Low-cost fabrication of digital light processing 3D printed conical microneedles for biomedical applications
Bibliographic record
Abstract
• Ultra-low cost DLP printers can be used to create functional microneedle molds. • Parametric input studies can be used to optimise microneedle geometry. • DLP printing layer height changes microneedle surface roughness and drug-coating capacity. • DLP printed microneedles can successfully penetrate skin for drug delivery applications. The 3D printing of microneedles has become an emerging area of research focus due to its ability to rapidly create microneedle arrays with parametrically variable geometry and composition. Through direct fabrication or via replica molding, 3D printed microneedles can facilitate iterative array assessment and act as a screening tool to quickly establish optimal parameters for tissue insertion and capacity for biomarker monitoring or drug release. However, the widespread adoption of 3D printing by microneedle array researcher group faces multiple barriers: (1) Investment in 3D printing systems that are traditionally associated with high-resolution pose a significant financial cost, (2) Current high-resolution printing methods are often slow and not conducive to rapid manufacturing, (3) Material selection can be limited in some ‘proprietary’ 3D printing systems (e.g. CLIP, SLA, or 2PP), (4) Given the multidisciplinary nature of the microneedle-field, researchers may not have the expertise to optimize printing parameters for a given material or printer. This work explores how ultra-low-cost digital light processing (DLP) printers can rapidly produce functional microneedle arrays for a variety of purposes, whether direct print, master mold production, or creation of coated microneedles, at a fraction of the cost of currently used systems. Further, this work highlights that high quality microneedle arrays can be created using DLP printing methods with reliability exceeding 98 %, tip radii on the order of 30 µm, and with appropriate parameter input optimization, high quality microneedle arrays can be fabricated that express desirable characteristics for multiple forms of solid microneedle array production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".