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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".