Highly Selective and Scalable Molecular Fluoride Sensor for Naked-Eye Detection

Fluoride is widely present in nature, and human exposure to it is generally regarded as inevitable. High levels of fluoride intake induce acute and chronic illnesses. To reduce potential harm to the general public, it is essential to create selective fluoride detectors capable of providing a colorimetric response for naked-eye detection without the need for sophisticated equipment. Here, we report a one-pot synthesis of four different diaminomaleonitrile-derived Schiff base sensors. The terephthalaldehyde adduct provided a strong color change visible to the naked eye at a F- concentration level as low as 2 ppm. From the evaluation against other anions, such as CN-, I-, Br-, Cl-, NO3-, PO43-, OAc-, and HSO4-, the molecular sensor displayed a visible color change exclusively upon exposure to fluoride, underscoring exceptional selectivity. As a key intermediate for understanding the mechanism, HF2- was confirmed by 19F nuclear magnetic resonance. Theoretical calculations suggested a deprotonation-triggered bathochromic shift brought about by the unique electronic structure of the sensor. Furthermore, the simple synthetic protocol from economically accessible materials allowed for the preparation of the compound on a large scale, rendering it a highly practical visual fluoride sensor.

Prediction of geogenic source of groundwater fluoride contamination in Indian states: A comparative study of different supervised machine learning algorithms

India has been dealing with fluoride contamination of groundwater for the past few decades. Long-term exposure of fluoride can cause skeletal and dental fluorosis. Therefore, an in-depth exploration of fluoride concentrations in different parts of India is desirable. This work employs machine learning algorithms to analyze the fluoride concentrations in five major affected Indian states (Andhra Pradesh, Rajasthan, Tamil Nadu, Telangana and West Bengal). A correlation matrix was used to identify appropriate predictor variables for fluoride prediction. The various algorithms used for predictions included K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector classifier (SVC), Gaussian NB, MLP classifier, decision tree classifier, gradient boosting classifier, voting classifier soft and voting classifier hard. The performance of these models is assessed over accuracy, precision, recall and error rate and receiver operating curve. As the dataset was skewed, the performance of models was evaluated before and after resampling. Analysis of results indicates that the RF model is the best model for predicting fluoride contamination in groundwater in Indian states.