REFERENCES
1. Wong, S.; Ngadi, N.; Inuwa, I. M.; Hassan, O. Recent advances in applications of activated carbon from biowaste for wastewater treatment: a short review. J. Clean. Prod. 2018, 175, 361-75.
2. Fan, Y.; Fowler, G. D.; Zhao, M. The past, present and future of carbon black as a rubber reinforcing filler - a review. J. Clean. Prod. 2020, 247, 119115.
3. Xu, X.; Ray, R.; Gu, Y.; et al. Electrophoretic analysis and purification of fluorescent single-walled carbon nanotube fragments. J. Am. Chem. Soc. 2004, 126, 12736-7.
4. Hou, Y.; Lu, Q.; Deng, J.; Li, H.; Zhang, Y. One-pot electrochemical synthesis of functionalized fluorescent carbon dots and their selective sensing for mercury ion. Anal. Chim. Acta. 2015, 866, 69-74.
5. Das, A.; Arefina, I. A.; Danilov, D. V.; et al. Chiral carbon dots based on L/D-cysteine produced via room temperature surface modification and one-pot carbonization. Nanoscale 2021, 13, 8058-66.
6. Wang, L.; Li, B.; Xu, F.; et al. Visual in vivo degradation of injectable hydrogel by real-time and non-invasive tracking using carbon nanodots as fluorescent indicator. Biomaterials 2017, 145, 192-206.
7. Boakye-Yiadom, K. O.; Kesse, S.; Opoku-Damoah, Y.; et al. Carbon dots: applications in bioimaging and theranostics. Int. J. Pharm. 2019, 564, 308-17.
8. Tao, S.; Zhou, C.; Kang, C.; et al. Confined-domain crosslink-enhanced emission effect in carbonized polymer dots. Light. Sci. Appl. 2022, 11, 56.
9. Liu, J.; Li, R.; Yang, B. Carbon dots: a new type of carbon-based nanomaterial with wide applications. ACS. Cent. Sci. 2020, 6, 2179-95.
10. Tao, H.; Yang, K.; Ma, Z.; et al. In vivo NIR fluorescence imaging, biodistribution, and toxicology of photoluminescent carbon dots produced from carbon nanotubes and graphite. Small 2012, 8, 281-90.
11. Sun, Y. P.; Zhou, B.; Lin, Y.; et al. Quantum-sized carbon dots for bright and colorful photoluminescence. J. Am. Chem. Soc. 2006, 128, 7756-7.
12. Su, Y.; Xie, M.; Lu, X.; et al. Facile synthesis and photoelectric properties of carbon dots with upconversion fluorescence using arc-synthesized carbon by-products. RSC. Adv. 2014, 4, 4839-42.
13. Zhao, Q. L.; Zhang, Z. L.; Huang, B. H.; Peng, J.; Zhang, M.; Pang, D. W. Facile preparation of low cytotoxicity fluorescent carbon nanocrystals by electrooxidation of graphite. Chem. Commun. 2008, 5116-8.
14. Qiao, Z. A.; Wang, Y.; Gao, Y.; et al. Commercially activated carbon as the source for producing multicolor photoluminescent carbon dots by chemical oxidation. Chem. Commun. 2010, 46, 8812-4.
15. Wu, Z. L.; Gao, M. X.; Wang, T. T.; Wan, X. Y.; Zheng, L. L.; Huang, C. Z. A general quantitative pH sensor developed with dicyandiamide N-doped high quantum yield graphene quantum dots. Nanoscale 2014, 6, 3868-74.
16. Zhu, H.; Wang, X.; Li, Y.; Wang, Z.; Yang, F.; Yang, X. Microwave synthesis of fluorescent carbon nanoparticles with electrochemiluminescence properties. Chem. Commun. 2009, 5118-20.
17. Yan, X.; Cui, X.; Li, L. S. Synthesis of large, stable colloidal graphene quantum dots with tunable size. J. Am. Chem. Soc. 2010, 132, 5944-5.
18. Chae, A.; Choi, Y.; Jo, S.; et al. Microwave-assisted synthesis of fluorescent carbon quantum dots from an A2/B3 monomer set. RSC. Adv. 2017, 7, 12663-9.
19. Shi, W.; Han, Q.; Wu, J.; et al. Synthesis mechanisms, structural models, and photothermal therapy applications of top-down carbon dots from carbon powder, graphite, graphene, and carbon nanotubes. Int. J. Mol. Sci. 2022, 23, 1456.
20. Yan, F.; Jiang, Y.; Sun, X.; Bai, Z.; Zhang, Y.; Zhou, X. Surface modification and chemical functionalization of carbon dots: a review. Mikrochim. Acta. 2018, 185, 424.
21. Wang, B.; Yu, J.; Sui, L.; et al. Rational design of multi-color-emissive carbon dots in a single reaction system by hydrothermal. Adv. Sci. 2020, 8, 2001453.
22. Zhu, S.; Song, Y.; Wang, J.; et al. Photoluminescence mechanism in graphene quantum dots: quantum confinement effect and surface/edge state. Nano. Today. 2017, 13, 10-4.
23. Dhyani, M.; Kumar, R. An intelligent Chatbot using deep learning with Bidirectional RNN and attention model. Mater. Today. Proc. 2021, 34, 817-24.
24. Xu, X.; Deng, J.; Cummins, N.; Zhang, Z.; Zhao, L.; Schuller, B. W. Exploring zero-shot emotion recognition in speech using semantic-embedding prototypes. IEEE. Trans. Multimedia. 2021, 24, 2752-65.
25. Ouni, A.; Royer, E.; Chevaldonné, M.; Dhome, M. Leveraging semantic segmentation for hybrid image retrieval methods. Neural. Comput. Appl. 2022, 34, 21519-37.
26. Birchler, C.; Khatiri, S.; Bosshard, B.; Gambi, A.; Panichella, S. Machine learning-based test selection for simulation-based testing of self-driving cars software. Empirical. Software. Eng. 2023, 28, 71.
27. Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL. Mater. 2016, 4, 053208.
28. Wahl, C. B.; Aykol, M.; Swisher, J. H.; Montoya, J. H.; Suram, S. K.; Mirkin, C. A. Machine learning-accelerated design and synthesis of polyelemental heterostructures. Sci. Adv. 2021, 7, eabj5505.
29. Dai, Y.; Zhang, Z.; Wang, D.; et al. Machine-learning-driven G-quartet-based circularly polarized luminescence materials. Adv. Mater. 2024, 36, e2310455.
30. Li, Y.; Zhu, R.; Wang, Y.; Feng, L.; Liu, Y. Center-environment deep transfer machine learning across crystal structures: from spinel oxides to perovskite oxides. npj. Comput. Mater. 2023, 9, 109.
31. Chen, Z.; Liu, Y.; Kang, Z. Diversity and tailorability of photoelectrochemical properties of carbon dots. Acc. Chem. Res. 2022, 55, 3110-24.
32. Kakhki, R. M.; Mohammadpoor, M. Machine learning-driven approaches for synthesizing carbon dots and their applications in photoelectrochemical sensors. Inorg. Chem. Commun. 2024, 159, 111859.
33. Duman, A. N.; Jalilov, A. S. Machine learning for carbon dot synthesis and applications. Mater. Adv. 2024, 5, 7097-112.
34. Tang, Y.; Xu, Q.; Zhu, P.; Zhu, R.; Wang, J. Utilizing machine learning to expedite the fabrication and biological application of carbon dots. Mater. Adv. 2023, 4, 5974-97.
35. Brust, M.; Kiely, C. J. Some recent advances in nanostructure preparation from gold and silver particles: a short topical review. Colloids. Surf. A. 2002, 202, 175-86.
36. Allahyari, M.; Pouriyeh, S.; Assefi, M.; et al. A brief survey of text mining: classification, clustering and extraction techniques. arXiv 2017, arXiv:1707.02919. Available online: https://doi.org/10.48550/arXiv.1707.02919. (accessed 27 Feb 2026).
37. Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; De Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE. 2016, 104, 148-75.
38. Zhang, R.; Zhang, J.; Chen, Q.; et al. A literature-mining method of integrating text and table extraction for materials science publications. Comput. Mater. Sci. 2023, 230, 112441.
39. Zhang, Y.; Xiao, G. Named entity recognition datasets: a classification framework. Int. J. Comput. Intell. Syst. 2024, 17, 71.
40. Veena, G.; Hemanth, R.; Hareesh, J. Relation extraction in clinical text using NLP based regular expressions. In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, July 05-06, 2019; IEEE; 2019. pp. 1278-82.
41. Abdelrazek, A.; Eid, Y.; Gawish, E.; Medhat, W.; Hassan, A. Topic modeling algorithms and applications: a survey. Inf. Syst. 2023, 112, 102131.
42. Segall, M. D.; Lindan, P. J. D.; Probert, M. J.; et al. First-principles simulation: ideas, illustrations and the CASTEP code. J. Phys. Condens. Matter. 2002, 14, 2717-44.
43. Pribram-Jones, A.; Gross, D. A.; Burke, K. DFT: a theory full of holes? Annu. Rev. Phys. Chem. 2015, 66, 283-304.
44. Gale, J. D. Semi-empirical methods as a tool in solid-state chemistry. Faraday. Discuss. 1997, 106, 219-32.
45. Parker, R. M.; Guidetti, G.; Williams, C. A.; et al. The self-assembly of cellulose nanocrystals: hierarchical design of visual appearance. Adv. Mater. 2018, 30, e1704477.
46. Hachmann, J.; Olivares-Amaya, R.; Atahan-Evrenk, S.; et al. The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2011, 2, 2241-51.
47. Kirklin, S.; Saal, J. E.; Meredig, B.; et al. The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies. npj. Comput. Mater. 2015, 1, 15010.
48. Jain, A.; Ong, S. P.; Hautier, G.; et al. Commentary: The materials Project: a materials genome approach to accelerating materials innovation. APL. Mater. 2013, 1, 011002.
49. Zhang, K.; Zhang, H. Predicting solute descriptors for organic chemicals by a deep neural network (DNN) using basic chemical structures and a surrogate metric. Environ. Sci. Technol. 2022, 56, 2054-64.
50. Tetko, I. V.; Gasteiger, J.; Todeschini, R.; et al. Virtual computational chemistry laboratory - design and description. J. Comput. Aided. Mol. Des. 2005, 19, 453-63.
51. Beckner, W.; Mao, C. M.; Pfaendtner, J. Statistical models are able to predict ionic liquid viscosity across a wide range of chemical functionalities and experimental conditions. Mol. Syst. Des. Eng. 2018, 3, 253-63.
52. Ramprasad, R.; Batra, R.; Pilania, G.; Mannodi-Kanakkithodi, A.; Kim, C. Machine learning in materials informatics: recent applications and prospects. npj. Comput. Mater. 2017, 3, 54.
53. Sun, Y.; Wong, A. K. C.; Kamel, M. S. Classification of imbalanced data: a review. Int. J. Pattern. Recognit. Artif. Intell. 2009, 23, 687-719.
54. Wang, L.; Han, M.; Li, X.; Zhang, N.; Cheng, H. Review of classification methods on unbalanced data sets. IEEE. Access. 2021, 9, 64606-28.
55. Sun, Z.; Ying, W.; Zhang, W.; Gong, S. Undersampling method based on minority class density for imbalanced data. Expert. Syst. Appl. 2024, 249, 123328.
56. Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 2002, 16, 321-57.
57. Li, D. C.; Liu, C. W.; Hu, S. C. A learning method for the class imbalance problem with medical data sets. Comput. Biol. Med. 2010, 40, 509-18.
58. Chen, A.; Cai, J.; Wang, Z.; Han, Y.; Ye, S.; Li, J. An ensemble learning classifier to discover arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction. J. Energy. Chem. 2023, 78, 268-76.
59. Xu, P.; Ji, X.; Li, M.; Lu, W. Small data machine learning in materials science. npj. Comput. Mater. 2023, 9, 42.
60. Ghazikhani, A.; Monsefi, R.; Yazdi, H. S. Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams. Neural. Comput. Appl. 2013, 23, 1283-95.
61. Wang, J.; Lu, S.; Wang, S. H.; Zhang, Y. D. A review on extreme learning machine. Multimedia. Tools. Appl. 2022, 81, 41611-60.
62. Ganin, Y.; Ustinova, E.; Ajakan, H.; et al. Domain-adversarial training of neural networks. In Domain Adaptation in Computer Vision Applications, Springer, 2017; pp. 189-209.
63. Yu, F.; Xiu, X.; Li, Y. A survey on deep transfer learning and beyond. Mathematics 2022, 10, 3619.
64. Liu, Z.; Wu, C. T.; Koishi, M. Transfer learning of deep material network for seamless structure - property predictions. Comput. Mech. 2019, 64, 451-65.
65. Liu, X.; Liu, Z.; Wang, G.; Cai, Z.; Zhang, H. Ensemble transfer learning algorithm. IEEE. Access. 2018, 6, 2389-96.
66. Liu, X.; Zhang, Y.; Xie, Y.; et al. Design of circularly polarized phosphorescence materials guided by transfer learning. Nat. Commun. 2025, 16, 4970.
67. Settles, B. Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, 2009. http://digital.library.wisc.edu/1793/60660. (accessed 2026-02-27).
68. Doolittle, P.; Wojdak, K.; Walters, A. Defining active learning: a restricted systemic review. Teach. Learn. Inq. 2023, 11.
69. Koizumi, A.; Deffrennes, G.; Terayama, K.; Tamura, R. Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science. Sci. Rep. 2024, 14, 27019.
70. Tian, Y.; Xue, D.; Yuan, R.; et al. Efficient estimation of material property curves and surfaces via active learning. Phys. Rev. Mater. 2021, 5, 013802.
71. Allotey, J.; Butler, K. T.; Thiyagalingam, J. Entropy-based active learning of graph neural network surrogate models for materials properties. J. Chem. Phys. 2021, 155, 174116.
72. He, T.; Zhang, S.; Xin, J.; et al. An active learning approach with uncertainty, representativeness, and diversity. ScientificWorldJournal 2014, 2014, 827586.
73. Noh, J.; Doan, H. A.; Job, H.; et al. An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations. Nat. Commun. 2024, 15, 2757.
74. Wang, X. Y.; Chen, B. B.; Zhang, J.; et al. Exploiting deep learning for predictable carbon dot design. Chem. Commun. 2021, 57, 532-5.
75. Hong, Q.; Wang, X. Y.; Gao, Y. T.; et al. Customized carbon dots with predictable optical properties synthesized at room temperature guided by machine learning. Chem. Mater. 2022, 34, 998-1009.
76. Xu, Q.; Tang, Y.; Zhu, P.; et al. Machine learning guided microwave-assisted quantum dot synthesis and an indication of residual H2O2 in human teeth. Nanoscale 2022, 14, 13771-8.
77. Tang, B.; Lu, Y.; Zhou, J.; et al. Machine learning-guided synthesis of advanced inorganic materials. Mater. Today. 2020, 41, 72-80.
78. Han, Y.; Tang, B.; Wang, L.; et al. Machine-learning-driven synthesis of carbon dots with enhanced quantum yields. ACS. Nano. 2020, 14, 14761-8.
79. Lan, Y.; Zheng, G. S.; Song, R. W.; et al. Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning. Nat. Commun. 2025, 16, 8167.
80. Zhang, Q.; Tao, Y.; Tang, B.; et al. Graphene quantum dots with improved fluorescence activity via machine learning: implications for fluorescence monitoring. ACS. Appl. Nano. Mater. 2022, 5, 2728-37.
81. Xing, C.; Chen, G.; Zhu, X.; et al. Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning. Nano. Res. 2024, 17, 1984-9.
82. Chen, J.; Luo, J. B.; Hu, M. Y.; Zhou, J.; Huang, C. Z.; Liu, H. Controlled synthesis of multicolor carbon dots assisted by machine learning. Adv. Funct. Mater. 2023, 33, 2210095.
83. Senanayake, R. D.; Yao, X.; Froehlich, C. E.; et al. Machine learning-assisted carbon dot synthesis: prediction of emission color and wavelength. J. Chem. Inf. Model. 2022, 62, 5918-28.
84. Muyassiroh, D. A. M.; Permatasari, F. A.; Hirano, T.; Ogi, T.; Iskandar, F. Machine learning-guided synthesis of room-temperature phosphorescent carbon dots for enhanced phosphorescence lifetime and information encryption. ACS. Appl. Nano. Mater. 2024, 7, 5465-75.
85. Guo, R.; Song, S. Y.; Cao, Q.; et al. Machine learning-driven achieving efficient phosphorescent carbon nanodots in aqueous solution by suppressing triplet electron leakage. Adv. Mater. 2025, 37, e2505925.
86. Luo, J. B.; Chen, J.; Liu, H.; Huang, C. Z.; Zhou, J. High-efficiency synthesis of red carbon dots using machine learning. Chem. Commun. 2022, 58, 9014-7.
87. Tuchin, V. S.; Stepanidenko, E. A.; Vedernikova, A. A.; et al. Optical properties prediction for red and near-infrared emitting carbon dots using machine learning. Small 2024, 20, e2310402.
88. Chen, J.; Zhang, M.; Xu, Z.; Ma, R.; Shi, Q. Machine-learning analysis to predict the fluorescence quantum yield of carbon quantum dots in biochar. Sci. Total. Environ. 2023, 896, 165136.
89. Pudza, M. Y.; Abidin, Z. Z.; Rashid, S. A.; Yasin, F. M.; Noor, A. S. M.; Issa, M. A. Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network. Processes 2019, 7, 704.
90. Yang, H.; Ran, Z.; Luo, Y.; et al. Exploration and design of carbon dot-based long afterglow materials using active machine learning and quantum chemical simulations. ACS. Nano. 2024, 18, 29203-13.
91. Guo, H.; Lu, Y.; Lei, Z.; et al. Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots. Nat. Commun. 2024, 15, 4843.
92. Li, T.; Cao, B.; Su, T.; et al. Machine learning-engineered nanozyme system for synergistic anti-tumor ferroptosis/apoptosis therapy. Small 2025, 21, e2408750.
93. He, H.; Shuang, E.; Ai, L.; et al. Exploiting machine learning for controlled synthesis of carbon dots-based corrosion inhibitors. J. Clean. Prod. 2023, 419, 138210.
94. Wang, X.; Wang, B.; Wang, H.; et al. Carbon-dot-based white-light-emitting diodes with adjustable correlated color temperature guided by machine learning. Angew. Chem. Int. Ed. 2021, 60, 12585-90.
95. Wang, X.; Bian, W.; Zhang, T.; et al. Highly crystalline core dominated the catalytic performance of carbon dot for cyclohexane to adipic acid reaction. Nano. Res. 2022, 15, 7662-9.
96. Wang, X.; Chen, S.; Ma, Y.; et al. Continuous homogeneous catalytic oxidation of C-H bonds by metal-free carbon dots with a poly(ascorbic acid) structure. ACS. Appl. Mater. Interfaces. 2022, 14, 26682-9.
97. Ostadhossein, F.; Moitra, P.; Alafeef, M.; et al. Ensemble and single-particle level fluorescent fine-tuning of carbon dots via positional changes of amines toward “supervised” oral microbiome sensing. J. Biomed. Opt. 2023, 28, 082807.
98. Xu, Z.; Wang, Z.; Liu, M.; Yan, B.; Ren, X.; Gao, Z. Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines. Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 2020, 232, 118147.
99. Pandit, S.; Banerjee, T.; Srivastava, I.; Nie, S.; Pan, D. Machine learning-assisted array-based biomolecular sensing using surface-functionalized carbon dots. ACS. Sens. 2019, 4, 2730-7.
100. Soares, A. C.; Soares, J. C.; Dos Santos, D. M.; et al. Nanoarchitectonic E-tongue of electrospun zein/curcumin carbon dots for detecting Staphylococcus aureusin milk. ACS. Omega. 2023, 8, 13721-32.
101. Shauloff, N.; Morag, A.; Yaniv, K.; et al. Sniffing bacteria with a carbon-dot artificial nose. Nanomicro. Lett. 2021, 13, 112.
102. Xu, Z.; Wang, K.; Zhang, M.; et al. Machine learning assisted dual-emission fluorescence/colorimetric sensor array detection of multiple antibiotics under stepwise prediction strategy. Sens. Actuators. B. 2022, 359, 131590.
103. Liu, Y.; Chen, J.; Xu, Z.; et al. Detection of multiple metal ions in water with a fluorescence sensor based on carbon quantum dots assisted by stepwise prediction and machine learning. Environ. Chem. Lett. 2022, 20, 3415-20.
104. Xu, Z.; Chen, J.; Liu, Y.; Wang, X.; Shi, Q. Multi-emission fluorescent sensor array based on carbon dots and lanthanide for detection of heavy metal ions under stepwise prediction strategy. Chem. Eng. J. 2022, 441, 135690.
105. Zhang, Y.; Liu, K.; Yu, J.; et al. Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification. Nano. Res. 2022, 15, 6399-406.
106. Cao, S.; Dong, S.; Chen, Y.; et al. Rapid fluorescence detection of black tea fermentation degree based on cobalt ion mediated carbon quantum dots. Food. Control. 2024, 165, 110610.
107. Döring, A.; Rogach, A. L. Utilizing deep learning to enhance optical sensing of ethanol content based on luminescent carbon dots. ACS. Appl. Nano. Mater. 2022, 5, 11208-18.
108. Tuccitto, N.; Fichera, L.; Ruffino, R.; et al. Carbon quantum dots as fluorescence nanochemosensors for selective detection of amino acids. ACS. Appl. Nano. Mater. 2021, 4, 6250-6.
109. Sarmanova, O. E.; Kudryashov, A. D.; Laptinskiy, K. A.; et al. Applications of fluorescence spectroscopy and machine learning methods for monitoring of elimination of carbon nanoagents from the body. Opt. Mem. Neural. Netw. 2023, 32, 20-33.
110. Liu, S.; Zhang, J.; Liu, X.; et al. Excitation wavelength as additional dimension in cross-reactive sensor arrays. Sens. Actuators. B. 2021, 344, 130183.
111. Döring, A.; Qiu, Y.; Rogach, A. L. Improving the accuracy of carbon dot temperature sensing using multi-dimensional machine learning. ACS. Appl. Nano. Mater. 2024, 7, 2258-69.
112. Zheng, Y.; Wang, X.; Guan, Z.; et al. Application of CD and Eu3+ dual emission MOF colorimetric fluorescent probe based on neural network in Fe3+ detection. Part. Part. Syst. Charact. 2022, 39, 2200124.
113. Zhang, M.; He, H.; Huang, Y.; et al. Machine learning integrated high quantum yield blue light carbon dots for real-time and on-site detection of Cr(VI) in groundwater and drinking water. Sci. Total. Environ. 2023, 904, 166822.
114. Yadav, N.; Mudgal, D.; Mishra, A.; Shukla, S.; Malik, T.; Mishra, V. Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification. PLoS. One. 2024, 19, e0296270.
115. Huang, R.; Zhou, Y.; Hu, J.; Peng, A.; Hu, W. Deep learning-assisted multicolor fluorescent probes for image and spectral dual-modal identification of illicit drugs. Sens. Actuators. B. 2023, 394, 134348.
116. Liu, T.; Chen, S.; Ruan, K.; et al. A handheld multifunctional smartphone platform integrated with 3D printing portable device: on-site evaluation for glutathione and azodicarbonamide with machine learning. J. Hazard. Mater. 2022, 426, 128091.
117. Lu, Z.; Chen, M.; Li, M.; et al. Smartphone-integrated multi-color ratiometric fluorescence portable optical device based on deep learning for visual monitoring of Cu2+ and thiram. Chem. Eng. J. 2022, 439, 135686.
118. Lu, Z.; Li, M.; Chen, M.; et al. Deep learning-assisted smartphone-based portable and visual ratiometric fluorescence device integrated intelligent gel label for agro-food freshness detection. Food. Chem. 2023, 413, 135640.
119. Lu, Z.; Chen, S.; Chen, M.; et al. Trichromatic ratiometric fluorescent sensor based on machine learning and smartphone for visual and portable monitoring of tetracycline antibiotics. Chem. Eng. J. 2023, 454, 140492.
120. Lu, Z.; Chen, M.; Liu, T.; et al. Machine learning system to monitor Hg2+ and sulfide using a polychromatic fluorescence-colorimetric paper sensor. ACS. Appl. Mater. Interfaces. 2023, 15, 9800-12.
121. Wang, F.; Xiao, M.; Qi, J.; Zhu, L. Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm. Anal. Bioanal. Chem. 2024, 416, 3139-48.
122. Thonghlueng, J.; Ngernpimai, S.; Chuaephon, A.; et al. Dual-responsive carbon quantum dots for the simultaneous detection of cytosine and 5-methylcytosine interpreted by a machine learning-assisted smartphone. ACS. Appl. Mater. Interfaces. 2023, 15, 40141-52.
123. Yen, Y. T.; Lin, Y. S.; Chang, Y. J.; Li, M. T.; Chyueh, S. C.; Chang, H. T. Nanomaterial-based sensor arrays with deep learning for screening of illicit drugs. Adv. Mater. Technol. 2022, 7, 2200243.
124. Doğan, V.; Evliya, M.; Nesrin Kahyaoglu, L.; Kılıç, V. On-site colorimetric food spoilage monitoring with smartphone embedded machine learning. Talanta 2024, 266, 125021.
125. Yu, J.; Yong, X.; Tang, Z.; Yang, B.; Lu, S. Theoretical understanding of structure-property relationships in luminescence of carbon dots. J. Phys. Chem. Lett. 2021, 12, 7671-87.
126. Salahinejad, M.; Sadjadi, S.; Abdouss, M. Investigating fluorescence quenching of cysteine-functionalized carbon quantum dots by heavy metal ions: experimental and QSPR studies. J. Mol. Liq. 2021, 334, 116067.
127. Roozbahani, A.; Salahinejad, M.; Gholipour, V. An exploratory in N-doped carbon dots as green fluorescence probes for Hg(II) ions detection. Environ. Technol. 2024, 45, 3612-20.
128. Bian, Z.; Bao, T.; Sun, X.; et al. Machine learning tools to assist the synthesis of antibacterial carbon dots. Int. J. Nanomedicine. 2024, 19, 5213-26.
129. Li, Y.; Chen, L.; Yang, S.; et al. Symmetry-triggered tunable phosphorescence lifetime of graphene quantum dots in a solid state. Adv. Mater. 2024, 36, e2313639.
130. Chen, L.; Yang, S.; Li, Y.; et al. Precursor symmetry triggered modulation of fluorescence quantum yield in graphene quantum dots. Adv. Funct. Mater. 2024, 34, 2401246.
