Artificial neural network analysis of titanium dissolution kinetics in a sustainable DL-malic acid and sodium fluoride system: a fundamental study using the rotating disc method
Bibliographic record
Abstract
This investigation presents a comprehensive kinetic analysis of titanium dissolution utilising DL-malic acid (a 50/50 mix of D- and L- isomer off malic acid) in conjunction with sodium fluoride solution, offering an innovative alternative to conventional chloride and sulphate methodologies. The experimental protocol employed a rotating disc apparatus to elucidate dissolution kinetics under systematically varied parameters, including angular velocity (rad/min), disc surface area (cm²), temperature (°C), and molar concentrations of DL-malic acid and sodium fluoride. A sophisticated Artificial Neural Network (ANN) architecture, implementing back-propagation methodology through the Levenberg-Marquardt algorithm with a multilayer {6-10-1} configuration, was developed to predict titanium dissolution behavior. Experimental findings demonstrated that sodium fluoride concentration predominantly influenced dissolution kinetics, manifesting a chemical reaction order of 0.674. The investigation substantiated the theoretical framework of the Levich equation within the rotating disc paradigm. The ANN model demonstrated exceptional predictive capability, achieving correlation coefficients (R²) of 0.995, 0.994, 0.996, and 0.995 for training, validation, testing, and aggregate datasets. The experimentally determined activation energy of 23 kJ/mol conclusively indicated a diffusion-controlled reaction mechanism, providing fundamental insights into the mass transfer phenomena governing the dissolution process.
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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) | 0.000 | 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".