esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmentally Friendly Green \(O\)-Alkylation Reaction for Ethenzamide Synthesis
###### Abstract  
Ethenzamide (2-ethoxybenzamideamide), besides acetylsalicylic acid, is one of the mostly used salicylic acid derivatives in pharmaceuticals. It has analgesic and anti-inflammatory effects that originate from the inhibition of cyclooxygenase (COX-1) activity, thus blocking prostaglandin synthesis. In this work, efficient and eco-friendly methods were developed for the synthesis of ethenzamide via the \(O\)-alkylation reaction of salic-lamide. The reactions were carried out under conventional conditions in a solvent-free system using variant solvents and different phase transfer catalysts (PTC) in the presence of microwave radiation or ultrasonic conditions. It was shown that in solvent-free conditions using TBAB as a catalyst, ethenzamide can be obtained within 15 min at 80 \({}^{\circ}\)C with 79% yield. Meanwhile, using microwave radiation under the same conditions, the reaction time can be shortened to 90 s with 92% yield. Notably, high yields can be achieved under PTC in water (or organic solvent-free) conditions using microwave radiation (2 min, 94%) or ultrasound (10 min, 95% efficiency). The studies prove that the PTC synthesis process of ethenzamide can be conducted under mild conditions, with a shorter reaction time and remarkably lower energy consumption in comparison to conventional processes, thus actualizing \"green chemistry\" for practical ethenzamide preparation.  
O-alkylation; green synthesis; salicamide; ethenzamide; phase transfer catalysts (PTC); solvent-free synthesis; microwave-assisted synthesis; sonochemical synthesis +
Footnote †: journal: applied sciences  
Article
## 2 Material and Methods
### Machine Learning Classification
#### 2.3.1 Data Split for Training and Testing  
For classification analysis, the dataset needs to be divided into test and train sets. Splitting the dataset helps to evaluate the performance of the model on unseen data. The training set will allow the model to fit and adjust the weights of the model while the test set evaluates the model performance on the new dataset, prevent overfitting, and ensures the model's generalizability. As the _stress-predict_ dataset is imbalanced (more baseline readings than stress readings), the standard classification techniques focus on minimizing the error rate and ignore the minority class. Furthermore, the random split of the imbalanced data might have negligible or no data from the minority class, thus resulting in biased classification results. The solution to the problem is the use of a stratified k-fold classification split. Stratified sampling ensures that splitting is randomly performed and that the same imbalance class distribution is maintained for each subset (fold). Thus, to obtain an unbiased model performance, stratified 10-fold cross-validation was implemented.