Photo‐Control of Molecular Alignment for Photonic and Mechanical Applications
Bibliographic record
Abstract
The development of stimuli-responsive functional materials is among the key goals of modern materials science. The structure and properties of such switchable materials can be designed to be controlled by various stimuli, among which light is frequently the most powerful trigger. Light is a gentle energy source that can target materials remotely, with extremely high spatial and temporal resolution, easily and cheaply. Light control over molecular alignment in particular has, in recent years, attracted significant interest, due to potential applications as reconfigurable photonic elements and optical-to-mechanical energy conversion. We introduce some key current research areas of photo-driven molecular alignment methods and highlight some of their recent applications using photo-chemical, photo-physical, and photo-physico-chemical systems. Photo-chemical and photo-physical alignment processes especially benefit from well-established theoretical understanding. The latest class, photo-physico-chemical alignment methods, where alignment shear stress arises from molecular diffusion, is also now just being approached theoretically, to help rationalize and thus optimize this new type of molecular alignment system. Twisting, aligning, and bending materials with light are exciting effects in particular that can offer important and significant advantages to many applied fields and warrant much further study and applications. Continued efforts toward the development of molecular alignment control with light can open new possibilities and opportunities for new future applications of functional soft materials for next-generation effects and devices.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".