@article{bateni_epps_antami_dargis_bennett_reyes_abolhasani_2022, title={Autonomous Nanocrystal Doping by Self-Driving Fluidic Micro-Processors}, volume={3}, ISSN={["2640-4567"]}, url={https://doi.org/10.1002/aisy.202200017}, DOI={10.1002/aisy.202200017}, abstractNote={Lead halide perovskite (LHP) nanocrystals (NCs) are considered an emerging class of advanced functional materials with numerous outstanding optoelectronic characteristics. Despite their success in the field, their precision synthesis and fundamental mechanistic studies remain a challenge. The vast colloidal synthesis and processing parameters of LHP NCs in combination with the batch‐to‐batch and lab‐to‐lab variation problems further complicate their progress. In response, a self‐driving fluidic micro‐processor is presented for accelerated navigation through the complex synthesis and processing parameter space of NCs with multistage chemistries. The capability of the developed autonomous experimentation strategy is demonstrated for a time‐, material‐, and labor‐efficient search through the sequential halide exchange and cation doping reactions of LHP NCs. Next, a machine learning model of the modular fluidic micro‐processors is autonomously built for accelerated fundamental studies of the in‐flow metal cation doping of LHP NCs. The surrogate model of the sequential halide exchange and cation doping reactions of LHP NCs is then utilized for five closed‐loop synthesis campaigns with different target NC doping levels. The precise and intelligent NC synthesis and processing strategy, presented herein, can be further applied toward the autonomous discovery and development of novel impurity‐doped NCs with applications in next‐generation energy technologies.}, number={5}, journal={ADVANCED INTELLIGENT SYSTEMS}, publisher={Wiley}, author={Bateni, Fazel and Epps, Robert W. and Antami, Kameel and Dargis, Rokas and Bennett, Jeffery A. and Reyes, Kristofer G. and Abolhasani, Milad}, year={2022}, month={Mar} }
 @article{bateni_epps_abdel-latif_dargis_han_volk_ramezani_cai_chen_abolhasani_2021, title={Ultrafast cation doping of perovskite quantum dots in flow}, volume={4}, ISSN={["2590-2385"]}, url={https://doi.org/10.1016/j.matt.2021.04.025}, DOI={10.1016/j.matt.2021.04.025}, abstractNote={Among all-inorganic metal halide perovskite quantum dots (PQDs), cesium lead chloride (CsPbCl3) with its large band-gap energy is an excellent candidate for enhancement of PQD radiative pathways through incorporation of additional internal energy transfer within its exciton band gap. In this study, we introduce a post-synthetic chemistry for ultrafast metal cation doping of CsPbCl3 QDs with a high degree of tunability, using a model transition metal impurity dopant, manganese. Due to the fast nature of the post-synthetic metal cation-doping reaction, an engineered time-to-space transformation strategy is employed to unravel the kinetics and fundamental mechanism of the doping process. Using a modular microfluidic platform equipped with a translational in situ absorption and photoluminescence spectroscopy probe, we propose a heterogeneous surface-doping mechanism through a vacancy-assisted metal cation migration. The developed in-flow doping strategy can open new avenues for on-demand optoelectronic properties tuning and scalable precision synthesis of high-quality metal cation-doped PQDs.}, number={7}, journal={MATTER}, publisher={Elsevier BV}, author={Bateni, Fazel and Epps, Robert W. and Abdel-latif, Kameel and Dargis, Rokas and Han, Suyong and Volk, Amanda A. and Ramezani, Mahdi and Cai, Tong and Chen, Ou and Abolhasani, Milad}, year={2021}, month={Jul}, pages={2429–2447} }