Influence of Treatment Conditions on the Chemical Oxidative Activity of H<sub>2</sub>SO<sub>4</sub>/H<sub>2</sub>O<sub>2</sub> Mixtures for Modulating the Topography of Titanium
Bibliographic record
Abstract
Abstract Host‐tissue integration of medical implants is governed by their surface properties. The capacity to rationally design the surface physico‐chemical cues of implantable materials is thus a fundamental prerequisite to confer enhanced biocompatibility. Our previous work demonstrated that different cellular processes are elicited by the nanotexture generated on titanium (cpTi) and Ti6Al4V alloy by chemical oxidation with a H2SO4/H2O2 mixture. Here, we illustrate that by varying the etching parameters such as temperature, concentration, and treatment time, we can create a variety of surface features on titanium which are expected to impact its biological response. The modified submicron and nanotextured surfaces were characterized by scanning electron (SEM) and atomic force (AFM) microscopies. Contact angle measurements revealed the higher hydrophilicity of the modified surfaces compared to untreated samples and Fourier transform infrared spectroscopy (FT‐IR) established that the etching generated a TiO2 layer with a thickness in the 40–60 nm range.
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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".