Graphical Abstract
![](https://wqketang.oss-cn-beijing.aliyuncs.com/zip-unzip/zip-370ef658-1817-4097-bf92-27e89c86bf89/files/6946-TOC.jpg?Expires=1725972113&OSSAccessKeyId=STS.NTMRFvsp3nrQW4DckLVJkW4HE&Signature=nW1flBpj9TGhXcbt1IQDFM4LOdk%3D&security-token=CAISywJ1q6Ft5B2yfSjIr5f4GfzCnq8S2bC6VRL1h2sZWsVHuPHjpzz2IHtKenhsBOsbtfk1mG5W5%2FgZlqJ9SptIAEfJa9d99MzLJ%2F46ltCT1fau5Jko1beHewHKeTOZsebWZ%2BLmNqC%2FHt6md1HDkAJq3LL%2Bbk%2FMdle5MJqP%2B%2FUFB5ZtKWveVzddA8pMLQZPsdITMWCrVcygKRn3mGHdfiEK00he8TouufTinpHMskGA1Aell7Mvyt6vcsT%2BXa5FJ4xiVtq55utye5fa3TRYgxowr%2Fwo0v0YpGya5YzHXwcPskvdKZbo78UqLQlla6w%2BGqFJqvPxr%2Fp8t%2Fx5fWJKAezhVgs8cVM8JOjIqKOscIsipky52WMqFV55c8Fdm%2BgUooJVgIMhTnduUfAPJAGOxzJitP%2BUVGGphr60TEnBL4rB5MUctfzRpyNM%2BkjUTzDnGoABiOqCzONoiCD0aBPfvoXI%2F66Cp97q3IG4O3mp48iTFoAmxN0xRJzuIkYPdNr6BZw8R%2F7xCggzNA47ArXVbTVOvFI9cu8YaFY4xvRURkEC3q0vH2D5J9LhpCqKU3GJZ%2B8290h0AmqGNMgdFGG4PKW8xDfaK2ctHhizzfdz3G43pmEgAA%3D%3D)
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Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co2+ and Fe3+, with a concentration range of 0–120 and 0–150 μM, and their detection limits are 1.17 and 0.06 μM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction.
Zhang, Y. Q.; Lu, S. Y. Lasing of carbon dots: Chemical design, mechanisms, and bright future. Chem 2024, 10, 134–171.
Yang, X.; Li, X.; Wang, B. Y.; Ai, L.; Li, G. P.; Yang, B.; Lu, S. Y. Advances, opportunities, and challenge for full-color emissive carbon dots. Chin. Chem. Lett. 2022, 33, 613–625.
Ma, C.; Zheng, T. T.; Zhang, R. Y.; Zhu, J. J. Editorial: Optical and electrochemical biosensing. Front. Chem. 2022, 10, 964825.
Chen, T. S.; Meng, F. B.; Ying, B. W.; Zhu, X. L. Editorial: Disease biomarker analysis based on optical biosensing. Front. Chem. 2023, 11, 1205533.
Hu, N.; Wan, H. Electrical/optical biosensing and regulating technology. Biosensors-Basel 2023, 13, 634.
Chen, Z. Q.; Wang, Z. Y.; Wang, J. T.; Chen, S. M.; Zhang, B. Y.; Li, Y.; Yuan, L.; Duan, Y. Analysis of the effect of graphene, metal, and metal oxide transparent electrodes on the performance of organic optoelectronic devices. Nanomaterials 2023, 13, 25.
Lee, M.; Seung, H.; Kwon, J. I.; Choi, M. K.; Kim, D. H.; Choi, C. Nanomaterial-based synaptic optoelectronic devices for in-sensor preprocessing of image data. Acs Omega 2023, 8, 5209–5224.
Yi, Z. C.; Zhang, H.; Jiang, M. H.; Wang, J. S. Editorial for the special issue on advances in optoelectronic devices. Micromachines 2023, 14, 652.
Chen, Y. X.; Zhang, C. Y.; Huang, Y. K.; Ma, Y. X.; Song, Q. X.; Chen, H. Z.; Jiang, G.; Gao, X. L. Intranasal drug delivery: The interaction between nanoparticles and the nose-to-brain pathway. Adv. Drug Delivery Rev. 2024, 207, 115196.
Park, D.; Lee, S. J.; Park, J. W. Aptamer-based smart targeting and spatial trigger-response drug-delivery systems for anticancer therapy. Biomedicines 2024, 12, 187.
Song, W. C.; Muhammad, S.; Dang, S. X.; Ou, X. Y.; Fang, X. Z.; Zhang, Y. H.; Huang, L. H.; Guo, B.; Du, X. L. The state-of-art polyurethane nanoparticles for drug delivery applications. Front. Chem. 2024, 12, 1378324.
Chen, S. Y.; Liu, X. C.; Li, S.; Tan, Y. G.; Yu, J. Y.; Zhang, C.; Feng, J. Impact of nitrogen doping on the polarization properties of carbon quantum dots. Opt. Mater. 2024, 149, 115034.
Das, S.; Mondal, S.; Ghosh, D. Carbon quantum dots in bioimaging and biomedicines. Front. Bioeng. Biotechnol. 2024, 11, 1333752.
Latif, Z.; Shahid, K.; Anwer, H.; Shahid, R.; Ali, M.; Lee, K. H.; Alshareef, M. Carbon quantum dots (CQDs)-modified polymers: A review of non-optical applications. Nanoscale 2024, 16, 2265–2288.
F. J.; Garcia-Hernández, L.; Camacho-López, S.; Camacho-López, M.; Camacho-López, M. A.; Reyes Contreras, D.; Pérez-Rodriguez, A.; Peña-Caravaca, J. P.; Páez-Rodríguez, A.; Darias-Gonzalez, J. G. et al. Carbon quantum dots by submerged arc discharge in water: Synthesis, characterization, and mechanism of formation. J. Appl. Phys. 2021, 129, 163301.
Unnikrishnan, E.; Krishnamoorthy, A.; Shaji, S. P.; Kamath, A. S.; Ulaganathan, M. Electrocatalytic behavior of carbon quantum dots in sustainable applications: A review. Curr. Opin. Electrochem. 2024, 43, 101436.
Zhang, Z.; Qu, D.; An, L.; Wang, X. Y.; Sun, Z. W. Preparation, luminescence mechanism and application of fluorescent carbon dots. Chin. J. Lumin. 2021, 42, 1125–1140.
Rocco, D.; Moldoveanu, V. G.; Feroci, M.; Bortolami, M.; Vetica, F. Electrochemical synthesis of carbon quantum dots. Chemelectrochem 2023, 10, e202201104.
Abraham, A.; Muhammed Anees, P.; Eldho, A.; Bushiri, M. J. WO3·0.33H2O/carbon quantum dots hybrid nanostructures for efficient electrochemical hydrogen evolution reaction. Diamond Relat. Mater. 2023, 139, 110309.
Zhu, J. J.; Zhu, M. Y.; He, Z. Y.; Xiong, L. P.; Zhang, R. H.; Guo, L. Chemical oxidation synthesized high-yield carbon dots for acid corrosion inhibition of Q235 steel. Chemistryselect 2023, 8, e202204621.
Ge, G. L.; Li, L.; Wang, D.; Chen, M. J.; Zeng, Z. Y.; Xiong, W.; Wu, X.; Guo, C. Carbon dots: synthesis, properties and biomedical applications. J. Mater. Chem. B 2021, 9, 6553–6575.
He, M. Q.; Zhang, J.; Wang, H.; Kong, Y. R.; Xiao, Y. M.; Xu, W. Material and optical properties of fluorescent carbon quantum dots fabricated from lemon juice via hydrothermal reaction. Nanoscale Res. Lett. 2018, 13, 175.
Wang, B. Y.; Song, H. Q.; Tang, Z. Y.; Yang, B.; Lu, S. Y. Ethanol-derived white emissive carbon dots: The formation process investigation and multi-color/white LEDs preparation. Nano Res. 2022, 15, 942–949.
Zhang, Y. X.; Fan, X. P.; Cao, Y. J.; Yang, X. T.; Li, Z. P.; Yang, Z. H.; Dong, C. Synthesis of oil-soluble carbon quantum dots by pyrolysis method for the detection of oxytetracycline. Chin. J. Appl. Chem. 2023, 40, 509–517.
Kostromin, S.; Borodina, A.; Podshivalov, A.; Pankin, D.; Zhigalina, O.; Bronnikov, S. Characterization of carbon quantum dots obtained through citric acid pyrolysis. Fullerenes Nanotubes Carbon Nanostruct. 2023, 31, 931–939.
Shabbir, H.; Tokarski, T.; Ungor, D.; Wojnicki, M. Eco friendly synthesis of carbon dot by hydrothermal method for metal ions salt identification. Materials 2021, 14, 7604.
M. N.; Smagulova, S. A. Effect of laser treatment on the luminescence of carbon dots synthesized by the hydrothermal method. AIP Conf. Proc. 2021, 2328, 050007.
Balogun, A. L.; Tella, A.; Baloo, L.; Adebisi, N. A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science. Urban Climate 2021, 40, 100989.
Akpan, U. I.; Starkey, A. Review of classification algorithms with changing inter-class distances. Mach. Learn. Appl. 2021, 4, 100031.
Tuchin, V. S.; Stepanidenko, E. A.; Vedernikova, A. A.; Cherevkov, S. A.; Li, D.; Li, L.; Döring, A.; Otyepka, M.; Ushakova, E. V.; Rogach, A. L. Optical properties prediction for red and near-infrared emitting carbon dots using machine learning. Small 2024, 20, 2310402.
Chen, S. T.; Cao, J. D.; Wan, Y.; Shi, X. L.; Huang, W. Enhancing rutting depth prediction in asphalt pavements: A synergistic approach of extreme gradient boosting and snake optimization. Constr. Build. Mater. 2024, 421, 135726.
J. Z.; Du, Z. X.; Liu, J. Y.; Xu, L. J.; He, L. P.; Gu, L.; Cheng, H.; He, Q. Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley additive exPlanations. Sci. Total Environ. 2024, 920, 171033.
Ashtiani, M. N.; Raahemi, B. News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review. Expert Syst. Appl. 2023, 217, 119509.
McDonald, S. M.; Augustine, E. K.; Lanners, Q.; Rudin, C.; Catherine Brinson, L.; Becker, M. L. Applied machine learning as a driver for polymeric biomaterials design. Nat. Commun. 2023, 14, 4838.
Mohammadzadeh Kakhki, R.; Mohammadpoor, M. Machine learning-driven approaches for synthesizing carbon dots and their applications in photoelectrochemical sensors. Inorg. Chem. Commun. 2024, 159, 111859.
Xu, Q.; Tang, Y. Y.; Zhu, P. D.; Zhang, W. Y.; Zhang, Y. Q.; Solis, O. S.; Hu, T. S.; Wang, J. C. Machine learning guided microwave-assisted quantum dot synthesis and an indication of residual H2O2 in human teeth. Nanoscale 2022, 14, 13771–13778.
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, 9014–9017.
Huang, W. J.; Martin, P.; Zhuang, H. L. Machine-learning phase prediction of high-entropy alloys. Acta Mater. 2019, 169, 225–236.
Hastings, J.; Glauer, M.; Memariani, A.; Neuhaus, F.; Mossakowski, T. Learning chemistry: Exploring the suitability of machine learning for the task of structure-based chemical ontology classification. J. Cheminform. 2021, 13, 23.
Chang, Y. J.; Jui, C. Y.; Lee, W. J.; Yeh, A. C. Prediction of the composition and hardness of high-entropy alloys by machine learning. JOM 2019, 71, 3433–3442.
Fan, C.; Xiao, F.; Yan, C. C.; Liu, C. L.; Li, Z. D.; Wang, J. Y. A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning. Appl. Energy 2019, 235, 1551–1560.
Kronberg, R.; Lappalainen, H.; Laasonen, K. Hydrogen adsorption on defective nitrogen-doped carbon nanotubes explained via machine learning augmented dft calculations and game-theoretic feature attributions. J. Phys. Chem. C 2021, 125, 15918–15933.
Deng, C. F.; Su, Y.; Li, F. H.; Shen, W. F.; Chen, Z. F.; Tang, Q. Understanding activity origin for the oxygen reduction reaction on bi-atom catalysts by DFT studies and machine-learning. J. Mater. Chem. A 2020, 8, 24563–24571.
Xing, C. Y.; Chen, G. Y.; Zhu, X.; An, J. K.; Bao, J. C.; Wang, X.; Zhou, X. Q.; Du, X. L.; Xu, X. X. Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning. Nano Res. 2024, 17, 1984–1989.
Wang, X. Y.; Chen, B. B.; Zhang, J.; Zhou, Z. R.; Lv, J.; Geng, X. P.; Qian, R. C. Exploiting deep learning for predictable carbon dot design. Chem. Commun. 2021, 57, 532–535.
Han, Y.; Tang, B. J.; Wang, L.; Bao, H.; Lu, Y. H.; Guan, C. T.; Zhang, L.; Le, M. Y.; Liu, Z.; Wu, M. H. Machine-learning-driven synthesis of carbon dots with enhanced quantum yields. ACS Nano 2020, 14, 14761–14768.
Wang, B. Y.; Wei, Z. H.; Sui, L. Z.; Yu, J. K.; Zhang, B. W.; Wang, X. Y.; Feng, S. N.; Song, H. Q.; Yong, X.; Tian, Y. X. et al. Electron-phonon coupling-assisted universal red luminescence of o-phenylenediamine-based carbon dots. Light Sci. Appl. 2022, 11, 172.
Yang, L. J.; Liu, S. C.; Quan, T.; Tao, Y. Q.; Tian, M.; Wang, L. C.; Wang, J. J.; Wang, D. D.; Gao, D. Sulfuric-acid-mediated synthesis strategy for multi-colour aggregation-induced emission fluorescent carbon dots: Application in anti-counterfeiting, information encryption, and rapid cytoplasmic imaging. J. Colloid Interface Sci. 2022, 612, 650–663.
Wang, H.; Cao, J. J.; Zhou, Y. J.; Wang, Z. Z.; Zhao, Y. J.; Liu, Y.; Huang, H.; Shao, M. W.; Liu, Y.; Kang, Z. H. Carbon dot-modified mesoporous carbon as a supercapacitor with enhanced light-assisted capacitance. Nanoscale 2020, 12, 17925–17930.
Kundu, A.; Park, B.; Oh, J.; Sankar, K. V.; Ray, C.; Kim, W. S.; Chan Jun, S. Multicolor emissive carbon dot with solvatochromic behavior across the entire visible spectrum. Carbon 2020, 156, 110–118.
Siddique, A. B.; Hossain, S. M.; Pramanick, A. K.; Ray, M. Excitation dependence and independence of photoluminescence in carbon dots and graphene quantum dots: Insights into the mechanism of emission. Nanoscale 2021, 13, 16662–16671.
Liang, C. Z.; Xie, X. B.; Shi, Q. S.; Feng, J.; Zhang, D. D.; Huang, X. M. Nitrogen/sulfur-doped dual-emission carbon dots with tunable fluorescence for ratiometric sensing of ferric ions and cell membrane imaging. Appl. Surf. Sci. 2022, 572, 151447.
Li, X. J.; Zheng, M. D.; Wang, H. J.; Meng, Y.; Wang, D.; Liu, L. L.; Zeng, Q. H.; Xu, X. W.; Zhou, D.; Sun, H. C. Synthesis of carbon dots with strong luminescence in both dispersed and aggregated states by tailoring sulfur doping. J. Colloid Interface Sci. 2022, 609, 54–64.
Tang, B. J.; Lu, Y. H.; Zhou, J. D.; Chouhan, T.; Wang, H.; Golani, P.; Xu, M. Z.; Xu, Q.; Guan, C. T.; Liu, Z. Machine learning-guided synthesis of advanced inorganic materials. Mater. Today 2020, 41, 72–80.
Hong, Q.; Wang, X. Y.; Gao, Y. T.; Lv, J.; Chen, B. B.; Li, D. W.; Qian, R. C. Customized carbon dots with predictable optical properties synthesized at room temperature guided by machine learning. Chem. Mater. 2022, 34, 998–1009.
Roodbar Shojaei, T.; Mohd Salleh, M. A.; Mobli, H.; Aghbashlo, M.; Tabatabaei, M. Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots. J. Mater. Sci. Mater. Electron. 2019, 30, 3156–3165.
Senanayake, R. D.; Yao, X. X.; Froehlich, C. E.; Cahill, M. S.; Sheldon, T. R.; McIntire, M.; Haynes, C. L.; Hernandez, R. Machine learning-assisted carbon dot synthesis: Prediction of emission color and wavelength. J. Chem. Inf. Model. 2022, 62, 5918–5928.
Zhang, Q.; Tao, Y. T. Z.; Tang, B.; Yang, J. X.; Liang, H. W.; Wang, B. B.; Wang, J. M.; Jiang, X.; Ji, L. H.; Li, S. S. Graphene quantum dots with improved fluorescence activity via machine learning: Implications for fluorescence monitoring. ACS Appl. Nano Mater. 2022, 5, 2728–2737.
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, 5465–5475.
Yu, X. W.; Liu, X. Y.; Jiang, Y. W.; Li, Y. H.; Gao, G.; Zhu, Y. X.; Lin, F. M.; Wu, F. G. Rose bengal-derived ultrabright sulfur-doped carbon dots for fast discrimination between live and dead cells. Anal. Chem. 2022, 94, 4243–4251.
Saengsrichan, A.; Saikate, C.; Silasana, P.; Khemthong, P.; Wanmolee, W.; Phanthasri, J.; Youngjan, S.; Posoknistakul, P.; Ratchahat, S.; Laosiripojana, N. et al. The role of N and S doping on photoluminescent characteristics of carbon dots from palm bunches for fluorimetric sensing of Fe3+ Ion. Int. J. Mol. Sci. 2022, 23, 5001.
S.; Zhang, J.; Yang, Z. Y.; Pang, A. P.; Zeng, J.; Sayed, S. M.; Khan, A.; Zhang, Y. Q.; Wu, F. G.; Lin, F. M. Plant-derived Ca, N, S-doped carbon dots for fast universal cell imaging and intracellular Congo red detection. Anal. Chim. Acta 2022, 1202, 339672.
Yang, Z.; Shen, W.; Chen, Q. J.; Wang, W. Direct electrochemical reduction and dyeing properties of CI Vat Yellow 1 using carbon felt electrode. Dyes Pigm. 2021, 184, 108835.
Ezati, P.; Rhim, J. W.; Molaei, R.; Priyadarshi, R.; Roy, S.; Min, S.; Kim, Y. H.; Lee, S. G.; Han, S. Preparation and characterization of B, S, and N-doped glucose carbon dots: Antibacterial, antifungal, and antioxidant activity. Sustain. Mater. Technol. 2022, 32, e00397.
Bai, H. Y.; Tu, Z. Q.; Liu, Y. T.; Tai, Q. X.; Guo, Z. K.; Liu, S. Y. Dual-emission carbon dots-stabilized copper nanoclusters for ratiometric and visual detection of Cr2O72− ions and Cd2+ ions. J. Hazard. Mater. 2020, 386, 121654.
Huo, F.; Kang, Z.; Zhu, M. G.; Tan, C.; Tang, Y. R.; Liu, Y. H.; Zhang, W. Metal-triggered fluorescence enhancement of multicolor carbon dots in sensing and bioimaging. Opt. Mater. 2019, 94, 363–370.
Bai, Y. L.; Zhao, J. L.; Wang, S. L.; Lin, T. R.; Ye, F. G.; Zhao, S. L. Carbon dots with absorption red-shifting for two-photon fluorescence imaging of tumor tissue pH and synergistic phototherapy. ACS Appl. Mater. Interfaces 2021, 13, 35365–35375.
Liao, J.; Cheng, Z. H.; Zhou, L. Nitrogen-doping enhanced fluorescent carbon dots: Green synthesis and their applications for bioimaging and label-free detection of Au3+ ions. ACS Sustain. Chem. Eng. 2016, 4, 3053–3061.
Spatafora, G.; Li, Y. H.; He, X. S.; Cowan, A.; Tanner, A. C. R. The evolving microbiome of dental caries. Microorganisms 2024, 12, 121.
Kassebaum, N. J.; Bernabé, E.; Dahiya, M.; Bhandari, B.; Murray, C. J. L.; Marcenes, W. Global burden of untreated caries: A systematic review and metaregression. J. Dental Res. 2015, 94, 650–658.
Zhou, L.; Li, Q. L.; Wong, H. M. A novel strategy for caries management: Constructing an antibiofouling and mineralizing dual-bioactive tooth surface. ACS Appl. Mater. Interfaces 2021, 13, 31140–31152.
De Menezes Oliveira, M. A. H.; Torres, C. P.; Gomes-Silva, J. M.; Chinelatti, M. A.; De Menezes, F. C. H.; Palma-Dibb, R. G.; Borsatto, M. C. Microstructure and mineral composition of dental enamel of permanent and deciduous teeth. Microsc. Res. Tech. 2010, 73, 572–577.