Nutrient bioavailability and uptake by a cyanobacteria consortium cultivated at high pH and alkalinity
Bibliographic record
Abstract
Alkaliphilic microalgae and cyanobacteria have gained significant importance due to their robustness, high biomass productivity, and ability to efficiently capture carbon dioxide directly from the atmosphere. To grow these alkaliphiles under high pH (pH >10.4) and high alkalinity conditions (0.1 - 0.5 M), substantial amounts of nutrients are required, which could potentially increase the operating costs of cultivation and adversely affect its environmental footprint. One conceivable way of tackling this issue is by re-using the spent medium and supplementing only the depleted nutrients. To effectively re-use the spent medium, first it is important to understand the nutrient bioavailability and uptake by alkaliphiles. In this study, we have determined the bioavailability of nutrients (e.g. C, N, P, S, Mg, S, Ca, Fe, etc.) in a high pH (> 10.4) and alkalinity (0.5 M) medium. Our results show that –with the exception of Mg, Ca, and Fe– all the nutrients are in bioavailable form for microbial growth. The availability of Mg, Ca, and Fe is limited because of precipitate formation with carbonates and hydroxides. Additionally, we have also carried out cultivation experiments to determine biomass productivity, elemental composition, and stoichiometric formula based on nutrient uptake. The cyanobacterial cultures grew well without any inhibition and a maximum productivity of 153 mg-AFDW L -1 d -1 was achieved. The elemental composition of biomass suggested that Mg and Ca content in biomass is low, consistent with the limited availability of these elements during the growth. Finally, the derived stoichiometric equation resulted in the following chemical formula CH 1.75 N 0.17 O 0.41 P 0.003 .
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".