Slow photons in the fast lane in chemistry
Bibliographic record
Abstract
A driving force in the rapidly developing field of photonic crystals has been the photonic bandgap, a range of energies where the propagation of light is completely forbidden. The photonic bandgap allows the design of photonic lattices that localize, guide and bend light at sub-micron length scales, providing opportunities for the creation of miniature optical devices and integrated optical circuits to help drive the revolution in photonics. A less well known attribute of photonic crystals is their theoretical ability to slow light to a velocity of zero. This phenomenon can be achieved at the high and low energy edges of photonic stopgaps where the photonic bands are flat and light exists as a standing wave commensurate with the photonic lattice and travels at a group velocity of zero, referred to as “slow photons” herein. It has been shown theoretically that the probability of harvesting slow photons scales inversely with their group velocity. This means that a number of well known photon driven processes and devices in chemistry and physics can be enhanced by capturing this unique property of slow photons. In this paper we will look at slow photons mainly through the eye of chemistry and highlight some recent developments in this exciting and emerging field that demonstrate the potential of slow photons in materials chemistry and nanochemistry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".