Controlled drug release from a polymer-free multi-walled carbon nanotube-based coating
Why this work is in the frame
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Bibliographic record
Abstract
This work presents the development and characterization of a polymer-free coating for metallic implant surfaces whose drug release kinetics can be altered by the application of an external alternating magnetic field. The coating structure consists of two distinct multi-walled carbon nanotube (MWCNT) layers which encompass a film of dispersed iron-based nanoparticles. The candidate drug was heparin, which was loaded into the coating non-covalently. The drug elution characteristics from the coating were compared against the ones from bare metallic surfaces. Release profiles from coatings with different initial heparin loadings were studied with and without the application of a periodic magnetic field at ∼ 30 mT and 95 kHz over 30 min. This investigation resulted in the synthesis of a MWCNT-based coating with a thickness of 6.39 ± 0.21 μm and an iron-based nanoparticle loading of approximately 14.19 ± 2.01 μg/cm 2 . Compared to bare metal surfaces, the addition of the MWCNT coating reduced the burst release from > 95% to 77.26 ± 2.33 % heparin elution within the first hour. The heparin release profiles in static conditions (without an external trigger) were accompanied by an initial burst release which followed first-order kinetics (K 1 = 54.99, R 2 = 0.89, R 2 adjusted = 0.88), while the sustained drug elution best fit zero-order kinetics (K 0 = 0.077, R 2 = 0.95, R 2 adjusted = 0.94). For the same elution times, the amount of heparin released increased by an average of 7.16 ± 1.15 % for triggered compared to passive elution.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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