Bionanotechnology and bioMEMS (BNM): state-of-the-art applications, opportunities, and challenges
Bibliographic record
Abstract
The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".