Additives which share a similar molecular structure with the crystallizing parent molecule are called tailor-made additives (TMAs). The inclusion of well-chosen TMAs often has a significant impact on crystallization behaviors. However, how TMAs affect the solution crystallization has seldom been systematically summarized in recent years. Herein this paper reviews the role of TMAs in solution crystallization. First, the effects and action mechanisms of TMAs on crystal nucleation and growth are discussed, respectively. Next, the applications of TMAs in the regulation of crystal properties including the polymorphism, crystal habit, crystal size, and chirality are introduced. Then the recent progress of molecular simulation in predicting the role of TMAs is discussed. Finally, we analyze the existing problems in this field and give an outlook on the future development. This paper is helpful and useful to readers interested in the use of TMAs to control crystallization and design crystals with desired properties.

Two deep learning models to reconstruct 3-dimensional (3D) steady-state rotating flows are proposed to capture the spatial information: the 3D Convolutional Encode-decoder and the 3D Convolutional Long Short-term Memory (LSTM) Model. They are based on deep learning methods such as the encoder-decoder convolutional neural network (ED-CNN) and recurrent neural network (RNN). Their common component sare an encoder, a middle layer, and a decoder. The rotating flows in a stirred tank with four inclined blades are calculated for the dataset to train and test the two models. A workflow for flow field reconstruction is established and all variants made up of various components are executed according to the flow. The optimal networks of the two models are selected by comparing performance measures. The results show that both models have the excellent ability to fit the 3D rotating flow field. Performance measures of the second model are better than those of the first one, but its running time is slower than that of the first one. In practice, this method can beused in the design and optimization of stirred tanks, centrifugal pumps, and other machines with rotating parts.

Single-pixel imaging (SPI) enables the use of advanced detector technologies to provide a potentially low-cost solution for sensing beyond the visible spectrum and has received increasing attentions recently. However, when it comes to sub-Nyquist sampling, the spectrum truncation and spectrum discretization effects significantly challenge the traditional SPI pipeline due to the lack of sufficient sparsity. In this work, a deep compressive sensing (CS) framework is built to conduct image reconstructions in classical SPIs, where a novel compression network is proposed to enable collaborative sparsity in discretized feature space while remaining excellent coherence with the sensing basis as per CS conditions. To alleviate the underlying limitations in an end-to-end supervised training, for example, the network typically needs to be re-trained as the basis patterns, sampling ratios and so on. change, the network is trained in an unsupervised fashion with no sensing physics involved. Validation experiments are performed both numerically and physically by comparing with traditional and cutting-edge SPI reconstruction methods. Particularly, fluorescence imaging is pioneered to preliminarily examine the in vivo biodistributions. Results show that the proposed method maintains comparable image fidelity to a sCMOS camera even at a sampling ratio down to 4%, while remaining the advantages inherent in SPI. The proposed technique maintains the unsupervised and self-contained properties that highly facilitate the downstream applications in the field of compressive imaging.

The reactor network synthesis (RNS) is one of the subtopics of the chemical process synthesis, the purpose of which is to find an optimal reactor network for the specific objective function of the studied reaction system. This review discusses the principles, advantages, and disadvantages of various methods of RNS, together with the state of the research in each field. These methods could be divided into four categories: the process‐based approach (such as the attainable region, the targeting approach, and the method based on the elementary process functions), superstructure‐based approach, stochastic optimization, and parametric method. Finally, the paper discusses the existing problems of RNS in academic researches and practical applications, as well as its research directions in the future. This paper is helpful to the readers who have interests in the design and optimization of the reactor networks.