Photocatalytic Remediation of Harmful Alexandrium minutum Bloom Using Hybrid Chitosan-Modified TiO2 Films in Seawater: A Lab-Based Study
Bibliographic record
Abstract
The uncontrolled growth of harmful algal blooms (HABs) can negatively impact the environment and pose threats to human health and aquatic ecosystems. Titanium dioxide (TiO2) is known to be effective in killing harmful algae through flocculation and sedimentation. However, TiO2 in a dispersed form can harm other non-target marine organisms, which has raised concerns by environmentalists and scientists. This research seeks to explore the utility of immobilized titanium oxide as a photocatalyst for mitigation of HABs, where the Alexandrium minutum bloom was used as a model system herein. Chitosan was modified with 0.2 wt.% TiO2 (Chi/TiO2 (x mL; x = 1, 3 and 5 mL) and the corresponding films were prepared via solvent casting method. Scanning electron microscope (SEM) images of the films reveal a highly uneven surface. X-ray diffraction (XRD) analysis indicates the reduction in chitosan crystallinity, where the presence of TiO2 was negligible, in accordance with its dispersion within the chitosan matrix. The photocatalytic mitigation of A.minutum was carried out via a physical approach in a laboratory-scale setting. The negative surface charge of the films was observed to repel the negatively charged A.minutum causing fluctuation in the removal efficiency (RE). The highest RE (76.1 ± 13.8%) was obtained when Chi/TiO2 (1 mL) was used at 72 h, where the hydroxyl radicals generated were inferred to contribute to the deactivation of the algae cells by causing oxidative stress. An outcome of this study indicates that such hybrid films have the potential to replace the non-immobilized (dispersed) TiO2 for HAB mitigation. However, further investigation is required to deploy these films for field applications at a larger scale.
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 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) | 0.000 | 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 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".