Active bacterial baths in droplets
Bibliographic record
Abstract
Suspensions of self-propelled objects represent a novel paradigm in colloidal science. In such "active baths," traditional concepts such as Brownian motion, fluctuation-dissipation relations, and work extraction from heat reservoirs, must be extended beyond the conventional framework of thermal baths. Unlike thermal baths, which are characterized by a single parameter, the temperature, the fundamental descriptors of an active bath remain elusive. Particularly relevant are confined environments, which are common conditions for bacteria in Nature and in microbioreactor devices. In this study, buoyant passive tracers are employed as generalized probes to extract the properties of an active bath comprising motile bacteria confined within a droplet. By describing the bacterial suspension as a colored noise acting on the tracer, we extract the temporal memory [Formula: see text] and characteristic intensity [Formula: see text] of such noise, finding that [Formula: see text] varies little across the explored experimental conditions and [Formula: see text] is positively correlated with bacterial concentration. Notably, we put forward the generalizing concept of "bath diffusivity," [Formula: see text], as a central predictor for the momentum transfer properties of this out-of-equilibrium situation. We show that [Formula: see text] scales linearly with bacterial concentration, modulated by a factor representing the role of confinement, expressed as the ratio of the confining radius to the probe radius. This finding, while still awaiting a complete theoretical explanation, offers insights into the transport or mixing properties of confined active baths and paves the way for a deeper understanding of active emulsions driven by confined active matter.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".