Controlled drug release from a polymer-free multi-walled carbon nanotube-based coating
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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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) | 0.000 | 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 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".