The Role of Nicotine in the Corrosive Behavior of a <scp>Ti</scp>‐6<scp>Al</scp>‐4<scp>V</scp> Dental Implant
Bibliographic record
Abstract
BACKGROUND: Metals react chemically/electrochemically in electrolytic solutions, such as that present in the oral cavity, which leads to corrosion of metal dental implants. Corrosion can increase the failure rate of dental implants. PURPOSE: This study evaluated the corrosion behavior of nicotine on Ti-6Al-4V under physiological conditions. It was hypothesized that nicotine in artificial saliva would have an adverse effect on the corrosion of Ti-6Al-4V. METHODS: Ti-6Al-4V discs were electrochemically analyzed using a three-electrode electrochemical cell. The disks were immersed in an electrolytic artificial saliva with varying pH (3.0 and 6.5) and nicotine concentration (control, 1 mg/mL, 5 mg/mL, and 20 mg/mL). Open circuit potential, cyclic polarization, and electrochemical impedance spectroscopy (EIS) tests were conducted. RESULTS: Electrochemical parameters indicated that the presence of nicotine significantly reduced (p < .05) the corrosion rate. For example, there was a decrease in corrosion current density from 2.94 × 10(-3) μA/cm(2) to 1.43 × 10(-3) μA/cm(2) in control compared with 20 mg/mL nicotine at pH 6.5. EIS results exhibited an unexpected trend in that the presence of nicotine decreased polarization resistance. This suggested a decrease in passive film growth. CONCLUSIONS: At certain concentrations, nicotine inhibits local corrosion; however, it also prevents the formation of a protective oxide film.
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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 | 0.001 |
| 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".