Synergy of robotics and microfluidics for intelligent micro- and nanomanipulation
Bibliographic record
Abstract
Micro- and nanomanipulation technology has found important applications in the fields of chemistry, materials, biology, and medicine. However, traditional manual techniques, constrained by the small size and fragile nature of target samples, often lack accuracy, efficiency, and throughput. Microfluidics has become a promising tool for handling micro- and nanoscale samples, addressing the above limitations. In particular, a growing number of innovations at the intersection of robotics and microfluidics have been proposed, showcasing the incredible potential in synergizing robotics and microfluidics technologies to develop fully automated and accurate systems for versatile micro- and nanomanipulation. In this Perspective, we discuss the ongoing research and development of robotics-enhanced microfluidics for micro- and nanomanipulation. We outline the key roles of major robotics technologies such as sensing, control, and artificial intelligence (AI) in microfluidic manipulation. We also propose the future directions of AI agents in microfluidic manipulation, aiming to achieve intelligent decision-making and execution across different manipulation tasks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".