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
Why this work is in the frame
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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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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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 it