Physical and Rheological Properties of Active Fluids Under Shear Stress: Suspensions of Synechocystis
Bibliographic record
Abstract
Abstract Design of photobioreactors (PBRs) for microalgae and cyanobacteria cultivation involves the interplay between fluid flow, microbe biokinetics, and radiative transport phenomena, in which the physical and rheological properties of the active fluid play a crucial role. In this study, we focus on the variation of physical and rheological properties of dilute suspensions of Synechocystis sp. CPCC534 with the shear stress applied to the fluid. Experiments were carried out at three different stirring rates in well-controlled conditions and the results were compared with stationary conditions where only molecular diffusion and cell motility govern the transport phenomena, and cell growth. Our results show that the growth and biomass production of Synechocystis sp. under various shear conditions were improved significantly, and the yield was nearly doubled by adding agitation to the system. The viscosity of Synechocystis suspensions, subjected to different shear stress levels, was measured with two different methods. The viscosity data showed shear stress independent Newtonian behavior. However, the viscosity of Synechocystis suspensions increased moderately with cell volume fraction up to 10%, beyond which it increased more rapidly. The shear stress history of the cell suspensions did not show any effect on the fluid viscosity.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".