Electrophoretic deposition of biomaterials
Bibliographic record
Abstract
Electrophoretic deposition (EPD) is attracting increasing attention as an effective technique for the processing of biomaterials, specifically bioactive coatings and biomedical nanostructures. The well-known advantages of EPD for the production of a wide range of microstructures and nanostructures as well as unique and complex material combinations are being exploited, starting from well-dispersed suspensions of biomaterials in particulate form (microsized and nanoscale particles, nanotubes, nanoplatelets). EPD of biological entities such as enzymes, bacteria and cells is also being investigated. The review presents a comprehensive summary and discussion of relevant recent work on EPD describing the specific application of the technique in the processing of several biomaterials, focusing on (i) conventional bioactive (inorganic) coatings, e.g. hydroxyapatite or bioactive glass coatings on orthopaedic implants, and (ii) biomedical nanostructures, including biopolymer-ceramic nanocomposites, carbon nanotube coatings, tissue engineering scaffolds, deposition of proteins and other biological entities for sensors and advanced functional coatings. It is the intention to inform the reader on how EPD has become an important tool in advanced biomaterials processing, as a convenient alternative to conventional methods, and to present the potential of the technique to manipulate and control the deposition of a range of nanomaterials of interest in the biomedical and biotechnology fields.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".