Lipid-based nanoparticle production in Micromixers
Bibliographic record
Abstract
Lipid-based nanoparticles have demonstrated to be a versatile vehicle for drugs, genetic material, and labels. These particles are often made of biocompatible and biodegradable materials, enabling a safe interaction with biological systems. The importance of this type of delivery vehicle has been shown recently, as the two leading vaccines are based on lipid-nanoparticles encapsulating mRNA. Passive micromixers produce lipid nanoparticles in a reproducible and controllable way. However, micromixers suffered at the beginning of low production rate, and complicated designs which were difficult to produce and prone to clogging. In recent years, the exploration of different mixing strategies based on the use of curvilinear paths to induce centripetal forces and vortex formation at high speeds as well as the increase of the microchannel cross-sectional area while keeping laminar flow regimes has led to designs capable of producing lipid-based nanoparticles on an industrial-scale. However, there are still challenges in the field which include the removal or substitution of the organic solvents that still need to be addressed. In this presentation, we introduce a general overview of lipid nanoparticle or liposome production in micromixers, the principles of mixing using curvilinear paths, the key variables controlling lipid-based nanoparticle physicochemical characteristics and approaches that help to substitute toxic solvent residues.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".