Nanojet Visualization and Dark-field Imaging of Optically Trapped Vaterite Capsules with Endoscopic Illumination
Bibliographic record
Abstract
Optical responsivity grants biomedical capsules additional capabilities, promoting them towards multifunctional theragnostic nanodevices. In this endeavor, screening candidates under conditions that closely resemble in situ environments is crucial for both the initial optimization and the subsequent inspection stages of development and operation. Optical tweezers equipped with dark-field spectroscopy are among the preferable tools for nanoparticle imaging and refractometry. However, the effectiveness of conventional illumination and light collection arrangements for inspecting anisotropic complex inner composition particles is quite limited due to reduced collection angles, which can result in the omission of features in scattering diagrams. Here we introduce an endoscopic dark-field illumination scheme, where light is launched on an optically trapped particle from a single-mode fiber, immersed into a fluid cell. This arrangement disentangles illumination and collection paths, thus allowing the collection of scattered light with a very high numerical aperture. This methodology is applied to vaterite nanocapsules, which are known to possess strong anisotropic responses. Tweezer configuration allows revealing optical properties for different crystallographic orientations of vaterite, which is complex to do otherwise. Furthermore, endoscopic dark-field images reveal the emergence of polarization-dependent long-range photonic nanojets, which are capable of interacting with nearby particles, demonstrating a new pathway for nanojet image formation.
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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".