Fluorinated Pickering Emulsions Impede Interfacial Transport and Form Rigid Interface for the Growth of Anchorage-Dependent Cells
Bibliographic record
Abstract
This study describes the design and synthesis of amphiphilic silica nanoparticles for the stabilization of aqueous drops in fluorinated oils for applications in droplet microfluidics. The success of droplet microfluidics has thus far relied on one type of surfactant for the stabilization of drops. However, surfactants are known to have two key limitations: (1) interdrop molecular transport leads to cross-contamination of droplet contents, and (2) the incompatibility with the growth of adherent mammalian cells as the liquid-liquid interface is too soft for cell adhesion. The use of nanoparticles as emulsifiers overcomes these two limitations. Particles are effective in mitigating undesirable interdrop molecular transport as they are irreversibly adsorbed to the liquid-liquid interface. They do not form micelles as surfactants do, and thus, a major pathway for interdrop transport is eliminated. In addition, particles at the droplet interface provide a rigid solid-like interface to which cells could adhere and spread, and are thus compatible with the proliferation of adherent mammalian cells such as fibroblasts and breast cancer cells. The particles described in this work can enable new applications for high-fidelity assays and for the culture of anchorage-dependent cells in droplet microfluidics, and they have the potential to become a competitive alternative to current surfactant systems for the stabilization of drops critical for the success of the technology.
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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) | 0.001 | 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".