Effects of Turbulent Mixing and Orbitally Shaking on Cell Growth and Biomass Production in Active Fluids
Bibliographic record
Abstract
and optimization of bioreactors are being impacted by several essential factors including availability of light and nutrients, pH, mixing, and temperature distribution [7].Among these parameters, mixing is the core factor in creating a homogenous environment AbstractWe have previously reported the effects of mixing on bacterial growth.Mixing can be achieved with different methods and in diverse devices.However, the nature of the intimate contact (the main purpose of mixing) made between different substances in a mixing vessel is a strong function of the hydrodynamic that underlies the specific mixing method.This study aimed to compare the growth and biomass yield of cyanobacterium Synechosystis sp.CPCC 534 (as a microorganism model) obtained by three different mixing methods commonly used in life science labs and in the industry, i.e., turbulent stirring (TS), orbital shaking (OS), and simple molecular diffusion (MD).The results revealed that imposing mixing on the culture significantly improved the specific growth rate as well as biomass yield production in comparison with simple molecular diffusion.Mixing obtained by turbulent stirring proved to be more efficient than the one achieved by orbitally shaking, in the production of Chlorophyll a (Chl a ) and phycocyanin (PC).The results of this study can help choosing the appropriate mixing method in life science research.
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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 | 0.001 |
| 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 | 0.000 |
| 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".