Bibliographic record
Abstract
Optical trapping has become an important tool in a wide range of fields. While these traps are most commonly realized using optical tweezers, dual-beam optical traps offer specific advantages for certain experiments. It is commonly assumed that a particle will become trapped midway between the focal points of the two beams. However, this is not always the case. We perform a theoretical and experimental investigation of trapping positions of weakly absorbing, spherical particles in a dual-beam optical trap. We evaluate the effect of offsetting the beams in the direction of propagation and identify four regimes with distinct trapping behavior. The effect of an offset perpendicular to the propagation direction and an imbalance in power between the two beams is also considered. Experiments utilize an aqueous aerosol particle whose size can be readily controlled and monitored over hundreds of nanometers. As such, it serves as an excellent probe of the optical trap. We demonstrate that it is possible to fit the evolution of the particle trapping position in order to determine the position of the particle relative to the focal point of each beam. The results presented here provide key insights into the workings of dual-beam optical traps, elucidating more complex behaviors than previously known.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".