Versatile photo-sensing ability of paper based flexible 2D-Sb0.3Sn0.7Se2 photodetector and performance prediction with machine learning algorithm

• Published in: Optical materials 2024-06, Vol.152, p.115547, Article 115547
• Main Authors: Rawal, Kuntesh, Devendrabhai, Patel Dixita, Pataniya, Pratik, Jain, Prince, Joshi, Anand, Solanki, G.K., Tannarana, Mohit
• Format: Article
• Language: English
• Subjects: Flexible devices; K-nearest neighbor; Machine learning; Photodetector; Sono-chemical exfoliation; Tin diselenide
• ISSN: 0925-3467; 1873-1252
• DOI: 10.1016/j.optmat.2024.115547

Present report demonstrates the application of Sb0.3Sn0.7Se2 single crystal as a paper based flexible photodetector. Direct vapour transport grown bulk crystals of Sb0.3Sn0.7Se2 has been converted to nanosheets by chemical assisted exfoliation process. The paper-based photodetector is fabricated and the switching action is studied. Tuning of photodetector has been carried out by 670 nm laser illumination for different bias voltages. Temporal photo-response is also studied under polychromatic light with different intensities in vacuum and open environment. The low temperature stability of photodetector has been studied for temperature range 300 K–180 K. Experimental results are obtained in terms of time resolved photocurrent under different illumination and atmospheric conditions. The flexibility and stability are also examined in detail for fabricated detector. Overall, the results suggest the application of Sb0.3Sn0.7Se2 as a versatile flexible photodetector. Additionally, the machine learning (ML) model is trained and tested using an experimental photocurrent dataset that has a complex material design with variations in time, bias voltage, intensity, and temperature. The k-nearest neighbor algorithm exhibited outstanding performance, achieving the highest R2 value of 0.9986 when applied to a temperature dataset, with a test size of 0.4. Performance metrics such as mean absolute error and root mean squared error of various test sizes ranging from 0.4 to 0.6 are used to assess the model's accuracy and robustness in changing conditions. This comprehensive analysis not only establishes a platform for future experimental optimization of photodetector materials but also underscores the efficacy of ML regression techniques in developing high-performance photodetectors. [Display omitted] •Chemical assisted exfoliation process were used for the synthesis of SbSnSe2 nanosheets.•Synthesized nanosheets were decorated on paper for the fabrication of flexible photo-detector.•The photodetection application was demonstrated for different atmospheric conditions and different illuminations.•Low temperature stability of the detector was also studied.•KNN Machine learning model were applied to the experimental data for the prediction of photodetection behavior.