Angular Trapping of Spherical Janus Particles
Bibliographic record
Abstract
Abstract Developing angular trapping methods, which enable optical tweezers to rotate a micronsized bead, is of great importance for studies of biomacromolecules in a wide range of torque‐generation processes. Here a novel controlled angular trapping method based on model composite Janus particles is reported, which consist of two hemispheres made of polystyrene and poly(methyl methacrylate). Through computational and experimental studies, the feasibility to control the rotation of a Janus particle in a linearly polarized laser trap is demonstrated. The results show that the Janus particle aligned its two hemispheres interface parallel to the laser propagation direction and polarization direction. The rotational state of the particle can be directly visualized by using a camera. The rotation of the Janus particle in the laser trap can be fully controlled in real time by controlling the laser polarization direction. The newly developed angular trapping technique has the great advantage of easy implementation and real‐time controllability. Considering the easy chemical preparation of Janus particles and implementation of the angular trapping, this novel method has the potential of becoming a general angular trapping method. It is anticipated that this new method will significantly broaden the availability of angular trapping in the biophysics community.
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 | 0.000 |
| 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".