A New Angle on Pluronic Additives: Advancing Droplets and Understanding in Digital Microfluidics
Bibliographic record
Abstract
Biofouling in microfluidic devices limits the type of samples which can be handled and the duration for which samples can be manipulated. Despite the cost of disposing fouled devices, relatively few strategies have been developed to tackle this problem. Here, we have analyzed a series of eight amphiphilic droplet additives, Pluronic coblock polymers of poly(propylene oxide) (PPO) and poly(ethylene oxide) (PEO), as a solution to biofouling in digital microfluidics using serum-containing cell culture media as a model fluid. Our analysis shows that species with longer PPO chains are superior for enabling droplet motion and reducing biofouling. Two of the tested species, L92 and P105, were found to lengthen device lifetimes by 2-3 times relative to additives used previously when used at optimal concentrations. Pluronics with low PEO content such as L92 were found to be cytotoxic to an immortalized mammalian cell line, and therefore we recommend that Pluronic additives with greater or equal to 50% PEO composition, such as P105, be used for digital microfluidic applications involving cells. Finally, contact angle measurements were used to probe the interaction between Pluronic-containing droplets and device surfaces. Strong correlations were found between various types of contact angle measurements and the capacity of additives to reduce biofouling, which suggests that contact angle measurements may be useful as a tool for rapidly screening new candidates for the potential to reduce biofouling. We propose that this study will be useful for scientists and engineers who are developing digital microfluidic platforms for a wide range of applications involving protein-containing solutions, and in particular, for applications involving cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".