Innovative and sustainable strategies for algal bloom mitigation and water quality enhancement
Bibliographic record
Abstract
Industrial activities increasingly release toxic pollutants into water bodies, threatening ecological and human health. Harmful algal blooms (HABs) are a critical concern, often resistant to traditional water treatment methods, highlighting the need for innovative, eco-friendly solutions. This study evaluates advanced materials, including layered Fe 3 O 4 @ZIF8, core-shell Fe 3 O 4 @ZIF8, FeCN, BiOBr, and CuBDC, to inhibit bloom-forming algae under visible and UV light. BiOBr demonstrated superior performance at low concentrations, effectively inactivating Microcystis aeruginosa. Chlorophyll pigment and phycobiliprotein content analysis revealed its mechanism of action. As a cost-effective and sustainable solution, BiOBr offers promise for mitigating HABs, protecting ecosystems, and enhancing water quality. This research highlights the transformative potential of novel materials in addressing global water pollution challenges.
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 | 0.001 | 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) | 0.000 | 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".