SURFACE MODIFICATION OF COMMERCIALLY PURE TITANIUM BY GOLD-ION IRRADIATION FOR BIOMEDICAL APPLICATIONS
Bibliographic record
Abstract
This research work’s aim was to enhance the biocompatibility of the commercially pure titanium (cpTi-2) surface via Au-ion irradiation. Various ion dosages, i.e. [Formula: see text] (Au-11), [Formula: see text] (Au-12) and [Formula: see text] (Au-13) ions[Formula: see text][Formula: see text][Formula: see text]cm[Formula: see text], were produced by exposing the polished cpTi-2 samples to Au-ion beam at room temperature. The surface topographic features of cpTi-2 and the effects of Au-ion-implanted surfaces were examined by atomic force microscopy and XRD analysis. Open-Circuit Potential (OCP), Potentiodynamic Polarization Scans (PPS) and Electrochemical Impedance Spectroscopy (EIS) were used to compare the electrochemical behavior of the cpTi-2 and Au-ion-implanted samples in Ringer’s lactate (RL) solution at 37∘C. The effects of Au-ion irradiation on the proliferation of mesenchymal stem cells (MSCs) were estimated during 24[Formula: see text]h and 48[Formula: see text]h of exposure. Based on the experimental results, Au-12 samples presented more positive OCP and lower corrosion rate in RL solution than the Au-11 and Au-13 samples. No significant change in the morphology of the MSCs was observed after exposure to the Au-ion-implanted samples. Similar to the controlled medium, the percentage of viability of the cells of the cells on Au-12 increased from 75% to 165% on the surface of Au-13 samples during 48[Formula: see text]h of incubation indicating the positive effects of Au-ion irradiation for biocompatibility.
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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 | 0.001 | 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".