Engineering最新文献

Infections caused by intracellular bacterial pathogens are difficult to treat since most antibiotics have low cell permeability and undergo rapid degradation within cells. The rapid development and dissemination of antimicrobial–resistant strains have exacerbated this dilemma. With the increasing knowledge of host–pathogen interactions, especially bacterial strategies for survival and proliferation within host cells, host-directed therapy (HDT) has attracted increased interest and has emerged as a promising anti-infection method for treating intracellular infection. Herein, we applied a cell-based screening approach to a US Food and Drug Administration (FDA)-approved drug library to identify compounds that can inhibit the intracellular replication of Salmonella Typhimurium (S. Typhimurium). This screening allowed us to identify the antidiarrheal agent loperamide (LPD) as a potent inhibitor of S. Typhimurium intracellular proliferation. LPD treatment of infected cells markedly promoted the host autophagic response and lysosomal activity. A mechanistic study revealed that the increase in host autophagy and elimination of intracellular bacteria were dependent on the high expression of glycoprotein nonmetastatic melanoma protein B (GPNMB) induced by LPD. In addition, LPD treatment effectively protected against S. Typhimurium infection in Galleria mellonella and mouse models. Thus, our study suggested that LPD may be useful for the treatment of diseases caused by intracellular bacterial pathogens. Moreover, LPD may serve as a promising lead compound for the development of anti-infection drugs based on the HDT strategy.

Reactive transport equations in porous media are critical in various scientific and engineering disciplines, but solving these equations can be computationally expensive when exploring different scenarios, such as varying porous structures and initial or boundary conditions. The deep operator network (DeepONet) has emerged as a popular deep learning framework for solving parametric partial differential equations. However, applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures. To address this issue, we propose the Porous-DeepONet, a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks (CNNs) to learn the solution operators of parametric reactive transport equations in porous media. By incorporating CNNs, we can effectively capture the intricate features of porous media, enabling accurate and efficient learning of the solution operators. We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions, multiple phases, and multi-physical fields through five examples. This approach offers significant computational savings, potentially reducing the computation time by 50–1000 times compared with the finite-element method. Our work may provide a robust alternative for solving parametric reactive transport equations in porous media, paving the way for exploring complex phenomena in porous media.

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

Oxy-combustion is a promising carbon-capture technology, but atmospheric-pressure oxy-combustion has a relatively low net efficiency, limiting its application in power plants. In pressurized oxy-combustion (POC), the boiler, air separation unit, flue gas recirculation unit, and CO₂ purification and compression unit are all operated at elevated pressure; this makes the process more efficient, with many advantages over atmospheric pressure, such as low NO_x emissions, a smaller boiler size, and more. POC is also more promising for industrial application and has attracted widespread research interest in recent years. It can produce high-pressure CO₂ with a purity of approximately 95%, which can be used directly for enhanced oil recovery or geo-sequestration. However, the pollutant emissions must meet the standards for carbon capture, storage, and utilization. Because of the high oxygen and moisture concentrations in POC, the formation of acids via the oxidation and solution of SO_x and NO_x can be increased, causing the corrosion of pipelines and equipment. Furthermore, particulate matter (PM) and mercury emissions can harm the environment and human health. The main distinction between pressurized and atmospheric-pressure oxy-combustion is the former’s elevated pressure; thus, the effect of this pressure on the pollutants emitted from POC—including SO_x, NO_x, PM, and mercury—must be understood, and effective control methodologies must be incorporated to control the formation of these pollutants. This paper reviews recent advances in research on SO_x, NO_x, PM, and mercury formation and control in POC systems that can aid in pollutant control in such systems.

To equip data-driven dynamic chemical process models with strong interpretability, we develop a light attention–convolution–gate recurrent unit (LACG) architecture with three sub-modules—a basic module, a brand-new light attention module, and a residue module—that are specially designed to learn the general dynamic behavior, transient disturbances, and other input factors of chemical processes, respectively. Combined with a hyperparameter optimization framework, Optuna, the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process. The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models, including the feedforward neural network, convolution neural network, long short-term memory (LSTM), and attention-LSTM. Moreover, compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1, the LACG parameters are demonstrated to be interpretable, and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM. This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling, paving a route to intelligent manufacturing.

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.