(105a) Development of an Integrated Multiscale Modeling, Experimental, and Control Framework for Commercialization of Quantum Dot Manufacturing and Their Applications

In the past few years, quantum dots (QDs) with tunable optical and optoelectronic properties have received significant attention due to their applications in next-generation photonic devices (i.e., solar cells and high-resolution displays) [1]. This can be attributed to their relatively high photoluminescence quantum yield, a wide color gamut, tunable optoelectronic properties, and cost-effective solution-processibilities [2]. Furthermore, the rising market share of these applications has led to an increased demand for fast and scalable production of QDs and the associated optoelectronics devices. However, there are major commercialization challenges associated with the manufacturing of QDs: (a) a lack of understanding of the crystallization kinetics of various QD systems, which hinders the predictive control of the QD size distribution; (b) a majority of existing QD synthesis techniques follow lab-scale batch protocols, and there are very few demonstrations of fast-scalable continuous production of QDs, which is essentially for validating QD scale-up frameworks [3]; and (c) lack of accurate models and adequate controllers for regulation of QD thin-film deposition (i.e., film thickness and roughness), which is an essential step in different QD-based optoelectronic applications (i.e., solar cells and high-resolution displays) [4]. All these challenges need to be addressed in an effective, and step-by-step manner to create a foundation for the commercialization of QDs in the near future. Thus, there is a need for an integrated end-to-end framework that addresses each of the above challenges by combining various appropriate modeling and control techniques.

To address these knowledge gaps, we (a) developed combination of different models to describe the mechanism of QD crystal growth and enable fast-scalable manufacturing of QDs and the associated optoelectronic devices; (b) developed appropriate control systems to regulate various QD properties (i.e., size distribution) for application in various optoelectronic devices; and (c) integrated the above-developed models and controllers into a single fully integrated framework to address the problem of commercialization of QD-based devices.

In this endeavor, first, a first-principled kinetic Monte Carlo (kMC) was developed and experimentally validated to describe the crystallization kinetics of QDs [5]. Second, to resolve the various issues associated with the batch synthesis of QDs, continuous manufacturing of QDs using a plug flow crystallizer (PFC) was demonstrated using a high-fidelity (HF) multiscale modeling approach. Specifically,, since the developed HF model was computationally expensive, a highly efficient Artificial Neural Networks (ANN) was utilized as a surrogate model and was incorporated into a data-driven optimal operation problem to regulate QD crystal size and distribution [5]. Further, this approach was extended to two-phase slug-flow crystallizers (SFCs) by constructing a CFD-based multiscale model to account for the slug-to-slug variation and its effect on the QD size distribution [6]. Third, modeling of process steps required for manufacturing solar cells and high-resolution displays was performed to address the commercialization challenges in the QD industry. Specifically, (a) a thin-film deposition framework comprising of macroscopic heat and mass transfer and microscopic discrete element method (DEM)-based aggregation model was developed for describing the spray coating of QDs; and (b) a kMC model was developed to model nanopatterning of QDs for application in high-resolution displays [7]. Lastly, it is important to note that all the above-developed models were experimentally validated using appropriate experimental observations.

Overall, the proposed work addresses three major challenges in the QD field such as the control of QD kinetics, continuous production of QDs, and designing manufacturing processes for fast scale-up of QD-based devices. This was executed by developing various experimentally validated models (i.e., the kMC model for QD crystallization, CFD-based model for SFCs, a DEM-based spray coating model, and others). Also, model predictive control systems that utilized the above-develop models were designed to regulate the various QD properties (i.e., size and distribution, and roughness of thin films). Finally, the abovementioned models and controllers were combined to construct an integrated end-to-end QD manufacturing framework that will pave the way for enabling the easy commercialization of QDs.

References:

1. Protesescu, Loredana, et al. "Nanocrystals of cesium lead halide perovskites (CsPbX3, X= Cl, Br, and I): novel optoelectronic materials showing bright emission with wide color gamut." Nano letters 15.6 (2015): 3692-3696.
2. Bera, Debasis, et al. "Quantum dots and their multimodal applications: a review." Materials 3.4 (2010): 2260-2345.
3. Zhang, Ting, et al. "Halide perovskite based light-emitting diodes: a scaling up perspective." Journal of Materials Chemistry C 9.24 (2021): 7532-7538.
4. Park, Nam-Gyu, and Kai Zhu. "Scalable fabrication and coating methods for perovskite solar cells and solar modules." Nature Reviews Materials 5.5 (2020): 333-350.
5. Sitapure, Niranjan, et al. "Multiscale modeling and optimal operation of millifluidic synthesis of perovskite quantum dots: Towards size-controlled continuous manufacturing." Chemical Engineering Journal 413 (2021): 127905.
6. Sitapure, Niranjan, et al. "CFD-based computational studies of quantum dot size control in slug flow crystallizers: Handling slug-to-slug variation." Industrial & Engineering Chemistry Research 60.13 (2021): 4930-4941.
7. Sitapure, Niranjan, et al. "Modeling ligand crosslinking for interlocking quantum dots in thin-films." Journal of Materials Chemistry C (2022).