Surface analysis methods for characterizing polymeric biomaterials
Bibliographic record
Abstract
Surface properties have an enormous effect on the success or failure of a biomaterial device, thus signifying the considerable importance of and the need for adequate characterization of the biomaterial surface. Microscopy techniques used in the analysis of biomaterial surfaces include scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and confocal microscopy. Spectroscopic techniques include X-ray photoelectron spectroscopy, Fourier Transform infrared attenuated total reflection and secondary ion mass spectrometry. The measurement of contact angles, although one of the earlier techniques developed remains a very useful tool in the evaluation of surface hydrophobicity/hydrophilicity. This paper provides a brief, easy to understand synopsis of these and other techniques including emerging techniques, which are proving useful in the analysis of the surface properties of polymeric biomaterials. Cautionary statements have been made, numerous authors referenced and examples used to show the specific type of information that can be acquired from the different techniques used in the characterization of polymeric biomaterials surfaces.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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".