Synergizing microfluidics with soft robotics: A perspective on miniaturization and future directions
Bibliographic record
Abstract
Soft robotics has gone through a decade of tremendous progress in advancing both fundamentals and technologies. It has also seen a wide range of applications such as surgery assistance, handling of delicate foods, and wearable assistive systems driven by its soft nature that is more human friendly than traditional hard robotics. The rapid growth of soft robotics introduces many challenges, which vary with applications. Common challenges include the availability of soft materials for realizing different functions and the precision and speed of control required for actuation. In the context of wearable systems, miniaturization appears to be an additional hurdle to be overcome in order to develop truly impactful systems with a high user acceptance. Microfluidics as a field of research has gone through more than two decades of intense and focused research resulting in many fundamental theories and practical tools that have the potentials to be applied synergistically to soft robotics toward miniaturization. This perspective aims to introduce the potential synergy between microfluidics and soft robotics as a research topic and suggest future directions that could leverage the advantages of the two fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".