Nanotubular surface modification of metallic implants via electrochemical anodization technique
Bibliographic record
Abstract
Due to increased awareness and interest in the biomedical implant field as a result of an aging population, research in the field of implantable devices has grown rapidly in the last few decades. Among the biomedical implants, metallic implant materials have been widely used to replace disordered bony tissues in orthopedic and orthodontic surgeries. The clinical success of implants is closely related to their early osseointegration (ie, the direct structural and functional connection between living bone and the surface of a load-bearing artificial implant), which relies heavily on the surface condition of the implant. Electrochemical techniques for modifying biomedical implants are relatively simple, cost-effective, and appropriate for implants with complex shapes. Recently, metal oxide nanotubular arrays via electrochemical anodization have become an attractive technique to build up on metallic implants to enhance the biocompatibility and bioactivity. This article will thoroughly review the relevance of electrochemical anodization techniques for the modification of metallic implant surfaces in nanoscale, and cover the electrochemical anodization techniques used in the development of the types of nanotubular/nanoporous modification achievable via electrochemical approaches, which hold tremendous potential for bio-implant applications. In vitro and in vivo studies using metallic oxide nanotubes are also presented, revealing the potential of nanotubes in biomedical applications. Finally, an outlook of future growth of research in metallic oxide nanotubular arrays is provided. This article will therefore provide researchers with an in-depth understanding of electrochemical anodization modification and provide guidance regarding the design and tuning of new materials to achieve a desired performance and reliable biocompatibility.
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 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 | 0.000 | 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".