A method for the rapid fabrication of solid metal microneedles
Bibliographic record
Abstract
Microneedles are an emerging technology that offer an alternative to traditional hypodermic needles for drug and vaccine delivery. Less than 1 mm in length, microneedles can penetrate the skin with little to no pain making them a suitable option for the 1-in-10 people that may avoid seeking medical care due to needle phobia. However, there are significant challenges with adapting existing microneedle fabrication methods for large scale manufacturing while matching the repeatability, reliability, and cost of current hypodermic needle mass production processes. In this work we present a novel method of fabricating microneedles using a modified automated wire bonding process that is highly suited for mass production due to existing widespread use of this process and equipment in the semiconductor industry. Microneedle arrays of different densities were fabricated on FR-4 based printed circuit board substrates using this automated process and tested by inserting into porcine skin tissue to determine insertion forces. The required insertion force generally increased with increasing array density due to the “bed of nails” effect and decreased with increasing insertion speed due to the viscoelastic properties of porcine skin tissue. Characterizing the correlations between insertion force, insertion speed, and array density are important for designing microneedle-based devices and applicators that can reliably penetrate skin. Microneedle arrays were also successfully created by automated wire bonding on polyimide-based printed circuit boards to demonstrate that this process can be done on flexible substrates. Further investigation with larger samples sizes is required to expand on the preliminary findings of this work.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".