A Novel Technique to Increase the Thickness of TiO₂ of Dental Implants by Nd: DPSS Q-sw Laser Treatment
Bibliographic record
Abstract
High bone-implant contact is a crucial factor in the achievement of osseointegration and long time clinical success of dental implants. Micro, nano, microtopography, and oxide layer of dental implants influence tissue response. The lasers were used for achieving an implant surface with homogeneous micro texturing and uncontaminated surface. The present study aimed to characterize the implant surfaces treated by Nd: DPSS Q-sw Laser treatment compared to machined implants. A total of 10 machined implants and 10 lasered surface implants were evaluated in this study. The implant surfaces were evaluated by X-ray Photoelectron Spectroscopy (XPS), Auger Electron Spectroscopy (AES), and metallography to characterize and measure the thickness of the oxide layer on the implant titanium surface. The machined surfaces showed a non-homogeneous oxide layer ranging between 20 and 30 nm. The lasered implant surfaces showed a homogeneous oxide layer ranging between 400 nm and 460 nm in the area of the laser holes, while outside the layer, thickness ranged between 200 nm and 400 nm without microcracks or evidence of damage. Another exciting result after this laser treatment is a topographically controlled, repeatable, homogeneous, and clean surface. This technique can obtain the implant surface without leaving residues of foreign substances on it. The study results indicate that the use of Nd: DPSS Q-sw laser produces a predictable and reproducible treatment able to improve the titanium oxide layer on the dental implant surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".