A versatile and facile surface modification route based on polydopamine for the growth of MOF films on different substrates
Bibliographic record
Abstract
Abstract Thin MOF‐5 films were grown from various substrates, which were modified by polydopamine – a bio‐inspired multi‐functional coating. A MOF‐5 mother solution was used. These supports included polypropylene (PP) fibrous membranes, PP porous membranes, and aluminum foils. SEM and XRD were used to characterize the prepared MOF films. The unmodified substrates did not support the growth of MOF films with MOF particles sparsely scattered on the surfaces. In contrast, the polydopamine‐modified substrates were fully covered with a dense layer of MOF crystals. Polydopamine was proven to be very useful in the surface modification that facilitated the growth of MOF films on the substrate materials.
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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 | 0.000 | 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 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".