Sustainable carbon capture via halophilic and alkaliphilic cyanobacteria: the role of light and bicarbonate
Bibliographic record
Abstract
The two-step photosynthetic biogas upgrading process, which combines CO2 capture by carbonate solution and carbonate regeneration by using aquatic microbial oxygenic photoautotrophs (i.e., cyanobacteria, algae, and diatoms), may provide a potential alternative to the commercial routes used for gaseous biofuel upgrading. Such a process not only provides a green and low energy intensive biogas upgrading pathway but also converts CO2 in biogas into high value biomass. To improve the upgrading performance, the effects of light intensity and NaHCO3 concentration on the growth and the HCO3- transformation characteristics of halophilic and alkaliphilic Spirulina platensis were investigated in this study. Experimental results showed that the light attenuation of S. platensis culture was significant. Increasing light intensity up to 210 μmol m-2 s-1 effectively improved the S. platensis growth and photosynthetic pigment accumulation. S. platensis could grow in the range of 0.05 to 0.6 M NaHCO3, and a maximum biomass concentration of 1.46 g L-1 was achieved under an optimal growth condition of 0.1 M NaHCO3, which was 65.9% higher than at 0.05 M NaHCO3. Moreover, the bicarbonate utilization efficiency reached 42.0%. Finally, in a case study, a biogas stream at a flow rate of 800 m3 h-1 could generate biomass up to 344 kg h-1, corresponding an energy value of 5591 MJ h-1.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".