Line optical tweezers as controllable micromachines: techniques and emerging trends
Bibliographic record
Abstract
In the past three decades, the technology of optical tweezers has made significant contributions in various scientific areas, including optics, photonics, and nanosciences. Breakthroughs include manipulating particles in both static and dynamic ways, particle sorting, and constructing controllable micromachines. Advances in shaping and controlling the laser beam profile enable control over the position and location of the trap, which has many possible applications. A line optical tweezer (LOT) can be created by rapidly moving a spot optical tweezer using a tool such as a galvanometer mirror or an acousto-optic modulator. By manipulating the intensity profile along the beam line to be asymmetric or non-uniform, the technique can be adapted to various specific applications. Among the many exciting applications of line optical tweezers, in this work, we discuss in detail applications of LOT, including probing colloidal interactions, transporting and sorting of colloidal microspheres, self-propelled motions, trapping anisotropic particles, exploring colloidal interactions at fluid-fluid interfaces, and building optical thermal ratchets. We further discuss prospective applications in each of these areas of soft matter, including polymeric and biological soft materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".