Deep-Learning-Assisted Discriminative Detection of Vitamin B and Vitamin B by Fluorescent MoSe Quantum Dots.

A facile and environmentally mindful approach for the synthesis of MoSe QDs was developed via the hydrothermal method from bulk MoSe. In this, the exfoliation of MoSe was enhanced with the aid of an intercalation agent (KOH), which could reduce the exfoliation time and increase the exfoliation efficiency to form MoSe QDs. We found that MoSe QDs display blue emission that is suitable for different applications. This fluorescence property of MoSe QDs was harnessed to fabricate a dual-modal sensor for the detection of both vitamin B (VB) and vitamin B (VB), employing fluorescence quenching. We performed a detailed study on the fluorescence quenching mechanism of both analytes. The predominant quenching mechanism for VB is via Förster resonance energy transfer. In contrast, the recognition of VB primarily relies on the inner filter effect. We applied an emerging and captivating approach to pattern recognition, the deep-learning method, which enables machines to "learn" patterns through training, eliminating the need for explicit programming of recognition methods. This attribute endows deep-learning with immense potential in the realm of sensing data analysis. Here, analyzing the array-based sensing data, the deep-learning technique, "convolution neural networks", has achieved 93% accuracy in determining the contribution of VB and VB.