Modeling of Triphenyl Phosphate Surfactant Enhanced Drying of Polystyrene/p-Xylene Coatings Using Artificial Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The drying process of polymeric coatings, particularly in the presence of surfactants, poses a complex challenge due to its intricate dynamics involving simultaneous heat and mass transfer. This study addresses the inherent complexity by employing Artificial Neural Networks (ANNs) to model the surfactant-enhanced drying of poly(styrene)-p-xylene coatings. A substantial dataset of 16,258 experimentally obtained samples forms the basis for training the ANN model, showcasing the suitability of this approach when ample training data is available. The chosen single-layer feed-forward network with backpropagation adeptly captures the non-linear relationships within the drying data, providing a predictive tool with exceptional accuracy. Our results demonstrate that the developed ANN model achieves a precision level exceeding 99% in predicting coating weight loss for specified input values of time, surfactant amount, and initial coating thickness. The model’s robust generalization capability eliminates the need for additional experiments, offering reliable predictions for both familiar and novel conditions. Comparative analysis reveals the superiority of the ANN over the regression tree, emphasizing its efficacy in handling the intricate dynamics of polymeric coating drying processes. In conclusion, this study contributes a valuable tool for optimizing polymeric coating processes, reducing production defects, and enhancing overall manufacturing quality and cost-effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it