Electrochemical sensors and biosensors based on nanodiamonds: A review

Silva, Luiz; Carvalho, Jefferson H.S.; Stefano, Jéssica S.; Oliveira, Geiser Gabriel; Prakash, Jai; Janegitz, Bruno C.

doi:10.1016/j.mtcomm.2023.106142

Cited by 5 publications

(1 citation statement)

References 68 publications

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“…The development of efficient organic luminescent materials is crucial for high-per formance organic light-emitting diodes [1][2][3], biological probes [4,5], and chemical sensors [6][7][8]. Organic luminescent materials have attracted extensive attention from researchers in various fields due to their intriguing biocompatibility, structural diversity, and ease of property tuning [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

Bi,

Jiang,

Chen

et al. 2024

Materials

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The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials.

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