Automated and collision-free navigation of multiple micro-objects in obstacle-dense microenvironments using optoelectronic tweezers
Bibliographic record
Abstract
Automated parallel manipulation of multiple micro-objects with optoelectronic tweezers (OET) has brought significant research interests recently. However, the parallel manipulation of multiple objects in complex obstacle-dense microenvironment using OET technology based on negative dielectrophoresis (nDEP) remain a big technical challenge. In this work, we proposed an adaptive light pattern design strategy to achieve automated parallel OET manipulation of multiple micro-objects and navigate them through obstacles to target positions with high precision and no collision. We first developed a multi-micro-object parallel manipulation OET system, capable of simultaneous image processing and microparticles path planning. To overcome microparticle collisions caused by overlapping light patterns, we employed a novel adaptive light pattern design that can dynamically adjust the layout of overlapping light patterns according to surrounding environment, ensuring enough space for each microparticle and preventing unintended escapes from the OET trap. The efficacy of this approach has been verified through systematic simulations and experiments. Utilizing this strategy, multiple polystyrene microparticles were autonomously navigated through obstacles and microchannels to their intended destinations, demonstrating the strategy's effectiveness and potential for automated parallel micromanipulation of multiple microparticles in complex and confined microenvironments.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".