Application of response surface methodology for the optimization of the production of electro-activated solutions in a three-cell reactor
Bibliographic record
Abstract
This study examines the possibility of producing optimally electro-activated (EA) solutions using response surface methodology (RSM). RSM yielded models to predict solution properties as functions of the process parameters studied with very high coefficients of determination (R2 = 0.9393–0.9974), indicating the models provided high correlation between observed and predicted values. The effect of production parameters, such as salt concentration, current and exposure time on the properties of the solutions, was investigated. For oxidation–reduction potential, pH and resistance, salt concentration was the most significant factor followed by exposure time and current. Using a Derringer's function, the best performance for the production of electro-activated solution was obtained at a current intensity of 200 mA in the presence of 0.05 M electrolyte after 38 and 60 min of treatment time, depending on which reactor configuration was used. The current investigation shows an agreement with the addition test of predicted data and confirms that the developed model equation can be used for prediction.
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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) | 0.000 | 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 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".