Xinjun Shi,Jinju Zheng,Minhui Shang,Tingting Xie,Jiangbo Xie,Sheng Cao,Weiyou Yang
In this work, we reported the growth of cadmium-free Ag-doped Zn–In–S nanocrystals (NCs) with effective photoluminescence (PL) via a hot-injection strategy. The effects of the nucleation temperatures, reaction times, and Ag-doping concentrations on the PL properties of Ag-doped Zn–In–S NCs were investigated systematically. The as-synthesized NCs exhibit color-tunable PL emissions covering a broad visible range of 472–585 nm. After being passivated by a protective ZnS shell, the PL quantum yield (QY) of the resultant NCs was greatly improved up to 33%. With the increase of the Ag-doping level, the PL is significantly intensified due to the improved concentration of Ag ions which provides more holes to recombine with electrons from the bottom of the conduction band. This also makes the emission via the dopant energy level become a powerful, competitive advantage for the NCs with higher Ag-doping levels, resulting in a longer lifetime and higher PL QY. These results suggest that tailoring the Ag-doping level can be a powerful strategy to control the optical properties of Ag-doped Zn–In–S NCs.
In the present work, we reported the synthesis of Mn2+-doped Zn–In–S quantum dots (QDs) with high optical performance in a controlled manner. The effects of the temperature, the introduced ODE-S amount and Zn/In ratios in the raw materials, as well as the Mn2+ doping concentrations on the photoluminescence (PL) properties of Mn2+-doped Zn–In–S QDs were investigated systematically. The resultant QDs exhibit tunable bandgaps ranging from 2.9 to 3.7 eV, well-resolved Mn2+ d–d emission with a PL quantum yield (QY) of 56% and a surprisingly long excited state lifetime up to ∼4.2 ms, suggesting their excellent PL properties. Meanwhile, the initial high PL QY of the obtained Mn2+-doped Zn–In–S QDs in organic solvents could be preserved very well once they were transferred into aqueous media via ligand exchange. Furthermore, the as-synthesized QDs exhibit excellent thermal stability up to 200 °C in a crude solution.
Machine learning-assisted synthesis of new materials has been an ongoing hot topic. Luminescent Ag–Zn–In–S quaternary semiconductor quantum dot thin films have great application potential in many fields because of their excellent photoelectric performance. In this study, an environmentally friendly ionic liquid-assisted approach was employed to prepare high-performance Ag–Zn–In–S luminescent thin films, and machine learning methods were applied to screen the optimal preparation conditions. The photoluminescence (PL) performance, crystal structure, and morphology of the thin films were also been studied. The initial data set was obtained by high-throughput experiments. By comparing machine learning algorithms of Xgboost, Random Forest, Bagging, SVM, Extra Tree, and Gradient Boosting, the Random Forest achieved the best accuracy (r = 0.84, R 2 = 0.847) and was selected to predict the optimal preparation conditions of Ag–Zn–In–S quaternary quantum dot thin films. The machine learning predicted results are in great agreement with the subsequent experimental results, which verifies the accuracy and effectiveness of the machine learning model. This study offers a theoretical foundation for preparing optimal inorganic quantum dot luminescent materials using machine learning.
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