Polarization-Resolved Second Harmonic Generation Microscopy of Silk Fibers Is Sensitive to β-Sheet Orientation and Molecular Structure
Bibliographic record
Abstract
Spider silk biomaterials have generated significant interest due to their high strength, biocompatibility, and biodegradability. However, the complex multiscale structure of silk fibers creates difficulties in investigating the relationship between the structure and mechanical properties in silks. Previous work has shown that silks at the focus of an ultrafast laser produce a significant second harmonic generation (SHG) signal. This presents an exciting opportunity since polarization-resolved SHG microscopy (PSHG) is a technique that has shown high sensitivity local molecular structure and organization in several biological samples. However, applications of PSHG to silks have been impeded by the lack of a theoretical model relating silk molecular structure and organization to its PSHG response. Here, a theoretical model of PSHG from silk materials is presented, which relates β-sheet organization within silk fibers to experimentally measurable parameters. Based on this, we present evidence that postspin stretching in ethanol induces planar alignment of the β-sheets within recombinant spider silk fibers, and the molecular structure and degree of axial alignment of β-sheets are highly dependent on the level of postspin stretching. Overall, this work demonstrates the significant potential for the application of PSHG to map the local structure of silk fibers, providing opportunities for the investigation of silk-based biomaterials.
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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) | 0.000 | 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) | 0.000 | 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".