Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles
Bibliographic record
Abstract
Feasibility of applying intelligent tools in prediction and optimization of photocatalytic degradation of beta‐naphthol using the titanium dioxide (TiO2) nanoparticles were conducted in this study. Biphasic TiO2 nanoparticles were synthesized using the controlled hydrolysis of TiCl4, and their properties were studied using the X‐ray diffraction and transmission electron microscopy methods. Therefore, factors affecting photocatalytic degradation of beta‐naphthol including impurity concentration, catalyst content, acidity, and aeration rate were monitored and controlled. The laboratory data showed that degradation rate of beta‐naphthol is a complicated nonlinear function of monitored variables. Two models including artificial network trained with particle swarm optimization (ANN‐PSO) and adaptive neuro‐fuzzy interference system trained with particle swarm optimization (ANFIS‐PSO) were used for prediction of this system. The results showed presence of a significant relation between the real and predicted data of these 2 models. However, ANFIS‐PSO can be more efficiently applied for prediction and optimization of photocatalytic behavior of TiO2 nanoparticles as for degradation of beta‐naphthol as compared to ANN‐PSO. As an advantage, ANFIS eliminates the problems of fuzzy logic, such as creation of membership functions, and local minima, which should be located in design of ANN, and through PSO algorithm, it could be a very powerful tool for simulating kinds of processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".