A Biomimetic Strategy for the Fabrication of Micro- and Nanodiamond Composite Films
Bibliographic record
Abstract
This investigation is motivated by increasing interest in diamond and composite films for applications in biomedical and electronic devices. A biomimetic strategy is based on the use of commercial bile acids, such as ursodeoxycholic acid (UDCA) and hyodeoxycholic acid (HDCA). Composite films are developed using UDCA and HDCA as solubilizing agents for poly(ethyl methacrylate) (PEMA) in isopropanol and as dispersing agents for micro- and nanodiamonds. In this approach, the use of traditional toxic solvents for PEMA dissolution is avoided. The ability to obtain high concentrations of high molecular mass PEMA and disperse diamond particles in such solutions is a key factor for the development of a dip-coating method. The PEMA dissolution and diamond dispersion mechanisms are discussed. The composition and microstructure of the films can be varied by variation of the diamond particle size and concentration in the suspensions. The films can be obtained as singular layers of different compositions, multilayers of similar composition, or alternating layers of different compositions. The films combine corrosion protection property and biocompatibility of PEMA with advanced functional properties of diamonds.
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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 | 0.001 |
| 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".