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Record W4289730972 · doi:10.3389/fbioe.2022.935902

Bioreactor as the root cause of the “manganese effect” during Aspergillus niger citric acid fermentations

2022· article· en· W4289730972 on OpenAlexaff
Erzsébet Fekete, Vivien Bíró, Alexandra Márton, István Bakondi-Kovács, Zoltán Németh, Erzsébet Sándor, Béla Kovács, István Fábián, Christian P. Kubicek, Adrian Tsang, Levente Karaffa

Bibliographic record

VenueFrontiers in Bioengineering and Biotechnology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsConcordia University
FundersNemzeti Kutatási, Fejlesztési és Innovaciós Alap
KeywordsCitric acidManganeseAspergillus nigerBioreactorLeaching (pedology)FermentationChemistryMetal ions in aqueous solutionNuclear chemistryMetalFood scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

) 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.002
GPT teacher head0.184
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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