Titanium Implants after Alkali Heating Treatment with a [Zn(OH)<sub>4</sub>]<sup>2−</sup> Complex: Analysis of Interfacial Bond Strength Using Push‐Out Tests
Bibliographic record
Abstract
BACKGROUND: Clinical demands for stronger and faster bone bonding to implants have motivated the development of chemically and topographically modified surfaces capable of chemical bonding. This study presents a new one-step alkali heat treatment performed with a solution containing [Zn(OH)(4)](2-) complex as an alternative to the conventional NaOH solution. PURPOSE: The objective of this work is to assess the effect of a Zn-modified surface chemistry on bone-implant shear strength using a rabbit model. MATERIALS AND METHODS: The study was conducted on mechanical-grinded and smooth surfaces of Ti cylindrical implants. The topographical structure, chemical surface composition, and structural properties of the chemically modified titanium surface were studied by scanning electron microscopy, x-ray photoelectron spectroscopy, and x-ray diffractometry. Implant-bone shear strength was evaluated by push-out tests undertaken at 4, 12, and 24 weeks after insertion in rabbit femora. RESULTS: Implants with smooth and rough surfaces chemically-modified with a solution containing [Zn(OH)(4)](2-) complex demonstrated significantly stronger bone fixation than nonmodified implants at all healing times (p < 0.05). CONCLUSIONS: The obtained results suggest that biochemical bonding at the bone-implant interface, stimulated by the Zn(2+) ion release in combination with mechanical interlocking definitively improved the implant fixation.
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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 | 0.000 | 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".