The Role of Nicotine in the Corrosive Behavior of a <scp>Ti</scp>‐6<scp>Al</scp>‐4<scp>V</scp> Dental Implant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 it