Growth and pigment production of <i>Synechocystis</i> sp. <scp>PCC</scp> 6803 under shear stress
Bibliographic record
Abstract
Abstract Cyanobacteria, such as Synechocystis , have recently become attractive hosts for sustainable production of biofuels and bio‐fixation of CO 2 due to their genetic tractability and relatively fast growth. Cultivation of cyanobacteria requires shear stress, which is generated by mixing and air bubbling. In the present work, the impact of shear stress caused by stirring and air bubbling on the growth and pigment production of Synechocystis sp. PCC 6803 is investigated. For this purpose, agitated and airlift bubble column photobioreactors were used. The results showed that the growth and yield production were improved by mixing the culture system. However, there is a limit to this improvement: In the case of air bubbling, increasing shear stress (by rising air bubbling flow rate) to more than 185 mPa did not show any significant growth enhancement, while increasing the shear stress from 40 to 185 mPa improved the yield production up to 85%. At the optimal stirring rate, the yield production in the stirred photobioreactors increased by about 60% as compared to that of unstirred culture. The measurements of chlorophyll a and carotenoid showed a strong correlation between biomass production and total pigment content. The highest level of cellular pigment (pigment per cell) was detected at the early stages of culture growth when cells were preparing for the rapid exponential growth phase.
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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) | 0.000 | 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".