Microalgae Recovery from Water for Biofuel Production Using CO<sub>2</sub>-Switchable Crystalline Nanocellulose
Bibliographic record
Abstract
There is a pressing need to develop efficient and sustainable approaches to harvesting microalgae for biofuel production and water treatment. CO2-switchable crystalline nanocellulose (CNC) modified with 1-(3-aminopropyl)imidazole (APIm) is proposed as a reversible coagulant for harvesting microalgae. Compared to native CNC, the positively charged APIm-modified CNC, which dispersed well in carbonated water, showed appreciable electrostatic interaction with negatively charged Chlorella vulgaris upon CO2-treatment. The gelation between the modified CNC, triggered by subsequent air sparging, can also enmesh adjacent microalgae and/or microalgae-modified CNC aggregates, thereby further enhancing harvesting efficiencies. Moreover, the surface charges and dispersion/gelation of APIm-modified CNC could be reversibly adjusted by alternatively sparging CO2/air. This CO2-switchability would make the reusability of redispersed CNC for further harvesting possible. After harvesting, the supernatant following sedimentation can be reused for microalgal cultivation without detrimental effects on cell growth. The use of this approach for harvesting microalgae presents an advantage to other current methods available because all materials involved, including the cellulose, CO2, and air, are natural and biocompatible without adverse effects on the downstream processing for biofuel production.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".