Photocatalytic strategies for combating harmful algal blooms: synergistic approaches to multifactorial challenges
Bibliographic record
Abstract
Harmful algal blooms constitute a critical global environmental challenge for aquatic ecosystems, driven by complex synergistic interactions of eutrophication, climate change, and hydrodynamic conditions that severely threaten public health, ecological integrity, and fisheries economies. This review highlights photocatalysis as a sustainable alternative that overcomes the limitations of traditional methods in terms of energy consumption and secondary pollution. It provides an in-depth evaluation of the evolution of photocatalytic technology as a sustainable solution, tracing its development from recyclable floating catalysts to functionally designed materials. We emphasize how metal–organic frameworks and perovskite oxides enable electronic and structural control, thereby enhancing visible-light activity and charge separation efficiency. We also explore the transition from material design to system-level integration, exemplified by photoelectrocatalytic systems, where spatial separation of redox reactions significantly improves efficiency and stability. Finally, we analyze the key challenges in material durability, ecological safety, and engineering and economic barriers to system scaling for practical applications. We construct a “lab-to-field” governance framework to provide forward-looking strategies for the practical translation and scaling of photocatalytic technologies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".