Electroluminescence from quantum dots (QDs) is a suitable photon source for futuristic displays offering hyper‐realistic images with free‐form factors. Accordingly, a nondestructive and scalable process capable of rendering multicolored QD patterns on a scale of several micrometers needs to be established. Here, nondestructive direct photopatterning for heavy‐metal‐free QDs is reported using branched light‐driven ligand crosslinkers (LiXers) containing multiple azide units. The branched LiXers effectively interlock QD films via photo‐crosslinking native aliphatic QD surface ligands without compromising the intrinsic optoelectronic properties of QDs. Using branched LiXers with six sterically engineered azide units, RGB QD patterns are achieved on the micrometer scale. The photo‐crosslinking process does not affect the photoluminescence and electroluminescence characteristics of QDs and extends the device lifetime. This nondestructive method can be readily adapted to industrial processes and make an immediate impact on display technologies, as it uses widely available photolithography facilities and high‐quality heavy‐metal‐free QDs with aliphatic ligands.
Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.
In this study, machine learning algorithms, such as support vector machine (SVM), k-nearest-neighbors (KNN), and rndom forest (RF), are applied to improve the accuracy of the quantitative structure–property relationship (QSPR) models to predict the upper flammability limit (UFL) of pure organic compounds. Ten molecular descriptors are utilized to develop the QSPR model. The experimental data set contains 79 chemicals and is split into 70% training and 30% test set in order to conduct cross-validation. The multiple linear regression (MLR) QSPR model of denary logarithms of the UFL obtained in this study has six molecular descriptors and an overall root-mean-square error (RMSE) of 0.145. The other four descriptors are eliminated based on statistical insignificance. The QSPR models aided by SVM and RF improve the prediction of the UFL as indicated by their overall RMSEs of 0.118 and 0.095, respectively. However, the QSPR model aided by KNN demonstrated the least performance with the overall RMSE of 0.163.
This article focuses on the modeling and control of a continuous crystallizer with a fines trap and a product classification unit employed to produce tetragonal hen-egg-white lysozyme crystals. A kinetic Monte Carlo model is initially developed to simulate the crystal nucleation, growth, and aggregation processes taking place in the crystallizer using experimentally determined rate expressions. Subsequently, the influence of varying (a) the flow rates of the streams to the fines trap and the product classification unit and (b) the corresponding cutoff sizes is studied, and as a result, an operating strategy that takes advantage of the aggregation, fines removal, and product classification units is proposed to simultaneously achieve a high production rate and a low polydispersity of the crystals produced by the crystallizer. Finally, a model predictive controller is designed using a reduced-order model, which manipulates the jacket temperature to lead to the production of crystals with the desired shape and size distributions.
In this paper, we focus on a batch protein crystallization process used to produce tetragonal hen egg white lysozyme crystals and present a comparative study of the performance of a model predictive control (MPC) strategy formulated to account for crystal shape and size distribution with conventional operating strategies used in industry, namely, constant temperature control (CTC) and constant supersaturation control (CSC). Initially, a comprehensive, batch crystallizer model is presented involving a kinetic Monte Carlo (kMC) simulation model which describes the nucleation and crystal growth via adsorption, desorption, and migration mechanisms on the (110) and (101) faces and mass and energy balances for the continuous phase, which are developed to estimate the depletion in the protein solute concentration and the variation in the crystallizer temperature. Existing experimental data are used to calibrate the crystal growth rate and to develop an empirical expression for the nucleation rate. Simulation results demonstrate that the proposed MPC, adjusting the crystallizer jacket temperature, is able to drive the crystal shape to a desired set-point value with a low polydispersity for crystal size compared to CTC and CSC operating policies, respectively. The proposed MPC determines the optimal operating conditions needed to obtain protein crystals of a desired shape and size distribution as it helps avoid the small crystal fines at the end of the batch run.
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