Photosynthetic conversion of carbon dioxide from cement production to microalgae biomass
Bibliographic record
Abstract
Abstract Production of microalgae is a potential technology for capturing and recycling carbon dioxide from cement kiln emissions. In this study, a process of selecting a suitable strain that would effectively utilize carbon dioxide and generate biomass was investigated. A down-selection screening method was applied to 28 strains isolated from the area surrounding a commercial cement plant. In laboratory-scale (1 L) continuous-mode chemostats, observed productivity was > 0.9 g L −1 d −1 for most strains studied. Chlorella sorokiniana (strain SMC-14M) appeared to be the most tolerant to cement kiln gas emissions in situ, delivered under control of a pH-stat system, and was down-selected to further investigate growth and biomass production at large-scale (1000 L) cultivation. Results demonstrated little variability in lipid, crude protein, and carbohydrate composition throughout growth between kiln-gas grown algal biomass and biomass produced with laboratory grade CO 2 . The growth rate at which the maximum quantity of CO 2 from the emissions is recycled also produced the maximum amount of the targeted biomass components to increase commercial value of the biomass. An accumulation of some heavy metals throughout its growth demonstrates the necessity to monitor the biomass cultivated with industrial flue gases and to carefully consider the potential applications for this biomass; despite its other attractive nutritional properties. Key points • Studied high biomass producing algal strains grown on CO 2 from cement flue gas. • Chlorella sorokiniana SMC-14M grew well at large scale, in situ on cement flue gas. • Demonstrated the resulting commercial potential of the cultured algal biomass.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".