Bioflocculants’ production in a biomass-degrading bacterium using untreated corn stover as carbon source and use of bioflocculants for microalgae harvest
Bibliographic record
Abstract
Bioflocculation has been developed as a cost-effective and environment-friendly method to harvest multiple microalgae. However, the high production cost of bioflocculants makes it difficult to scale up. In the current study, low-cost bioflocculants were produced from untreated corn stover by a biomass-degrading bacterium Pseudomonas sp. GO2. Pseudomonas sp. GO2 showed excellent production ability of bioflocculants through directly hydrolyzing various biomasses. The untreated corn stover was selected as carbon source for bioflocculants’ production due to its highest flocculating efficiency compared to that when using other biomasses as carbon source. The effects of fermentation parameters on bioflocculants’ production were optimized via response surface methodology. According to the optimal model, an ideal flocculating efficiency of 99.8% was obtained with the fermentation time of 130.46 h, initial pH of 7.46, and biomass content of 0.64%. The relative importance of carboxymethyl cellulase and xylanase accounted for 51.8% in the process of bioflocculants’ production by boosted regression tree analysis, further indicating that the bioflocculants were mainly from the hydrolysates of biomass. Biochemical analysis showed that it contained 59.0% polysaccharides with uronic acid (34.2%), 32.1% protein, and 6.1% nucleic acid in the bioflocculants, which had an average molecular weight as 1.33 × 106 Da. In addition, the bioflocculants showed the highest flocculating efficiency at a concentration of 12.5 mg L−1 and were stable over broad ranges of pH and temperature. The highest flocculating efficiencies obtained for Chlorella zofingiensis and Neochloris oleoabundans were 77.9 and 88.9%, respectively. The results indicated that Pseudomonas sp. GO2 can directly utilize various untreated lignocellulolytic biomasses to produce low-cost bioflocculants, which showed the high efficiency to harvest two green microalgae in a low GO2 fermentation broth/algal culture ratio.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".