Nonlinear optical response and self-trapping of light in biological suspensions
Bibliographic record
Abstract
In the past decade, the development of artificial materials exhibiting novel optical properties has become a major scientific endeavor. One particularly interesting system is synthetic soft matter, which plays a central role in numerous fields ranging from life sciences, chemistry to condensed matter and biophysics. In this paper, we review briefly the optical force-induced nonlinearities in colloidal suspensions, which can give rise to nonlinear self-trapping of light for enhanced propagation through otherwise highly scattering media such as dielectric and plasmonic nanosuspensions. We then focus on discussing our recent work with respect to nonlinear biological suspensions, including self-trapping of light in colloidal suspensions of marine bacteria and red blood cells, where the nonlinear response is largely attributed to the optical forces acting on the cells. Although it is commonly believed that biological media cannot exhibit high optical nonlinearity, self-focusing of light and formation of soliton-like waveguides in bio-soft matter have been observed. Furthermore, we present preliminary results on biological waveguiding and sensing, and discuss some perspectives towards biomedical applications. The concept may be developed for subsequent studies and techniques in situations when low scattering and deep penetration of light is desired.
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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) | 0.001 | 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".