Algal biomass dual roles in phycoremediation of wastewater and production of bioenergy and value-added products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Algal biomass has been gaining attention over the last decades as it is versatile and can be used in different industries, such as wastewater treatment and bioenergy industries. Microalgae are mixotrophic microorganisms that have potential to utilize nitrogen and phosphate (nutrients) and remove organic matters from wastewater streams. Phycoremediation is an intriguing and cost-efficient technique to simultaneously remove heavy metals from wastewater while removing nutrients and organic matters. The cultivated and produced algal biomass can be a promising candidate and a sustainable feedstock to produce biofuels (e.g., biodiesel, bio-alcohol, and bio-oil) and value-added products such as biochar, glycerol, functional food, and pigments. The algae suspended cultivation systems, WSP and HRAP, are efficient methods for the wastewater treatment in shallow ponds with no mechanical aeration and less required energy consumption, but when a short HRT and minimum evaporation losses are key points in the algal cultivation the PBRs are recommended. It was reported that biosorption and bioaccumulation are the two promising techniques of phycoremediation. Studies showed that among the current processes of algal biomass conversion to biofuels, transesterification of algal lipids and pyrolysis of algal biomass were found to be the most efficient techniques. This review paper investigates the applications of algal biomass in the phycoremediation of wastewater, productions of bioenergy and value-added products by reviewing articles mainly published over the last five years. Graphical abstract
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it