Toward sustainable phycocyanin production using halo-alkaliphilic cyanobacteria: from direct air capture of carbon dioxide to biorefinery
Bibliographic record
Abstract
, also known as "Spirulina." Its production is costly and faces sustainability challenges due to water needs, carbon dioxide emissions, and lack of operational stability. Here, we review the use of halo-alkaliphilic cyanobacteria to overcome these challenges. This review synthesizes conceptual innovations that were proposed and tested experimentally previously, resulting in the presentation of a complete bioprocess for phycocyanin production. These innovations are: (1) the use of a pH above 10.5 to implement direct air capture of carbon dioxide, reducing carbon dioxide emissions; (2) the use of a consortium of an alkaliphilic cyanobacterium and its associated heterotrophs for improved process stability; (3) the use of passive fermentation for phycocyanin extraction, thereby reducing water needs; and (4) the use of anaerobic digestion to recover energy and recycle carbon dioxide and nutrients. Integrating the above approaches could offer a potentially scalable, more sustainable alternative to conventional phycocyanin production, aligning with circular bioeconomy goals. Several challenges still require solutions. For example, despite water savings, water losses associated with direct air capture of carbon dioxide remain high, and nutrient recycling is only partially successful so far.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".