and EIS experiments conducted to assess the anti-corrosion performance in the solution phase revealed that the mix@HA/Z8 had the best corrosion inhibition (51%) among the other samples. Furthermore, the EIS test for the intact coating containing the mix@HA/Z8 after 56 days of exposure to 3.5% saline solution demonstrated its ability to maintain log|Z| f = 0.01Hz higher than 10 10.5 Ω cm 2 . FE-SEM testing of the scratched coating containing mix@HA/Z8 (after 48 h of immersion) confirmed its remarkable self-healing feature. The enhancement of the adhesion of mix@HA/Z8, as demonstrated by the pull-off analysis, indicated that the adhesion loss of the mix sample was lower than that of other coatings, standing at 21.21%.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".