1. Additional file 1 of Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging
- Author
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Zhang, Yibin, Fan, Miaozhuang, Xu, Zhourui, Jiang, Yihang, Ding, Huijun, Li, Zhengzheng, Shu, Kaixin, Zhao, Mingyan, Feng, Gang, Yong, Ken-Tye, Dong, Biqin, Zhu, Wei, and Xu, Gaixia
- Abstract
Scheme S1. The synthetic route to prepare PTMM, TTNA, and TTBI. Figure S1 1 H NMR spectrum of PM. Figure S2 1 H NMR spectrum of PTMM. Figure S3 13 C NMR spectrum of PTMM. Figure S4. LC-MS spectrum of PTMM. Figure S5 1 H NMR spectrum of TTA. Figure S6 1 H NMR spectrum of TTNA. Figure S7 13 C NMR spectrum of TTNA. Figure S8. MALDI-TOF-MS spectrum of TTNA. Figure S9 1 H NMR spectrum of TTBI. Figure S10. 13 C NMR spectrum of TTBI. Figure S11. LC-MS spectrum of TTBI. Figure S12. The absorption spectrum of AIEgens in different solvents. Figure S13. PL spectra of AIEgens of AIEgens with different water fractions. Table S1. Comparison of absorption and emission peak between experimental and ML predicted. Table S2. Particle size of AIEgens NPs. Table S3. Zeta potentials of AIEgens NPs. Figure S14. Calculated LUMO and HOMO of PTMM, TTNA, and TTBI. Figure S15. Z-stack images of phantom of PTMM NPs. Figure S16. Z-stack images of phantom of TTNA NPs. Figure S17. Z-stack images of phantom of TTBI NPs. Figure S18. Experimental and predicted data are compared using 10-fold cross-validation. Figure S19. ML prediction error distribution. Figure S20. Model scalability. Figure S21. Comparison of ML accuracy and TD-DFT. Figure S22. Illustration of 10-fold cross-validation.
- Published
- 2023
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