The Effect of 3D Printing Tilt Angle on the Penetration of 3D-Printed Microneedle Arrays
Bibliographic record
Abstract
Microneedle arrays (MNAs) are emerging devices that are mainly used for drug delivery and diagnostic applications through the skin. Different methods have been used to fabricate MNAs. Recently developed fabrication methods based on 3D printing have many advantages compared to conventional fabrication methods, such as faster fabrication in one step and the ability to fabricate complex structures with precise control over their geometry, form, size, and mechanical and biological properties. Despite the several advantages that 3D printing offers for the fabrication of microneedles, their poor penetration capability into the skin should be improved. MNAs need a sharp needle tip to penetrate the skin barrier layer, the stratum corneum (SC). This article presents a method to improve the penetration of 3D-printed microneedle arrays by investigating the effect of the printing angle on the penetration force of MNAs. The penetration force needed to puncture the skin for MNAs fabricated using a commercial digital light processing (DLP) printer, with different printing tilt angles (0-60°), was measured in this study. The results showed that the minimum puncture force was achieved using a 45° printing tilt angle. Using this angle, the puncture force was reduced by 38% compared to MNAs printed with a tilting angle of 0°. We also identified that a tip angle of 120° resulted in the smallest penetration force needed to puncture the skin. The outcomes of the research show that the presented method can significantly improve the penetration capability of 3D-printed MNAs into the skin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".