Shaken, and stirred: oscillatory segmented flow for controlled size-evolution of colloidal nanomaterials
Bibliographic record
Abstract
We introduce oscillatory segmented flow as a compact microfluidic format that accommodates slow chemical reactions for the solution-phase processing of colloidal nanomaterials. The strategy allows the reaction progress to be monitored at a dynamic range of up to 80 decibels (i.e., residence times of up to one day, equivalent to 720-14,400 times the mixing time) from only one sensing location. A train of alternating gas bubbles and liquid reaction compartments (segmented flow) was initially formed, stopped and then subjected to a consistent back-and-forth motion. The oscillatory segmented flow was obtained by periodically manipulating the pressures at the device inlet and outlet via square wave signals generated by non-wetted solenoid valves. The readily implementable format significantly reduced the device footprint as compared with continuous segmented flow. We investigated mixing enhancement for varying liquid segment lengths, oscillation amplitudes and oscillation frequencies. The etching of gold nanorods served as a case study to illustrate the utility of the approach for dynamic characterization and precise control of colloidal nanomaterial size and shape for 5 h. Oscillatory segmented flows will be beneficial for a broad range of lab-on-a-chip applications that require long processing times.
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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".