Grafting of Aminoethylphosphonic Acid Monolayers on Titanium Nitride: The Effect of Surface Pretreatments through Electrochemical‐Assisted Oxidation
Bibliographic record
Abstract
The surface of titanium nitride (TiN) contains, in addition to nitride compounds, a certain amount of native oxide. This oxide allows the functionalization of TiN with amine‐terminated groups through spontaneous self‐assembly of aminoethylphosphonic acid. However, real industrial applications of titanium nitride, particularly for microelectronics and electrochemistry, require the development of efficient methods to improve the aminoethylphosphonate loading while preserving the main intrinsic characteristics of the TiN substrate. It is demonstrated that surface pretreatment, by either electrochemical anodization or cleaning in H2O2:HCl:H2O mixture, considerably enhances the phosphonic loading. This is the first report on mild oxidation of TiN surface using an acidic peroxide mixture. This electrochemical‐assisted method does not require any electrochemical equipment, and works through simple immersion of the TiN surface in the cleaning solution at room temperature. Moreover, cleaning in this solution leads to a significantly increased quantity of the grafted phosphonate groups via uniform hydroxylation of the entire surface, without altering the behavior of the pristine TiN. The electrochemical anodization technique, which results in quasicomplete conversion of surface TiN compounds to TiO2 under our working conditions, needs further fundamental investigation towards incorporation of oxygen into the TiN lattice.
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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) | 0.001 | 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".