Autofermentation of alkaline cyanobacterial biomass to enable biorefinery approach
Bibliographic record
Abstract
BACKGROUND: Carbon capture using alkaliphilic cyanobacteria can be an energy-efficient and environmentally friendly process for producing bioenergy and bioproducts. The inefficiency of current harvesting and downstream processes, however, hinders large-scale feasibility. The high alkalinity of the biomass also introduces extra challenges, such as potential corrosion, inhibitory effects, or contamination of the final products. Thus, it is critical to identify low cost and energy-efficient downstream processes. RESULTS: Autofermentation was investigated as an energy-efficient and low-cost biomass pre-treatment method to reduce pH to levels suitable for downstream processes, enabling the conversion of cyanobacterial biomass into hydrogen and organic acids using cyanobacteria's own fermentative pathways. Temperature, initial biomass concentration, and oxygen presence were found to affect yield and distribution of organic acids. Autofermentation of alkaline cyanobacterial biomass was found to be a viable approach to produce hydrogen and organic acids simultaneously, while enabling the successful conversion of biomass to biogas. Between 5.8 and 60% of the initial carbon was converted into organic acids, 8.7-25% was obtained as soluble protein, and 16-72% stayed in the biomass. Interestingly, we found that extensive dewatering is not needed to effectively process the alkaline cyanobacterial biomass. Using natural settling as the only harvesting and dewatering method resulted in a slurry with relatively low biomass concentration. Nevertheless, autofermentation of this slurry led to the maximum total organic acid yield (60% C mol/C mol biomass) and hydrogen yield (326.1 µmol/g AFDM). CONCLUSION: Autofermentation is a simple, but highly effective pretreatment that can play a significant role within a cyanobacterial-based biorefinery platform by enabling the conversion of alkaline cyanobacterial biomass into organic acids, hydrogen, and methane via anaerobic digestion without the addition of energy or chemicals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".