Taming Macromolecules with Light: Lessons Learned from Vibrational Spectroscopy
Bibliographic record
Abstract
Exciting new applications, from large-area nanopatterning and templating to soft light-powered robotics, are emerging from the fundamental research on light-triggered changes in macromolecular systems upon photoisomerization of azobenzene-based molecular photoswitches. The understanding of how the initial molecular-scale photoisomerization of azobenzene, a complex photochemical event in itself, is translated into the response of macromolecules and even into macroscopic-scale motion of illuminated azomaterials is an enormous task. The focus here is on how this knowledge has advanced by applying different vibrational spectroscopy techniques that provide rich molecular insight into the photoresponse of chemically specific molecular moieties. In particular, infrared and Raman spectroscopy studies are highlighted, in the context of phototriggered perturbation of self-assembled structures and photoinduced linear and circular anisotropy, as well as photoinduced surface patterning, with the objective of offering a perspective on how vibrational spectroscopy can help in answering an array of essential yet unsettled questions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".