Characterization of Biomaterials by Soft X-Ray Spectromicroscopy
Bibliographic record
Abstract
Synchrotron-based soft X-ray spectromicroscopy techniques are emerging as useful tools to characterize potentially biocompatible materials and to probe protein interactions with model biomaterial surfaces. Simultaneous quantitative chemical analysis of the near surface region of the candidate biomaterial, and adsorbed proteins, peptides or other biological species can be obtained at high spatial resolution via scanning transmission X-ray microscopy (STXM) and X-ray photoemission electron microscopy (X-PEEM). Both techniques use near-edge X-ray absorption fine structure (NEXAFS) spectral contrast for chemical identification and quantitation. The capabilities of STXM and X-PEEM for the analysis of biomaterials are reviewed and illustrated by three recent studies: (1) characterization of hydrophobic surfaces, including adsorption of fibrinogen (Fg) or human serum albumin (HSA) to hydrophobic polymeric thin films, (2) studies of HSA adsorption to biodegradable or potentially biocompatible polymers, and (3) studies of biomaterials under fully hydrated conditions. Other recent applications of STXM and X-PEEM to biomaterials are also reviewed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".