Application of bile acids for biomedical devices and sensors
Bibliographic record
Abstract
Abstract The objective of this mini‐review is to describe the recent advances and applications of bile acids (BAs) for manufacturing biomedical devices and sensors. The biological origin and unique multifunctional properties of BAs are key factors for novel biomedical applications. BAs are used for solubilization of drugs and the development of advanced devices for controlled drug delivery. BAs outperform many commercial dispersants in the dispersion of carbon nanotubes and hydrophobic polymers. They also exhibit unique gel‐forming and film‐forming properties, which are used for the development of biosensors and functionalization of implant materials. Especially important is the possibility of bile acid gel synthesis for controlled release of drugs and other functional molecules. Electrodeposition of BAs films and composites is emerging as a new area of technological interest. The discovery of BAs mediating the biomineralization phenomena allows the development of biomedical implants with enhanced bioactivity and biocompatibility. Bile acids are used as efficient biocompatible reducing and capping agents for the synthesis of inorganic particles and their functionalization for application in biosensors and antimicrobial coatings. The progress in the modification of biopolymers with BAs and development of BAs derivatives paves the way for the fabrication of advanced implants and sensors.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".