Direct Incorporation of Lipophilic Nanoparticles into Monodisperse Perfluorocarbon Nanodroplets via Solvent Dissolution from Microfluidic-Generated Precursor Microdroplets
Bibliographic record
Abstract
Multifunctional medical agents based on imaging or therapy nanoparticles (NPs) incorporated into perfluorocarbon (PFC) droplets are promising new agents for cancer detection and treatment. For the first time, monodisperse PFC nanodroplets labeled with NPs have been produced. Lipophilic, as-synthesized, hydrocarbon-stabilized NPs are directly miscibilized into lipophobic PFCs using a removable cosolvent, diethyl ether (DEE), which eliminates the need of the typical time-consuming and expertise-specific NP surface modification steps previously required for NP incorporation into PFCs. This NP-DEE/PFC solution is then used to synthesize monodisperse, micrometer-scale, DEE-infused NP-PFC precursor droplets in water using microfluidics. After precursor microdroplet generation, the DEE cosolvent is removed by dissolution and evaporation, resulting in dramatically smaller, monodisperse, NP-labeled nanodroplets, with final droplet sizes far smaller than the minimum droplet size limit of the microfluidic system, and easily controlled by the amount of DEE mixed in the PFC phase prior to precursor droplet synthesis. Using this technique, unmodified lipophilic quantum dot (QD) NPs were integrated into monodisperse and PFC nanodroplets 165 times smaller in volume than the precursor microdroplets, with dimensions down to 470 nm. The final droplet sizes scaled with the PFC concentrations in the precursor microdroplets, and the QDs remain localized within the droplets after DEE is removed from the system. This method is robust and versatile, and it comprises a platform technology for other unmodified lipophilic NPs and molecules to be incorporated into different types of PFC droplets for the production of new NP-PFC hybrid agents for medical imaging and therapy applications.
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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".