Scalable polydopamine coatings with increased thickness and stability using polyamidoamine dendrimers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Polydopamine coatings have garnered significant attention due to their versatility and multifunctional properties, rendering them suitable for a wide array of applications, including medical devices and the creation of coatings with varying degrees of amphiphilicity. Nevertheless, the fabricating of durable polydopamine coatings with substantial thickness remains challenging, primarily due to difficulties in controlling the deposition process and the potential for coating delamination or detachment under mechanical stress. Here, we reported the development of easily scalable polydopamine (PDA) coatings with remarkable thickness (approximately 1.7 μm) and stability, adaptable to diverse materials, by incorporating an amine-containing dendrimer . Through a systematic screening process, we identified an optimal dopamine-dendrimer combination that effectively modified the synthesis of polycatecholamine, facilitated nanoparticle formation, and enhanced stability. This resulted in the controlled deposition of composite PDA nanoparticles formed in situ. Using this optimal binary composition, we achieved the eco-friendly creation of a superhydrophobic coating with exceptional stability via a one-step dip post-modification process involving polydimethylsiloxane (PDMS). This innovative approach yielded a remarkable water contact angle exceeding 150°. Furthermore, we measured advancing and receding contact angles of 155.88° and 150.90°, respectively, resulting in a hysteresis of only 4.98°.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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