Graphene quantum dots (GQDs) have shown great potential in physical−chemical-biological applications, especially for fluorescence monitoring. However, the low fluorescence activity, safety issues, and unclear synthesis mechanism restrict their application. Here, we investigate the synthesis process of B,N-GQDs by oxidizing 3-aminophenylboronic acid monohydrate and study their core synthesis process parameters (synthesis temperature, H 2 O 2 additional volume, and synthesis time) and corresponding synergic/antagonistic effects in a multidimensional and wide-ranging region. By collecting the optical properties of B,N-GQDs in varied synthesis conditions and utilizing different machine learning models to fit the data, we select the polynomial regression 7 model and the 675/500 peak intensity ratio to evaluate the best synthesis process parameters. Furthermore, through the weight analysis method, we demonstrate that the weight of H 2 O 2 additional volume (0.0260) is obviously higher than those of synthesis temperature (−0.0058) and synthesis time (0.0172), exhibiting that H 2 O 2 additional volume dominates in the synthesis process of B,N-GQDs. Meanwhile, by the greedy random walk method, we could confirm that B,N-GQDs synthesized in the condition of "184-10-2.23" proved to be the best synthesis condition among the different conditions tested in this work. The sample shows a high 675/500 peak intensity ratio (0.285) and photoluminescence quantum yield (PLQY) (0.74%). More importantly, the sample in the laboratory rat reveals a bright fluorescence, indicating that optimized B,N-GQDs are suitable for fluorescence monitoring.

high-resolution, especially in the solid state or high concentration liquid states. [5][6][7] Dong et al. constructed two Ln-MOFs {[Ln(H 3 L)(H 2 O)] • 7H 2 O} n (Ln = Eu and Tb, H 3 L = 5-(3',5'-dicarboxylphenyl)picolinic acid), and utilized these two kinds of Ln-MOFs to detect the Fe 3+ and chromate ions. [8] And the detection limits of Fe 3+ / CrO 4 2-/Cr 2 O 7 2are 0.67/0.53/0.32 × 10 −6 m for europium-based metal-organic frameworks (Eu-MOFs) and 1.26/0.75/0.57 × 10 −6 m for Tb-MOFs, respectively. Cui et al. synthesized the functional Eu-MOFs by selecting Eu 3+ and 5-boronobenzene-1,3-dicarboxylic acid (BBDC) as metal node and organic ligand, respectively. [9] Due to the special nucleophilic reaction between H 2 O 2 and boric group, these Eu-MOFs can be used to detect the concentration of H 2 O 2 and glucose, and their low detection limit concentrations are 0.0335 × 10 −6 and 0.0643 × 10 −6 m, respectively. Zhang et al. also synthesized five kinds of 3D Ln-MOFs as [Ln 4 (µ 6 -L) 2 (µ-HCOO)(µ 3 -OH)3(µ 3 -O) (DMF)2(H 2 O) 4 ] n (Ln = Tb, Eu, Gd, Dy, and Er), and reflected that Tb-MOFs showed an high efficiency and selectivity in detecting the acetone and metal (III) cations (Fe 3+ and Ce 3+ ). [10] Expecting for the effect of precursors components (such as rare-salt, organic ligand, solvent and their composition ratio, etc.) discussed above, the morphology and size of MOFs also reported to have an significant influence on the optical properties of AIE characteristic MOFs. [11][12][13] For instance, the size of MOFs not only affects their specific surface area and reaction Metal-organic frameworks (MOFs) with the aggregation-induced emission (AIE) activities exhibit potential applications in the fields of energy and biomedical technology. However, the controllable synthesis of MOFs in the varied particle sizes not only affects their AIE activities, but also restricts their application scenarios. In this work, the varied particle sizes of Eu-MOFs are synthesized by adjusting the synthesis process parameters, and their variation rules combining the single factor analysis method with machine learning technology are studied. Based on the R 2 score, the gradient boosting decision tree (GBDT) regression model (0.9535) is employed to calculate the weight and correlation between different synthesis process parameters and it is shown that all these parameters have synergic effects on the particle sizes of Eu-MOFs, and the Eu-precursors concentration dominates in their synthesis process. Furthermore, it is indicated that the large size of Eu-MOFs and strong structural stability contribute to their high AIE activities. Finally, a screen-printed pattern is fabricated using the sample of "120-0.3-6," and this pattern exhibits a bright red fluorescence under the UV light. More importantly, this kind of Eu-MOFs can also be used to identify varied ions (Fe 3+ , F -, I -, SO 4 2-, CO 3 2-, and PO 4 3-) and citric acid.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002...

Metal−organic frameworks (MOFs) with aggregation-induced emission (AIE) activity show a high emission intensity, high sensitivity, and high resolution in biological imaging and identification technologies. However, their AIE activity is controlled by various Eu precursors' components and synthesis process parameters, and traditional research methods are hard to deal with these complex multiple parameter systems. In this work, we utilize two machine learning technologies to optimize the synthesis process parameters of Eu-MOFs and analyze their synthesis mechanism. First, we choose gradient boosting decision tree (GBDT) regression as the best fitting model. Second, on the basis of the SHapley Additive exPlanation (SHAP) calculation method with the PL/UV intensity ratio regarded as the evaluation standard, we demonstrate that the Eu-precursor concentration (1.91 × 10 7 ) and synthesis time (1.73 × 10 7 ) dominate in the synthesis systems. Meanwhile, these two parameters show synergic and antagonistic effects on the PL/UV intensity ratio, respectively. Finally, we employ a greedy random walk method to work out that "142-0.83-4.1" should be the best optimization process parameters, and the corresponding sample shows a high photoluminescence quantum yield (PLQY) with a value of 7.65% in the solid state. More importantly, the screen-printed pattern exhibits bright red fluorescence under UV light.

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