Optoelectronic tweezers: a versatile toolbox for nano-/micro-manipulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it