Droplet-on-chip electro-spectroscopy detects the ultra-short relaxation time of a dilute polymer solution
Bibliographic record
Abstract
We report an electrode-embedded on-chip platform technology for the precise determination of ultra-short (of the order of a few nanoseconds) relaxation times of dilute polymer solutions, by deploying time-alternating electrical voltages. Our methodology delves into the sensitive dependence of the contact line dynamics of a droplet of the polymer solution atop a hydrophobic interface in response to the actuation voltage, resulting in a non-trivial interplay between the time-evolving electrical, capillary, and viscous forces. This culminates into a time-decaying dynamic response that mimics the features of a damped oscillator having its 'stiffness' mapped with the polymeric content of the droplet. The observed electro-spreading characteristics of the droplet are thus shown to correlate explicitly with the relaxation time of the polymer solution, drawing analogies with a damped electro-mechanical oscillator. By corroborating well with the reported values of the relaxation times as obtained from more elaborate and sophisticated laboratory set-ups. Our findings provide perspectives for a unique and simple approach towards electrically-modulated on-chip-spectroscopy for deriving ultra-short relaxation times of a broad class of viscoelastic fluids that could not be realized thus far.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".