Fabrication of sharp silicon hollow microneedles by deep-reactive ion etching towards minimally invasive diagnostics
Bibliographic record
Abstract
Microneedle technologies have the potential for expanding the capabilities of wearable health monitoring from physiology to biochemistry. This paper presents the fabrication of silicon hollow microneedles by a deep-reactive ion etching (DRIE) process, with the aim of exploring the feasibility of microneedle-based in-vivo monitoring of biomarkers in skin fluid. Such devices shall have the ability to allow the sensing elements to be integrated either within the needle borehole or on the backside of the device, relying on capillary filling of the borehole with dermal interstitial fluid (ISF) for transporting clinically relevant biomarkers to the sensor sites. The modified DRIE process was utilized for the anisotropic etching of circular holes with diameters as small as 30 μm to a depth of >300 μm by enhancing ion bombardment to efficiently remove the fluorocarbon passivation polymer. Afterward, isotropic wet and/or dry etching was utilized to sharpen the needle due to faster etching at the pillar top, achieving tip radii as small as 5 μm. Such sharp microneedles have been demonstrated to be sufficiently robust to penetrate porcine skin without needing any aids such as an impact-insertion applicator, with the needles remaining mechanically intact after repetitive penetrations. The capillary filling of DRIE-etched through-wafer holes with water has also been demonstrated, showing the feasibility of use to transport the analyte to the target sites.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".