Synthesis of polycaprolactone-titanium oxide multilayer films by nanosecond laser pulses and electrospinning technique for better implant fabrication
Bibliographic record
Abstract
In this study, a combination of electrospinning and laser texturing is introduced as a novel method for increasing the biocompatibility of metal implants. Besides having a rough laser treated surface, the implant benefits from the high porosity and better wettability of an electrospun fibrous structure, which is a more favorable environment for cell proliferation. Titanium samples were patterned using a nanosecond laser beam and were placed as collectors in an electrospinning machine. They were then soaked in simulated body fluid for four weeks. Energy Dispersive X-ray and X-Ray Diffraction results indicate significantly more hydroxyapatite formation on laser treated samples with nanoscale fibers deposited on their surface. This shows that having a laser treated surface underneath the fibrous layer can improve short-term biocompatibility even before degradation of fibers. The thermal conductivity of the electrospun layer, measured using a Hot Disk Transient Plane Source instrument and computer code, was shown to be considerably lower than that of titanium and very close to bone. The presence of this layer can therefore be beneficial in making the implant more compatible to a biological medium. In case of dental implants, it was shown that this layer can act as a thermal barrier while a hot beverage is consumed and it can decrease the temperature rise by about 60%, which avoids any possible damage to newly formed cells during the healing period.
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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) | 0.001 | 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".