Diversidad cultural y educación en Quebec
Bibliographic record
Abstract
A new class of bio-nano hybrid catalyst useable in downstream wastewater treatment was developed. We combined the sorption potentialities of Chlorella vulgaris microalgae with the photocatalytic properties of TiO<sub>2</sub> NPs in order to investigate unexplored synergistic effects that could push the algal remediation technology toward a more promising cost-effective balance. We exploited non-living C. vulgaris, which keeps the biosorption properties of the living microalgae, but greatly enhancing the overall processability. C. vulgaris biomass was coupled with TiO<sub>2</sub> NPs and the nanosols were then dried by means of a spray freeze drying (SFD) process able to produce highly reactive granules. A widespread physicochemical characterization supported the preparation and the performance evaluation, so highlighting the key-role of C. vulgaris/TiO<sub>2</sub> interaction at the colloidal state. Heavy metal adsorption, tested for copper ions, and photocatalytic activity, assessed for Rhodamine B (RhB) photodegradation, were evaluated as key performances. The results pointed out a positive synergistic effect for hybrid samples consistent with the enhancement of metal biosorption which ranges from 103 mg g<sup>-1</sup>, for pristine C. vulgaris, to about 4000 mg g<sup>-1</sup>, when the biomass was coupled with the inorganic nanophase. The photocatalytic activity was well preserved with a complete RhB conversion after 1 h and even advanced in presence of SiO<sub>2</sub>NPs into the inorganic counterpart, so increasing the kinetic constant from 8.70 to 10.7 10<sup>-2</sup> min<sup>-1</sup>. The results pave the way for the integration of these sorbent/photocatalytic hybrid materials into water remediation systems in an innovative sustainable design perspective.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".