Flow Chemistry to Control the Synthesis of Nano and Microparticles for Biomedical Applications
Bibliographic record
Abstract
In this article we review the flow chemistry methodologies for the controlled synthesis of different kind of nano and microparticles for biomedical applications. Injection mechanism has emerged as new alternative for the synthesis of nanoparticles due to this strategy allows achieving superior levels of control of self-assemblies, leading to higher-ordered structures and rapid chemical reactions. Self-assembly events are strongly dependent on factors such as the local concentration of reagents, the mixing rates, and the shear forces, which can be finely tuned, as an example, in a microfluidic device. Injection methods have also proved to be optimal to elaborate microsystems comprising polymer solutions. Concretely, extrusion based methods can provide controlled fluid transport, rapid chemical reactions, and cost-saving advantages over conventional reactors. We provide an update of synthesis of nano and microparticles such as core/shell, Janus, nanocrystals, liposomes, and biopolymeric microgels through flow chemistry, its potential bioapplications and future challenges in this field are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".