Photocatalytic degradation on Sulphur–Nitrogen Co-Doped Fe <sub>2</sub> O <sub>3</sub> surface and enhanced nanostructure design using RERNN-FFO approach for methylene blue adsorption
Bibliographic record
Abstract
Fe2O3 is an exceptional substance that possesses distinct properties, including high stability, oxidising power, affordability, environmental friendliness, availability, and some visible light qualities. The paper presents a unique technique for the Design of Nanostructure Surface for Adsorption and Photo catalysis of Methylene Blue termed Hybrid RERNN-FFO in order to overcome this problem. The proposed hybrid technique is the joint execution of both the Recalling Enhanced Recurrent Neural Network (RERNN) and Flying Foxes Optimization (FFO). Hence, it is named as RERNN-FFO. The major objective of the proposed technique is to accurately predict the dye removal effectiveness. The RERNN is utilized to predict the efficiency of the dye removal and eliminate its dependency on neuron count and FFO is utilized to optimize the RERNN’s parameters. The proposed strategy was executed in the MATLAB platform and compared with other existing strategies like Crystal Graph Convolutional Neural Network (CGCNN), Deep Neural Network (DNN)and Particle Swarm Optimization (PSO). The proposed method is more efficient than current approaches and achieves an impressive 99% dye removal efficiency. The findings indicate that, in comparison to alternative methods, this strategy reduces MSE by 0.048% and increases R-squared by 0.94%, demonstrating its superior performance.
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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 | 0.000 | 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".