Bioreactor as the root cause of the “manganese effect” during Aspergillus niger citric acid fermentations
Bibliographic record
Abstract
) is a key feature. Technical-scale production of citric acid predominantly uses stainless-steel tank fermenters, but glass bioreactors used for strain improvement and manufacturing process development also contain stainless steel components, in which manganese is an essential alloying element. We show here that during citric acid fermentations manganese (II) ions were leaching from the bioreactor into the growth media, resulting in altered fungal physiology and morphology, and significant reduction of citric acid yields. The leaching of manganese (II) ions was dependent on the fermentation time, the acidity of the culture broth and the sterilization protocol applied. Manganese (II) ion leaching was partially mitigated by electrochemical polishing of stainless steel components of the bioreactor. High concentrations of manganese (II) ions during early cultivation led to a reduction in citric acid yield. However, the effect of manganese (II) ions on the reduction of citric acid yield diminished towards the second half of the fermentation. Since maintaining low concentrations of manganese (II) ions is costly, the results of this study can potentially be used to modify protocols to reduce the cost of citric acid production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".