Visible Light Driven MnO <sub>2</sub> /MnS Hybrid Nanostructures for Photocatalytic Degradation of Organic Pollutants: A Synergistic Approach for Wastewater Treatment
Bibliographic record
Abstract
Abstract The environment is being threatened by rapid industrialization and water pollution. This research is focused on the synthesis of MnO 2 /MnS binary nanocomposites by using a hydrothermal process and tested as an effective photocatalyst when degrading Rhodamine B (RhB) dye in the presence of sunlight. UV–visible spectroscopy, FTIR, XRD, SEM, and EDX were used in structural and optical characterizations. The band gap was found to be 2.33 eV, and the average crystallite size was 17 nm of the nanocomposite. During photocatalytic degradation, the optimization of various factors such as catalyst dosage, oxidant dosage, pH, time, and concentration of dye was carried out. It is found that MnO 2 /MnS nanocomposite performed excellently and achieved 93% degradation of RhB after 120 min at pH 4, catalyst amount (20 mg/50 mL), and an oxidant dosage of 15 mM. Different radical scavengers were used to find the radical tappers (OH · , e − and h + ) that were effective in the degradation mechanism under sunlight. Kinetics was used, and the reaction proceeded in a first‐order model. Response surface methodology was used for identifying the interaction between various variables. It has been confirmed that MnO 2 /MnS nanocomposites can be efficiently applied in many other real‐world applications of wastewater treatment under solar irradiation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".