New trends in nonconventional carbon dot synthesis

Alternative strategies different from the solvothermal one emerged for carbon dot (CD) synthesis. This represents a great opportunity to advance the level of control of CD properties. Mechanochemistry, flow chemistry, and laser synthesis in solution resulted in the formation of CDs using mild and greener conditions. These strategies offer control on different synthetic parameters compared with the batch synthesis. The classical trial-and-error approach limits the discovery and optimization of these nanomaterials. Machine learning has been presented as an effective tool to design and guide the synthesis of CDs with targeted properties. Carbon dots (CDs) are currently one of the hot topics in the nanomaterial world. Until recently, their preparation has been mostly based on solvothermal or hydrothermal syntheses requiring high temperatures, long reaction times, or toxic solvents. Moreover, the resulting materials are often affected by low reproducibility and difficult purification. A potential solution to these problems could be represented by innovative fields of chemistry, such as mechanochemistry, flow chemistry, and laser synthesis in the liquid phase. Machine learning could also be applied to go beyond the trial-and-error approach commonly used to explore the CD chemical space. In this review, we explore these recent approaches and their future potential to address some of the CD limitations, widening the range of properties and applications of these highly promising nanomaterials. Carbon dots (CDs) are currently one of the hot topics in the nanomaterial world. Until recently, their preparation has been mostly based on solvothermal or hydrothermal syntheses requiring high temperatures, long reaction times, or toxic solvents. Moreover, the resulting materials are often affected by low reproducibility and difficult purification. A potential solution to these problems could be represented by innovative fields of chemistry, such as mechanochemistry, flow chemistry, and laser synthesis in the liquid phase. Machine learning could also be applied to go beyond the trial-and-error approach commonly used to explore the CD chemical space. In this review, we explore these recent approaches and their future potential to address some of the CD limitations, widening the range of properties and applications of these highly promising nanomaterials. the distance that the sonicator tip can longitudinally fluctuate. algorithms that learn a function from specific data by optimizing internal parameters of a general model. this approach relies on the combination of multiple nonlinear functions and the single nonlinear relationship is referred to as an artificial neuron. The resulting deep model is called a neural network. machine learning techniques that combine independent base models in order to produce one predictive model. the fluence of a laser pulse is the optical energy delivered per unit area. the ratio of the number of photons emitted to the number of photons absorbed. the irradiation of a liquid sample with ultrasonic waves, resulting in agitation and cavitation.