CFD-Based Computational Studies of Quantum Dot Size Control in Slug Flow Crystallizers: Handling Slug-to-Slug Variation

Abstract

Recently, slug-flow crystallizers (SFCs) have been proposed for continuous manufacturing of colloidal quantum dots (QDs). Despite the intriguing advantages of SFCs for controlled manufacturing of QDs, it has been difficult to account for the wide crystal size distribution (CSD) caused by slug-to-slug (S2S) variation, and the absence of a modeling and control framework made it challenging to fine-tune the QD size distribution. In response, we developed a computational fluid dynamics (CFD) model to simulate the S2S variation in SFCs. The results from the CFD model were integrated with a slug crystallizer model, which can describe the effect of S2S heterogeneity on crystallization of QDs in SFCs. Specifically, the slug crystallizer model was constructed by combining a continuum model with a kinetic Monte Carlo model. Based on the proposed CFD-based multiscale model, an optimal operation problem was formulated to ensure a good set-point (QD size) tracking performance and a narrow CSD.

Introducing Hybrid Modeling with Time-Series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization

“…For example, in the current case study, the TST predicts the G & B kinetics, which are fed to the FP model of a batch crystallizer. However, the same TST model can be integrated with an FP model for a continuous tubular crystallizer or a continuously agitated crystallizer. ,,, Basically, given the internal configuration of the series hybrid model, it is possible to replace the FP model with another FP crystallization model, and the new series hybrid model can be easily fine-tuned for a certain case.…”

“…3,5−7 For instance, various RNN and DNN models have been utilized in the literature to mimic the crystallization of pharmaceutical and food products by Wu and colleagues. 8−11 Similarly, Kwon and colleagues have DNN-based models for continuous crystallization of quantum dot (QD) systems.. 12,13 Similarly, other DNN-based models have also been demonstrated for modeling and control of thin-film deposition for different substrates. 14−16 Furthermore, Braatz and colleagues have demonstrated a plethora of impressive different ML models for the prediction of battery life, developing optimization or control frameworks using these ML models.…”

“…By integrating real-time data, advanced analytics, and ML/AI algorithms, digital twins enable continuous monitoring, predictive analysis, and optimization of chemical processes in real-time. , A majority of the existing digital twins utilize data-driven black-box models, which are majorly based on different ML architectures as they provide high computational efficiency. Specifically, deep, recurrent, or convolution neural networks (DNN, RNN, and CNN) are used to mimic the process dynamics of complex chemical systems. ,− For instance, various RNN and DNN models have been utilized in the literature to mimic the crystallization of pharmaceutical and food products by Wu and colleagues. − Similarly, Kwon and colleagues have DNN-based models for continuous crystallization of quantum dot (QD) systems.. , Similarly, other DNN-based models have also been demonstrated for modeling and control of thin-film deposition for different substrates. − Furthermore, Braatz and colleagues have demonstrated a plethora of impressive different ML models for the prediction of battery life, developing optimization or control frameworks using these ML models. − …”

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

“…Moreover, the crystallization was greatly influenced by impeller speeds (50 to 350 rpm, optimum 300 rpm), local supersaturation, seed mass (100 to 1000 g, optimum 750 g), seed size (100 to 700 μm, optimum 500 μm), and seed temperature (303 to 323 K, optimum 308 K). Sitapure et al 364 and Sitapure et al 365 numerically studied the continuous manufacturing of quantum dots (QDs) in a slug flow crystallizer (SFC). The gas velocity, liquid velocity, and precursor concentration were optimized by a novel CFDbased multiscale model to minimize the adverse effect of slug variation in a SFC to achieve high set-point (QD size) tracking performance.…”

Modeling ligand crosslinking for interlocking quantum dots in thin-films

“…[1][2][3][4][5][6] This can be attributed to their relatively high photoluminescence quantum yield, 7 a wide color gamut, 8 tunable optoelectronic properties, and cost-effective solution-processibilities. [9][10][11][12][13][14][15][16][17] However, when integrating QDs into thin-film photonic devices, the chemical solubility of QDs in solid state should be delicately controlled, so that the as-deposited QDs are not damaged during post-processing steps involving various chemical solvents. 18,19 For this purpose, engineering on the ligands bound to the QD surface has been studied intensively.…”