Machine Learning-Assisted Optimization and Application of Carbon-Based Emitters

Bo Lian; Xianzhi Song; Jiameng Du; Runqing Fan; Linjuan Yang; Wenran Zhang; Yang Zhang; Xiaohong Li; Yunchao Li; Lin Shen; Louzhen Fan

Abstract

Machine learning (ML) is progressively being integrated into materials science, exhibiting great potential for optimizing chemical synthesis and structural regulation, thereby accelerating intelligent design and efficient exploration of novel materials. Carbon-based emitters (CBEs), as emerging functional materials, have attracted considerable attention due to their tunable photoluminescence properties, abundant precursor sources, and structural versatility. To date, extensive research on CBEs has generated a substantial data foundation, laying the groundwork for ML driven structural screening and property prediction. Although the application of ML in CBEs remains in its early stages compared to that of inorganic semiconductors and organic-inorganic hybrid perovskites, its potential to accelerate material screening and reveal structure-property relationships is becoming increasingly evident. Given the transformative role ML has played in other functional materials, it is expected to drive a paradigm shift in CBEs research from conventional trial-and-error approaches to data-driven, intelligence-guided design, substantially accelerating material discovery and expanding their functional applications. Therefore, this review systematically summarizes recent advances in ML applications in CBEs, focusing on the general ML workflow, property prediction and structural design strategies for organic small-molecules and carbon dots (CDs), and their applications in organic light-emitting diodes (OLEDs), quantum dot light-emitting diodes (QLEDs), information encryption, biomedicine, and sensing. Finally, this review discusses the key challenges currently facing the field and offers perspectives on future directions for ML-driven CBEs research, aiming to provide guidance for the rational design and efficient development of such materials.

References

1.
Jumper, J.; Evans, R.; Pritzel, A.; et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596, 583–589.
2.
Gao, D.; Lu, S.; Zhang, C.; et al. Autonomous closed-loop framework for reproducible perovskite solar cells. Nature 2026, 653, 707–714.
3.
Baranwal, B.S.; Jones, L.O.; Maind, A.; et al. The AI Revolution in Chemistry: Shaping the Future of Materials and Biomedical Sciences. ChemRxiv 2025. https://doi.org/10.26434/chemrxiv-2025-8lrf0.
4.
Chen, C.; Lin, X.; Wu, X.-G.; et al. Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes. Nano Lett. 2023, 23, 5738–5745.
5.
Thapa, K.; Jones, J.I.; Ratcliff, L.E. A high throughput virtual screening approach for identifying new thermally activated delayed fluorescence-based emitters. J. Mater. Chem. C 2025, 13, 19212–19225.
6.
Tao, H.; Wu, T.; Aldeghi, M.; et al. Nanoparticle synthesis assisted by machine learning. Nat. Rev. Mater. 2021, 6, 701–716.
7.
Ahmadi, M.; Ziatdinov, M.; Zhou, Y.; et al. Machine learning for high-throughput experimental exploration of metal halide perovskites. Joule 2021, 5, 2797–2822.
8.
Shi, Y.; Xu, H.; Yuan, T.; et al. Carbon dots: An innovative luminescent nanomaterial. Aggregate 2021, 3, 2692–4560.
9.
Yuan, T.; Yuan, F.; Sui, L.; et al. Carbon Quantum Dots with Near-Unity Quantum Yield Bandgap Emission for Electroluminescent Light-Emitting Diodes. Angew. Chem. Int. Ed. 2023, 62, e202218568.
10.
Li, M.-Z.; Tan, H.-J.; Liang, Q.-R.; et al. Versatile Dual-Emissive Boron-Nitrogen Co-Doped carbon Dots: Unlocking efficient electroluminescent Light-Emitting diodes with breakthrough EQE Approaching 7%. Chem. Eng. J. 2025, 514, 163146.
11.
Teng, Q.; Tan, Q.; Hou, M.; et al. Thermally Activated Sensitized Fluorescence Enables Efficient Carbon Quantum Dot-Based Electroluminescent LEDs with Current Efficiency of 31 cd A⁻¹. Angew. Chem. Int. Ed. 2025, 64, e202508650.
12.
Wei, S.; Ma, P.; Song, X.; et al. High-brightness electroluminescent white light-emitting diodes based on solution-processable, yellow-emissive carbon quantum dots. J. Mater. Chem. C 2025, 13, 16151–16158.
13.
Song, X.; Li, C.; Li, J.; et al. High-color-purity carbon-based light-emitting materials. Trends Chem. 2024, 6, 525–539.
14.
Yuan, T.; Zhao, G.; Tang, D.; et al. Symmetric Boron-Bridged Carbon Quantum Frameworks for Light-Emitting Diodes with over 20% External Quantum Efficiency. J. Am. Chem. Soc. 2025, 147, 28504–28512.
15.
Tang, Y.; Xu, Q.; Zhu, P.; et al. Utilizing machine learning to expedite the fabrication and biological application of carbon dots. Mater. Adv. 2023, 4, 5974–5997.
16.
Yu, H.; Ai, L.; Lu, S. Customized NIR-II Dual-Phase Carbon Dots: Elucidating the Emission Mechanism for Smart Photonic Applications. Angew. Chem. Int. Ed. 2026, 138, e8078052.
17.
Hao, W.; Wang, X.; Chen, X.; et al. Carbon Dots: An Emerging Frontier for Green and Sustainable Civil Engineering Materials. Energy Environ. Mater. 2026, 9, e70240.
18.
Chen, B.B.; Gao, Y.T.; Liu, M.L.; et al. Smart luminescent nanoclusters with dynamic covalent bond for reversible information encryption. Smart Mol. 2025, 3, e20240053.
19.
Shi, Y.; Wang, Z.; Meng, T.; et al. Red Phosphorescent Carbon Quantum Dot Organic Framework-Based Electroluminescent Light-Emitting Diodes Exceeding 5% External Quantum Efficiency. J. Am. Chem. Soc. 2021, 143, 18941–18951.
20.
Han, Y.; Tang, B.; Wang, L.; et al. Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields. ACS Nano 2020, 14, 14761–14768.
21.
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.
22.
Kang, L.; Wang, J.; Lin, X.; et al. A Full-Color Carbon Quantum Dots Fluorescence Sensing Array Combined with Machine Learning for Rapid Bacterial Detection and Classification. Adv. Healthc. Mater. 2025, 15, e02916.
23.
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.
24.
Han, J.H. Artificial neural network based inverse design of organic light emitting diode structures for optimized optical properties. Eng. Appl. Artif. Intell. 2026, 163, 112969.
25.
Pan, M.; Tan, T.; Ouyang, Y.; et al. Generative AI-powered inverse design for tailored narrowband molecular emitters. Digit. Discov. 2025, 4, 2942–2953.
26.
Shengcao, Y.; Qin, X.; Wang, Q.; et al. Deep learning-driven innovation in metallic materials: A comprehensive review on microstructure analysis, property prediction, and inverse design. J. Mater. Sci. Technol. 2026, 271, 91–108.
27.
Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 2015, 521, 452–459.
28.
Azevedo, B.F.; Rocha, A.M.A.C.; Pereira, A.I. Hybrid approaches to optimization and machine learning methods: A systematic literature review. Mach. Learn. 2024, 113, 4055–4097.
29.
Wang, Z.; Sun, Z.; Yin, H.; et al. Data-Driven Materials Innovation and Applications. Adv. Mater. 2022, 34, 2104113.
30.
Krishnamurthy, D.; Weiland, H.; Barati Farimani, A.; et al. Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization. ACS Energy Lett. 2019, 4, 187–191.
31.
Muyassiroh, D.A.M.; Permatasari, F.A.; Iskandar, F. Machine learning-driven advanced development of carbon-based luminescent nanomaterials. J. Mater. Chem. C 2022, 10, 17431–17450.
32.
Shi, Y.; Sun, M.; Shi, H.; et al. Integrating AI into OLED material design: A comprehensive review of computational frameworks, challenges, and opportunities. Sci. Bull. 2025, 70, 3058–3089.
33.
Kusne, A.G.; McDannald, A.; DeCost, B. Learning material synthesis-process-structure-property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning. Digit. Discov. 2024, 3, 2211–2225.
34.
Chen, H.; Chen, J.; Ding, J. Data Evaluation and Enhancement for Quality Improvement of Machine Learning. IEEE Trans. Reliab. 2021, 70, 831–847.
35.
Zhang, Y.; Ma, Q.; Gao, S.; et al. Ratiometric Determination and Discrimination of Oxicams via Dual-Excitation Carbon Dots Assisted by Machine Learning. Anal. Chem. 2025, 97, 21428–21437.
36.
Rodríguez-Martínez, X.; Pascual-San-José, E.; Campoy-Quiles, M. Accelerating organic solar cell material’s discovery: High-throughput screening and big data. Energy Environ. Sci. 2021, 14, 3301–3322.
37.
Orosz, Á.; Héberger, K.; Rácz, A. Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets. Front. Chem. 2022, 10, 852893.
38.
Özkan, C.; Sahlmann, L.; Würger, T.; et al. Gaining scientific understanding with small data machine learning: Explainable molecule representations and their consensus. NPJ Mater. Degrad. 2025, 9, 157.
39.
Huang, L.; Zhang, X.; Li, S.; et al. Text mining-assisted machine learning prediction and experimental validation of emission wavelengths. NPJ Comput. Mater. 2026, 12, 98.
40.
Yan, F.; Bai, R.; Huang, J.; et al. Machine learning-assisted carbon dots synthesis and analysis: State of the art and future directions. TrAC Trends Anal. Chem. 2025, 184, 118141.
41.
Dou, B.; Zhu, Z.; Merkurjev, E.; et al. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem. Rev. 2023, 123, 8736–8780.
42.
Liu, J.; Sun, S.; Tan, Z.; et al. Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest. Spectrochim. Acta Part A 2020, 242, 118718.
43.
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, 2310402.
44.
Bi, H.; Jiang, J.; Chen, J.; et al. Machine learning prediction of quantum yields and wavelengths of aggregation-induced emission molecules. Materials 2024, 17, 1664.
45.
Pereira Ribeiro Teodoro, L.; Estevão, R.; Santana, D.C.; et al. Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning. Forests 2024, 15, 39.
46.
Sun, M.; Fu, C.; Su, H.; et al. Enhancing chemistry-intuitive feature learning to improve prediction performance of optical properties. Chem. Sci. 2024, 15, 17533–17546.
47.
Jain, A. Machine learning in materials research: Developments over the last decade and challenges for the future. Curr. Opin. Solid State Mater. Sci. 2024, 33, 101189.
48.
Kaba, S.-O.; Groleau-Paré, B.; Gauthier, M.-A.; et al. Prediction of large magnetic moment materials with graph neural networks and random forests. Phys. Rev. Mater. 2023, 7, 044407.
49.
Yeung, C.; Tsai, R.; Pham, B.; et al. Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning. Adv. Opt. Mater. 2021, 9, 2100548.
50.
Yuan, T.; Song, X.; Shi, Y.; et al. Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age. Chem. Asian J. 2024, 19, e202301088.
51.
Hung, S.-H.; Ye, Z.-R.; Cheng, C.-F.; et al. Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach. J. Chem. Theory Comput. 2023, 19, 4559–4567.
52.
Mahato, K.D.; Das, S.S.G.K.; Azad, C.; et al. Stokes shift prediction of fluorescent organic dyes using machine learning based hybrid cascade models. Dyes Pigm. 2024, 222, 111918.
53.
Luo, J.B.; Chen, J.; Liu, H.; et al. High-efficiency synthesis of red carbon dots using machine learning. Chem. Commun. 2022, 58, 9014–9017.
54.
Shi, H.; Shi, Y.; Zeng, G.; et al. DFT-enhanced machine learning for accurate PLQY prediction and inverse design of novel MR-TADF materials. Chem. Eng. J. 2025, 525, 170146.
55.
Tan, H.-J.; Yu, J.-R.; Lin, Z.-Z.; et al. The role of a small molecular dipole moment for efficient non-doped deep blue thermally activated delayed fluorescence emitters. Chem. Eng. J. 2024, 481, 148567.
56.
Li, C.; Li, J.; Teng, Q.; et al. Single Solid-State Emissive Carbon Quantum Dots for Multicolor, Bright and Efficient Electroluminescent Light-Emitting Diodes. Angew. Chem. 2025, 137, e202419983.
57.
Li, H.; Sun, C.; Zhang, M.; et al. Multifunctional Thermally Activated Delayed Fluorescence Carbon Dots for Temperature-Responsive Sensor, Information Encryption, and Organelle Imaging. Adv. Funct. Mater. 2024, 34, 2405669.
58.
Shi, Y.; Su, W.; Teng, Q.; et al. Opportunity and application of chiral carbon dots. Matter 2023, 6, 2776–2806.
59.
Yuan, F.; Yuan, T.; Sui, L.; et al. Engineering triangular carbon quantum dots with unprecedented narrow bandwidth emission for multicolored LEDs. Nat. Commun. 2018, 9, 2249.
60.
Liu, M.; Dou, L.; Qu, Y.; et al. Carbon Dots with Aggregation-Dependent Photoluminescence and Application in Rewritable Water-Jet Printing. Aggregate 2026, 7, e70356.
61.
Shi, Y.; Su, W.; Yuan, F.; et al. Carbon Dots for Electroluminescent Light-Emitting Diodes: Recent Progress and Future Prospects. Adv. Mater. 2023, 35, 2210699.
62.
Gómez-Bombarelli, R.; Aguilera-Iparraguirre, J.; Hirzel, T.D.; et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 2016, 15, 1120–1127.
63.
Li, P.; Wang, Z.; Li, W.; et al. Design of Thermally Activated Delayed Fluorescence Materials with High Intersystem Crossing Efficiencies by Machine Learning-Assisted Virtual Screening. J. Phys. Chem. Lett. 2022, 13, 9910–9918.
64.
Shi, H.; Shi, Y.; Liang, Z.; et al. Machine learning-enabled discovery of multi-resonance TADF molecules: Unraveling PLQY predictions from molecular structures. Chem. Eng. J. 2024, 494, 153150.
65.
Kim, H.S.; Cheon, H.J.; Lee, S.H.; et al. Advancing efficiency in deep-blue OLEDs: Exploring a machine learning–driven multiresonance TADF molecular design. Sci. Adv. 2025, 11, eadr1326.
66.
Chen, Z.; Song, J.; Hu, L.; et al. Structure-aware machine learning for predicting photophysical properties of MR-TADF materials. Chin. Chem. Lett. 2025, 37, 111967.
67.
Wang, X.-Y.; Chen, B.-B.; Zhang, J.; et al. Exploiting deep learning for predictable carbon dot design. Chem. Commun. 2021, 57, 532–535.
68.
Chen, J.; Luo, J.B.; Hu, M.Y.; et al. Controlled Synthesis of Multicolor Carbon Dots Assisted by Machine Learning. Adv. Funct. Mater. 2022, 33, 2210095.
69.
Jeong, M.; Joung, J.F.; Hwang, J.; et al. Deep learning for development of organic optoelectronic devices: Efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs. NPJ Comput. Mater. 2022, 8, 147.
70.
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, 12585–12590.
71.
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, 29203–29213.
72.
Yan, Z.; Li, J.; Zhou, S.; et al. Machine learning-assisted controllable preparation of multicolor phosphorescent carbon dots. Carbon 2025, 241, 120394.
73.
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.
74.
Banerjee, S.; Mandal, S.; Singh, S.; et al. Machine learning-guided fabrication of carbon dot-pepsin nano-conjugates for enhanced bioimaging, synergistic drug delivery, and visible light-induced photosensitization. Nanoscale 2025, 17, 14233–14247.
75.
Mandal, S.; Paul, D.; Saha, S.; et al. Deep learning assisted detection of toxic heavy metal ions based on visual fluorescence responses from a carbon nanoparticle array. Environ. Sci.:Nano 2022, 9, 2596–2606.
76.
Tian, C.; Lee, Y.; Song, Y.; et al. Machine-learning-enhanced fluorescent nanosensor based on carbon quantum dots for heavy metal detection. ACS Appl. Nano Mater. 2024, 7, 5576–5586.
77.
Han, Y.; Song, X.; Wei, S.; et al. Deep-Blue Electroluminescent Light-Emitting Diodes with Efficient Hot-Exciton Emission from Carbon Quantum Gear Rack. Adv. Mater. 2025, 37, e10604.
78.
Zhang, Y.; Ma, Y.; Yang, Y.; et al. Heteroatom-Doping and Conjugation-Extension Enable Ultrahigh-Bright Full-Color Carbon Quantum Dots for Tunable Liquid-State Lasers and 3D Printing. Adv. Mater. 2025, 38, e03728.
79.
Wang, B.; Wang, J.; Lu, S. Carbon Dots: Small Materials with Big Impacts on Optoelectronic Devices. Aggregate 2025, 6, e70212.
80.
Liu, Y.; Cheng, D.; Wang, B.; et al. Carbon Dots-Inked Paper with Single/Two-Photon Excited Dual-Mode Thermochromic Afterglow for Advanced Dynamic Information Encryption. Adv. Mater. 2024, 36, 2403775.
81.
Shi, Y.; Zhang, Y.; Wang, Z.; et al. Onion-like multicolor thermally activated delayed fluorescent carbon quantum dots for efficient electroluminescent light-emitting diodes. Nat. Commun. 2024, 15, 3043.
82.
Song, X.; Zhang, C.; Tang, D.; et al. Pure-violet oxygen-doped carbon quantum rings with near-unity quantum yield and a full-width at half-maximum of 18 nm. Nat. Synth. 2025, 5, 262–271.
83.
Chen, L.; Yang, S.; Cui, X.; et al. Machine learning-driven advances in carbon-based quantum dots: Opportunities accompanied by challenges. Responsive Mater. 2026, e70052. https://doi.org/10.1002/rpm2.70052.
84.
Pandit, S.; Banerjee, T.; Srivastava, I.; et al. Machine Learning-Assisted Array-Based Biomolecular Sensing Using Surface-Functionalized Carbon Dots. ACS Sens. 2019, 4, 2730–2737.
85.
Tang, B.; Lu, Y.; Zhou, J.; et al. Machine learning-guided synthesis of advanced inorganic materials. Mater. Today 2020, 41, 72–80.
86.
An, S.; Thuy, T.T.; Jeon, E.; et al. Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs. J. Am. Chem. Soc. 2025, 148, 944–956.
87.
Meng, G.; Zhou, J.; Wang, Q.; et al. Isomeric Pentagonal Fusion and π-Expanding of Nitrogen/Carbonyl-Containing Multi-Resonant Emitters for High-Performance and Narrowband Organic Electroluminescence. Adv. Funct. Mater. 2025, 35, 2422973.
88.
Chen, Z.; Zhao, W.; Gong, X.; et al. Generative Screening of Multi-Resonance Molecules for Narrowband Pure Violet OLEDs. Adv. Funct. Mater. 2025, 36, e24183.
89.
Nakanotani, H.; Higuchi, T.; Furukawa, T.; et al. High-efficiency organic light-emitting diodes with fluorescent emitters. Nat. Commun. 2014, 5, 4016.
90.
Kondo, Y.; Yoshiura, K.; Kitera, S.; et al. Narrowband deep-blue organic light-emitting diode featuring an organoboron-based emitter. Nat. Photonics 2019, 13, 678–682.
91.
Qiao, X.; Huang, C.; Ni, F.; et al. Accelerating OLED development with machine learning: Advances and prospects. Chem. Commun. 2026, 62, 3095–3115.
92.
Cui, L.; Li, Q.; Zhang, Y.; et al. Machine learning-assisted high-throughput screening of transparent organic light-emitting diode anode materials. Chem. Sci. 2024, 15, 19375–19389.
93.
Zhao, B.; Ma, H.; Jia, H.; et al. Triphenylamine-Derived Solid-State Emissive Carbon Dots for Multicolor High-Efficiency Electroluminescent Light-Emitting Diodes. Angew. Chem. Int. Ed. 2023, 62, e202301651.
94.
Huang, P.; Yu, J.-R.; Li, M.-Z.; et al. Engineering Rhombus Carbon Quantum Dots for High-Performance Deep-Blue LEDs with External Quantum Efficiency Exceeding 4.5%. ACS Sustain. Chem. Eng. 2023, 11, 10041–10050.
95.
Chen, Z.; Tian, N.; Chen, X.; et al. Large Scale Synthesis of Red-Emitting Quantum Dots for Efficient and Stable Light-Emitting Diodes. Adv. Mater. 2025, 37, 2413978.
96.
Shi, L.; Ding, L.; Zhang, Y.; et al. Application of room-temperature phosphorescent carbon dots in information encryption and anti-counterfeiting. Nano Today 2024, 55, 102200.
97.
Chang, Y.; Wang, F.; Li, F.; et al. Controlling the Afterglow of Carbon Dot Composite Materials Through pH Value and the Application in Information Encryption. Laser Photonics Rev. 2025, 19, e02270.
98.
Liu, Y.; Hao, Y.; Li, Y.; et al. Triple Matrix Confinement-Induced Ultrabright Afterglow from Carbon Dots with Multivariate Responsive Afterglow Colors for Advanced Dynamic Information Encryption. Angew. Chem. Int. Ed. 2026, 65, e8266335.
99.
Ran, X.; Pan, Y.; Qiao, L.; et al. Broadband and dynamic afterglow photoluminescent carbon dots for time-resolved optical information coding and encryption. Carbon 2025, 234, 119980.
100.
Qi, Z.; Dai, M.; Ma, Y.J.; et al. Artificial Decision Tree Guided Screening of Ultralong Room-Temperature Phosphorescent Cocrystals with Locally Excited States. Angew. Chem. Int. Ed. 2025, 64, e21837.
101.
Wu, S.; Zhou, Y.; Wu, W.; et al. Smart cellulose-based room temperature phosphorescent materials: From mechanisms to construction strategies and applications. Smart Mol. 2026, e70059. https://doi.org/10.1002/smo2.70059.
102.
Deng, Y.; Zhao, D.; Chen, X.; et al. Long lifetime pure organic phosphorescence based on water soluble carbon dots. Chem. Commun. 2013, 49, 5751–5753.
103.
Xu, H.; Chang, J.; Wu, H.; et al. Carbon Dots with Guanidinium and Amino Acid Functional Groups for Targeted Small Interfering RNA Delivery toward Tumor Gene Therapy. Small 2023, 19, 2207204.
104.
Yu, H.; Ai, L.; Lu, S. Second near-infrared fluorophores: Rational design, optical enhancement strategies, and recent advances in imaging-guided biomedical applications. Chem 2026, 12, 102867.
105.
Feng, H.; Hong, Y.; Li, Q.; et al. Advancements in research on the carbon dots nanomaterials in immune modulate and immunotherapy. Chem. Eng. J. 2024, 502, 157991.
106.
Zhao, Z.; Chen, T.; Li, J.; et al. A NIR light activated self-reporting carbon dots assembly as phototheranostics for tumor photodynamic therapy. Smart Mol. 2025, 3, e20240049.
107.
Bian, J.; Wang, Z.; Ge, J.; et al. Recent advances of fluorescent carbon dots for biological imaging and biosensing. Chem. Eng. J. 2025, 519, 165135.
108.
Xie, W.; Wang, H.; Xu, H.; et al. Sterically chained amino acid-rich water-soluble carbon quantum dots as a robust tumor-targeted drug delivery platform. Nat. Commun. 2025, 16, 2716.
109.
Tang, Y.; Zhu, P.; Xu, Q.; et al. Machine learning assists the sensor array constructed by the tri-emission carbon dots to detect multiple metal ions. Microchem. J. 2024, 201, 110536.
110.
Xu, Z.; Wang, Z.; Liu, M.; et al. Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines. Spectrochim. Acta Part A 2020, 232, 118147.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us