Optoelectronic tweezers: a versatile toolbox for nano-/micro-manipulation
Bibliographic record
Abstract
The rapid development of micromanipulation technologies has opened exciting new opportunities for the actuation, selection and assembly of a variety of non-biological and biological nano/micro-objects for applications ranging from microfabrication, cell analysis, tissue engineering, biochemical sensing, to nano/micro-machines. To date, a variety of precise, flexible and high-throughput manipulation techniques have been developed based on different physical fields. Among them, optoelectronic tweezers (OET) is a state-of-art technique that combines light stimuli with electric field together by leveraging the photoconductive effect of semiconductor materials. Herein, the behavior of micro-objects can be directly controlled by inducing the change of electric fields on demand in an optical manner. Relying on this light-induced electrokinetic effect, OET offers tremendous advantages in micromanipulation such as programmability, flexibility, versatility, high-throughput and ease of integration with other characterization systems, thus showing impressive performance compared to those of many other manipulation techniques. A lot of research on OET have been reported in recent years and the technology has developed rapidly in various fields of science and engineering. This work provides a comprehensive review of the OET technology, including its working mechanisms, experimental setups, applications in non-biological and biological scenarios, technology commercialization and future perspectives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.003 |
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".