Analyzing radial and azimuthal Brownian motion in Laguerre–Gaussian optical traps
Bibliographic record
Abstract
Optical trapping, orchestrated by intensity-gradient forces and radiation pressure, is a powerful technique in physics and nanotechnology that enables precise control and manipulation of microscopic and nanoscale particles with widespread applications in biotechnology, nanoscience, and fundamental physics research. The annular intensity profile and helical phase structure of Laguerre–Gaussian (LG) beams furnish unparalleled conditions for optical trapping, particularly, in the context of studying rotational motion of particles known as tweezing. Here, we study tweezing of dielectric particles trapped with higher-order LG beams under the strong focusing condition and in the presence of spherical aberrations. We quantify the transverse trap stiffness with the aid of two complementary approaches—Boltzmann statistics and the equipartition theorem—through a comprehensive analysis of the times series of the radial and angular position of a particle. In contrast to the quadrant photodiode (QPD) method, which is faster but has a limited field of view and requires careful calibration, our wide-field imaging-based approach provides direct distance calibration and allows accurate stiffness measurements even when the trapped particle experiences a significant slowdown or the light field distribution lacks perfect axial symmetry. The method offers several attractive advantages: (i) it enables reliable characterization of stiffness anisotropy for LG traps with topological charges up to ℓ =7; (ii) it remains robust in the presence of aberrations that distort the axial symmetry of the beam; and (iii) it provides insight into both radial stability and angular-velocity variations. The proposed approach can enhance the accuracy of optical trapping measurements, improve the design of high-order LG optical traps, and deepen our understanding of particle behavior in complex optical potential landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".