On‐Site Surface Coordination Complexation via Mechanochemistry for Versatile Metal–Phenolic Networks Films
Bibliographic record
Abstract
Abstract The development of functional metal–phenolic networks (MPNs) films by a simple and green strategy on metallic plane substrates for surface modification is a big challenge. Herein, tribochemistry, as an effective and robust method to prepare MPNs films with versatile applications and controllable thickness (≈1.5 nm to 2.2 µm) by surface coordination complexation derived from tannic acid (TA) and metal oxides or ions (M) on plane substrates, is reported. The mechanism of the formation of TA‐M films is investigated in detail, showing that films are constructed by two‐layer structures. At the bottom of films, the chelation of TA active moieties and M is triggered by friction, facilitating the coating growth and reducing the friction coefficient. There is a downward trend in the concentration of M with the thickness of films increasing, which is attributed to the diffusion of metal ions. As a result, the dominant structure of films changes into the hydrogen bonding or π–π stacking interaction among oligomers derived from coupling of the TA active moieties. Such facile surface modification strategy can broaden in situ mechanochemical synthesis of functional layers and open a promising route for the design of patterning, antifouling, and controlled release coatings.
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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".