Design of Acceptors with Suitable Frontier Molecular Orbitals to Match Donors via Substitutions on Perylene Diimide for Organic Solar Cells

A series of perylene diimide (PDI) derivatives have been investigated at the CAM-B3LYP/6-31G(d) and the TD-B3LYP/6-31+G(d,p) levels to design solar cell acceptors with high performance in areas such as suitable frontier molecular orbital (FMO) energies to match oligo(thienylenevinylene) derivatives and improved charge transfer properties. The calculated results reveal that the substituents slightly affect the distribution patterns of FMOs for PDI-BI. The electron withdrawing group substituents decrease the FMO energies of PDI-BI, and the electron donating group substituents slightly affect the FMO energies of PDI-BI. The di-electron withdrawing group substituents can tune the FMOs of PDI-BI to be more suitable for the oligo(thienylenevinylene) derivatives. The electron withdrawing group substituents result in red shifts of absorption spectra and electron donating group substituents result in blue shifts for PDI-BI. The –CN substituent can improve the electron transport properties of PDI-BI. The –CH₃ group in different positions slightly affects the electron transport properties of PDI-BI.     

Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression

Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pathways. As pathways comprise knowledge frameworks widely used by human researchers, representing gene-to-pathway relationships in deep learning structures may aid in their comprehension. Here, we propose a deep neural network (PathDeep), which implements gene-to-pathway relationships in its structure. We also provide an application framework measuring the contribution of pathways and genes in deep neural networks in a classification problem. We applied PathDeep to classify cancer and normal tissues based on the publicly available, large gene expression dataset. PathDeep showed higher accuracy than fully connected neural networks in distinguishing cancer from normal tissues (accuracy = 0.994) in 32 tissue samples. We identified 42 pathways related to 32 cancer tissues and 57 associated genes contributing highly to the biological functions of cancer. The most significant pathway was G-protein-coupled receptor signaling, and the most enriched function was the G1/S transition of the mitotic cell cycle, suggesting that these biological functions were the most common cancer characteristics in the 32 tissues.     

量子化学参数法分析咪唑啉及其衍生物缓蚀机理

咪唑啉缓蚀剂是由以负电性O,S,N等原子为中心的极性基和以C,H为中心的非极性基组成。前者吸附于金属表面,后者位于离开金属的方向。当金属吸附了这类化合物时,可使表面能量状态稳定,又由于非极性基排列在金属表面形成疏水薄膜,可以抵抗电荷的移动,从而使腐蚀反应受到抑制。通过量子化学法计算缓蚀剂的缓蚀性能与EHOMO﹑ELUMO及ELUMO与EHOMO的差值ΔE关系来研究咪唑啉型缓蚀剂的亲水基团与其缓蚀性能的关系,以期为缓蚀剂的筛选、开发提供准确借鉴。     

枣泉煤分子模型构建及热解的分子模拟

以宁东枣泉煤为研究对象，使用工业分析、元素分析、X射线光电子能谱、¹³C固体核磁等表征手段和计算机辅助，构建获得枣泉煤大分子结构模型。经过分子动力学退火动力学模拟和几何结构全优化，与初始结构相比键长、键角发生明显改变，立体构型显著，芳香层片之间近似平行的排列方式明显。获得的傅里叶变换红外和¹³C固体核磁的实验与计算谱图总体吻合较好，进一步证明了构建模型的合理性。使用反应分子动力学方法模拟枣泉煤的热解过程，考察不同热解终温和升温速率对热解行为的影响。结果发现，随着温度的升高，反应速率逐渐加快。不同升温速率对枣泉煤热解过程中气体的产生有显著影响。在动力学模拟中大多产生C₁₅以下的碎片，大分子的种类则并不多。随着升温速率的增加，气、液、固三相产物整体上都呈现下降的趋势。此外，还根据反应分子动力学模拟结果追踪了热解过程中CO₂的形成机理，获得了三种不同的CO₂形成路径。     

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions

Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutional neural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understand by a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players’ strategies is to first cluster the pigmented lesions (melanoma, nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We used EfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists.     

互穿网络型高分子.NPK缓释化肥的研究

介绍了将尿素和磷酸二氢钾的缩聚物与脲醛反应,以合成一种互穿网络型高分子NPK缓释化肥的实验。研究评价了该缓释化肥的缓释性能以及合成中尿素和磷酸二氢钾的摩尔比、反应温度、甲醛的加入量对缓释性能的影响。研究结果表明:该缓释化肥中N的缓释期>120 d,P的缓释期为80~100 d,K的缓释期为70~90 d。     

超高分子量聚乙烯塑料的性能、加工及应用

综述了新型工程塑料超高分子量聚乙烯(UHMWPE)的性能、加工及应用情况。     

A model for adhesion-producing interactions of zinc oxide surfaces with alcohols,amines, and alkenes

The interactions between paint/adhesive polymers and metal surfaces that are critical for adhesion have been studied theoretically. This study used zinc oxide as a model of a galvanized steel surface, and ammonia, water, and ethylene as models for amino, hydroxy, and unsaturated functionalities in paint/adhesive polymers. Ab initio molecular orbital calculations were carried out on zinc oxide and zinc oxide dimer. Geometries were optimized at the HF/3-21G level and relative energies were calculated by CASSCF/3-21G and by MP2 with the DZP basis set of Wachters and Hay. Ethylene forms a stable complex with zinc oxide dimer that has a stabilization energy of 24.9 kcal/mol. Insertion of ethylene into zinc oxide dimer to form a stable six-membered ring adduct occurs with a surprisingly low activation energy of 8.8 kcal/mol. The binding energy of ammonia with zinc oxide dimer is 38.5 kcal/mol and the activation energy for insertion of ammonia forming covalent Zn-NH₂ and O-H bonds is calculated to be 9.6 kcal/mol. Aminolysis of zinc oxide dimer with two ammonia molecules has a predicted barrier height of 6.7 kcal/mol. The transition structure for Zn-O bond rupture with one NH₃ and one H₂O molecule is only 1.5 kcal/mol higher in energy than the reactant cluster. The calculations suggest that alkenes, amines, and alcohols could readily form covalent bonds with the ZnO surface, thereby facilitating adhesion of the polymer containing these functional groups to a galvanized surface.     

Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches

Flooding of separation columns is a severe limitation in the operation of distillation and liquid-liquid extraction columns. To observe operation conditions, machine learning algorithms are implemented to recognize the flooding behavior of separation columns on laboratory scale. Besides this, the investigated columns already provided the modular automation interface Module Type Package (MTP), which is used for data access of necessary sensor data. Hence, artificial intelligence (AI) tools with deep learning offer high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice due to complex combination of operational sensing, data analysis, and active control of the equipment. This article provides an overview on how AI-based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns for stable and robust operational conditions.     

Heat Transfer Augmentation and Entropy Generation Analysis of a Helically Coiled Tube with Internal Longitudinal Fins

Heat transfer augmentation and entropy generation were investigated for a helically coiled tube with internal longitudinal fins. The Nusselt number, friction factor, thermal‐hydraulic performance ratio, and augmentation entropy generation number were calculated and analyzed. The results indicated that the internal longitudinal fins enhance the secondary flows and increase the temperature gradient near the tube wall, which in turn increase the heat transfer. It was found that the helically coiled tube with internal longitudinal fins provides the best integrated performance over the range of computed Dean numbers. The Nusselt number rised by 20–35 % with a corresponding 27–56 % increase of the friction factor. The computed results indicated that augmentation entropy generation numbers are approximately changed between 0.012 to 0.132 levels, i.e., the novel helically coiled tube with internal longitudinal fins is more efficient than that without internal fins.     

炭黑、微珠粉填充PE-UHMW复合材料性能比较分析

对超高相对分子质量聚乙烯(PE-UHMW)和炭黑、微珠粉填充的PE-UHMW复合材料进行了拉伸、硬度和磨损性能试验。结果表明：炭黑、微珠粉对PE-UHMW拉伸性能和摩擦磨损性能的影响不同，两种填充材料加入PE-UHMW后，复合材料的拉伸强度和断裂延伸率有不同程度的下降。炭黑的加入会使PE-UHMW的硬度下降，但可较好地改善其耐磨性，而微珠粉的加入会使PE-UHMW的耐磨性下降。     

DFT Study of the Molecular and Electronic Structure of Metal-Free Tetrabenzoporphyrin and Its Metal Complexes with Zn,Cd, Al,Ga, In

The electronic and molecular structures of metal-free tetrabenzoporphyrin (H₂TBP) and its complexes with zinc, cadmium, aluminum, gallium and indium were investigated by density functional theory (DFT) calculations with a def2-TZVP basis set. A geometrical structure of ZnTBP and CdTBP was found to possess D_4h symmetry; AlClTBP, GaClTBP and InClTBP were non-planar complexes with C_4v symmetry. The molecular structure of H₂TBP belonged to the point symmetry group of D_2h. According to the results of the natural bond orbital (NBO) analysis, the M-N bonds had a substantial ionic character in the cases of the Zn(II) and Cd(II) complexes, with a noticeably increased covalent contribution for Al(III), Ga(III) and In(III) complexes with an axial –Cl ligand. The lowest excited states were computed with the use of time-dependent density functional theory (TDDFT) calculations. The model electronic absorption spectra indicated a weak influence of the nature of the metal on the Q-band position.     

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.     

Modeling and predictive control of nonlinear processes using transfer learning method

This work develops a transfer learning (TL) framework for modeling and predictive control of nonlinear systems using recurrent neural networks (RNNs) with the knowledge obtained in modeling one process transferred to another. Specifically, transfer learning uses a pretrained model developed based on a source domain as the starting point, and adapts the model to a target process with similar configurations. The generalization error for TL-based RNN (TL-RNN) is first derived to demonstrate the generalization capability on the target process. The theoretical error bound that depends on model capacity and the discrepancy between source and target domains is then utilized to guide the development of pretrained models for improved model transferability. Subsequently, the TL-RNN model is utilized as the prediction model in model predictive controller (MPC) for the target process. Finally, the simulation study of chemical reactors via Aspen Plus Dynamics is used to demonstrate the benefits of transfer learning.     

分子筛VP800-5分离氢同位素的动态实验与模型

液氮温度下用分子筛VP800-5在自行设计的单塔变压吸附装置上进行氢同位素气体分离的研究,考察了气体流量、压力与吸附床长度对分离效果的影响;在总压0.40 Mpa、总流量129.79 cm3.min-1与吸附床长度1.0 m时氢氘同位素混合气的分离因子可达到1.63.然而压力为0.0139 Mpa和0.0175 Mpa时D2和H2在分子筛VP800-5上的平衡吸附量比值仅分别为1.14和1.11.结合平衡吸附、动态吸附和分离的结果,低温吸附法能有效分离氧同位素主要是由于两者之间存在显著的动力学吸附速率差异.运用建立的柱动力学模型对氢氘吸附分离过程进行了模拟,结果表明模拟结果与实验结果吻合较好.     

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.     

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for type 2 diabetes (T2D) treatment. We first integrated databases to construct the genome-wide genetic and epigenetic networks (GWGENs), which consist of protein–protein interaction networks (PPINs) and gene regulatory networks (GRNs) for T2D and non-T2D (health), respectively. Second, the relevant “real GWGENs” are identified by system identification and system order detection methods performed on the T2D and non-T2D RNA-seq data. To simplify network analysis, principal network projection (PNP) was thereby exploited to extract core GWGENs from real GWGENs. Then, with the help of KEGG pathway annotation, core signaling pathways were constructed to identify significant biomarkers. Furthermore, in order to discover potential drugs for the selected pathogenic biomarkers (i.e., drug targets) from the core signaling pathways, not only did we train a deep neural network (DNN)-based drug–target interaction (DTI) model to predict candidate drug’s binding with the identified biomarkers but also considered a set of design specifications, including drug regulation ability, toxicity, sensitivity, and side effects to sieve out promising drugs suitable for T2D.     

Modelling the elementary mechanisms involved in grafting polymers onto metals — relationship between structure and properties of the resulting product

With the aim of modelling the molecular-level phenomena involved in the interaction between a polymerizable organic molecule (acrylonitrile) and a controlled metallic surface (polycrystalline nickel), systematic experimental investigations and theoretical calculations have been carried out on polyacrylonitrile films grafted on nickel surfaces. Is is anticipated that the results, which indicate the relationships between structure, and properties and their evolution during ageing under various stresses, will enable the laws governing the behaviour of adhesive materials to be determined.