3D shape evolution of microparticles and 3D enabled applications using non-uniform UV flow lithography (NUFL)
Bibliographic record
Abstract
The generation of microparticles with non-spherical morphologies has generated extensive interest because of their enhanced physical properties that can increase their performance in a wide variety of clinical and industrial applications. A flow lithographic technique based on stop flow lithography (SFL) recently showed the ability to fabricate particles with 3D shapes via manipulation of the UV intensity profile in a simple 2D microfluidic channel. Here, we further explore this flow lithographic method, called non-uniform flow lithography (NUFL), to investigate the 3D-shape tuning ability for the generation of 3D magnetic microparticles and their potential applications. We characterize the morphological microparticle shape change through variation of polymerization objective, UV intensity, and solution opacity. We also couple the particles' intrinsic anisotropic magnetic properties with an external magnetic field to create chains of bullet- and bell-shaped particles and a valve-like micromachine. In addition, in contrast to other complex and multi-step methodologies, NUFL shows a simple route for the facile creation of 3D microstructure platforms such as microneedles with fully modifiable tip morphology. This method presents intriguing possibilities for growing research within 3D microstructure assembly, micromachine systems and minimally invasive medical interventions.
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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 | 0.000 | 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".