Flow behavior of active fluids in a bifurcating microchannel
Bibliographic record
Abstract
Suspensions of photosynthetic microorganisms as "living", or "active" fluids play a crucial role in many biological, medical, and engineering applications. The properties of active fluids are fundamentally different from passive fluids. In passive fluids, the gradients of pressure, velocity, or temperature are driving forces for the flow, while in active fluids, such as bacterial suspensions, chemicals and/or light trigger cellular molecular motors to accomplish metabolic functions and navigate in the media, leading to a directed movement. Therefore, microorganisms in active fluids can create complicated and spontaneous fluid motions in the absence of external gradients. However, the presence of an external gradient may alter these spontaneous motions, a topic that requires further investigation. Therefore, in this study, Synechocystis sp., a unicellular species of cyanobacteria, was suspended in a fluid medium (BG11) to constitute the working active fluid. In order to investigate various delicate flow features of this active fluid, a bifurcating microchannel with a rectangular cross-section was designed and fabricated using Polydimethylsiloxane (PDMS). A micro-pump was used to drive the fluid at a specific flow rate into the microchannel. A high-magnification inverted optical video microscope captured the cells' behavior at a channel plane. The recorded video images were processed using a particle-imagevelocimetry algorithm available in PIVlab to obtain flow patterns for both fluids: the live and dead Synechocystis sp.
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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".