Role of Hydrogen and Nitrogen on the Surface Chemical Structure of Bioactive Amorphous Silicon Oxynitride Films
Bibliographic record
Abstract
Silicon oxynitride (Si–O–N) is a new biomaterial in which its O/N ratio is tunable for variable Si release and its subsequent endocytotic incorporation into native hydroxyapatite for enhanced bone healing. However, the effect of nitrogen and hydrogen bonding on the formation and structure of hydroxyapatite is unclear. This study aims to uncover the roles of H and N in tuning Si–O–N surface bioactivity for hydroxyapatite formation. Conformal Si–O–N films were fabricated by plasma-enhanced chemical vapor deposition (PECVD) onto Ti/Si substrates. Fourier transform infrared spectroscopy (FTIR) and Rutherford backscattering spectrometry (RBS) analysis indicated increased Si–H and N–H bonding with increased N content. Surface energy decreased with increased N content. X-ray absorbance near edge structure (XANES) analysis showed tetrahedral coordination in O-rich films and trigonal coordination in N-rich films. O-rich films exhibited a 1:1 ratio of 2p 3/2 to 2p 1/2 electron absorbance, while this ratio was 1.73:1 for N-rich films. Both Si and N had a reduced partial charge for both O- and N-rich films, whereas O maintained its partial charge for either film. O-rich films were found to exhibit random bonding Si z O x N y, while N-rich films exhibited random mixing: [Si–Si]–[Si–O]–[Si–N]. Thus, hydrogen bonding limits random nitrogen bonding in Si–O–N films via surface Si–H and N–H bonding. Moreover, increased nitrogen content reduces the partial charge of constituent elements and changes the bonding structure from random bonding to random mixing.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".