Plasma‐Assisted Hydroxyapatite/Chitosan Bionanocomposite Films with Improved Thermal Stability, Biomineralization and Optical Absorption Properties
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Bibliographic record
Abstract
Abstract Hydroxyapatite (HAp) is a well‐known precursor for synthesizing different bionanocomposite products for biomedical applications. For the first time, we aimed to evaluate the effects of plasma surface functionalization of HAp nanoparticles (NPs) on the chemical, physical, and bio‐functional properties of chitosan films using experimental and computational evaluations. Atmospheric air plasma process was conducted on HAp NPs at two different air pressures (650 and 1300 mTorr) and four different exposure times (1, 3, 6, and 9 min), followed by fabrication of HAp/chitosan bionanocomposites. Fourier transform infrared (FTIR) spectra proved that the position of bands at 1639 and 1037 cm −1 were shifted to 1635 and 1031 cm −1 due to the interaction between chitosan amine groups and HAp phosphate groups. Quantum mechanical and molecular dynamic (MD) simulations were used to understand the interactions between chitosan and HAp. Density functional theory (DFT) calculations were used to explore the electronic properties of untreated and plasma‐treated HAp (T‐HAp). MD simulations using the PCFF force field were used to investigate the interactions of HAp/chitosan and T‐HAp/chitosan bionanocomposites. According to the results from thermal gravimetric analysis (TGA), the duration of HAp NP plasma treatment is a significant factor in the weight loss properties for the resultant HAp/chitosan bionanocomposites. The overall reflectance % properties of films prepared with T‐HAp NP samples decreased, confirming the potential applications for skin tissue protection against solar UV radiation. The bioactivity of the bionanocomposite films was also studied by examining the HAp formation by incubating in simulated body fluid.
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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 | 0.000 | 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 it