Fabrication of Highly Porous Nonspherical Particles Using Stop-Flow Lithography and the Study of Their Optical Properties
Bibliographic record
Abstract
A microfluidic flow lithography approach was investigated to synthesize highly porous nonspherical particles and Janus particles in a one-step and high-throughput fashion. In this study, using common solvents as porogens, we were able to synthesize highly porous particles with different shapes using ultraviolet (UV) polymerization-induced phase separation in a microfluidic channel. We also studied the pore-forming process using operating parameters such as porogen type, porogen concentration, and UV intensity to tune the pore size and increase the pore size to submicron levels. By simply coflowing multiple streams in the microfluidic channel, we were able to create porous Janus particles; we showed that their anisotropic swelling/deswelling exhibit a unique optical shifting. The distinctive optical properties and the enlarged surface area of the highly porous particles can improve their performance in various applications such as optical sensors and drug loading.
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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".