Effects of the In Situ Growth of CNTs on Ti-Coated Diamond Surfaces on the Mechanical Properties of Diamond/Aluminum Composites.

Diamond/aluminum composites have attracted significant attention as novel thermal management materials, with their interfacial bonding state and configuration playing a crucial role in determining their thermal conductivity and mechanical properties. The present work aims to evaluate the bending strength and thermal conductivity of CNT-modified Ti-coated diamond/aluminum composites with multi-scale structures. The Fe catalyst was encapsulated on the surface of Ti-coated diamond particles using the solution impregnation method, and CNTs were grown in situ on the surface of Ti-coated diamond particles using the plasma-enhanced chemical vapor deposition (PECVD) method. We investigated the influence of interface structure on the thermal conductivity and mechanical properties of diamond/aluminum composites. The results show that the CNT-modified Ti-coated diamond/aluminum composite exhibits excellent bending strength, reaching up to 281 MPa, compared to uncoated diamond/aluminum composites and Ti-coated diamond/aluminum composites. The selective bonding between diamond and aluminum was improved by the interfacial reaction between Ti and diamond particles, as well as between CNT and Al. This led to the enhanced mechanical properties of Ti-coated diamond/aluminum composites while maintaining acceptable thermal conductivity. This work provides insights into the interface's configuration design and the performance optimization of diamond/metal composites for thermal management.

HybAVPnet: a Novel Hybrid Network Architecture for Antiviral Peptides Prediction.

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models. This study introduces a novel two-step approach, named HybAVPnet, to predict antiviral peptides with a hybrid network architecture based on neural networks and traditional machine learning methods. We adopted a stacking-like structure to capture both the long-term dependencies and local evolution information to achieve a comprehensive and diverse prediction using the predicted labels and probabilities. Using an ensemble technique with the different kinds of features can reduce the variance without increasing the bias. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability.

Effect of Interfacial Strength on Mechanical Behavior of Be/2024Al Composites by Pressure Infiltration.

In this paper, two kinds of Be/2024Al composites were prepared by the pressure infiltration method using two different beryllium powders as reinforcements and 2024Al as a matrix. The effect of interfacial strength on the mechanical behavior of Be/2024Al composites was studied. Firstly, the microstructure and mechanical properties of the two composites were characterized, and then the finite element analysis (FEA) simulation was used to further illustrate the influence of interfacial strength on the mechanical properties of the two Be/2024Al composites. The mechanical tensile test results show that the tensile strength and elongation of the beryllium/2024Al composite prepared by the blocky impact grinding beryllium powder (blocky-Be/2024Al composite) are 405 MPa and 1.58%, respectively, which is superior to that of the beryllium/2024Al composite prepared by the spherical atomization beryllium powder (spherical-Be/2024Al composite), as its strength and elongation are 331 MPa and 0.38%, respectively. Meanwhile, the fracture of the former shows brittle fracture of beryllium particles and ductile fracture of aluminum, while the latter shows interface debonding. Further FEA simulation illustrates that the interfacial strength of the blocky-Be/2024Al composite is 600 MPa, which is higher than that of the spherical-Be/2024Al composite (330 MPa). Therefore, it can be concluded that the better mechanical properties of the blocky-Be/2024Al composite contribute to its stronger beryllium/aluminum interfacial strength, and the better interfacial strength might be due to the rough surface and microcrack morphology of blocky beryllium particles. These research results provide effective experimental and simulation support for the selection of beryllium powder and the design and preparation of high-performance beryllium/aluminum composites.

Effect of Nanoscale W Coating on Corrosion Behavior of Diamond/Aluminum Composites.

The stability of diamond/aluminum composite is of significant importance for its extensive application. In this paper, the interface of diamond/aluminum composite was modified by adding nanoscale W coating on diamond surface. We evaluated the corrosion rate of nanoscale W-coated and uncoated diamond/aluminum composite by a full immersion test and polarization curve test and clarified the corrosion products and corrosion mechanism of the composite. The introduction of W nanoscale coating effectively reduces the corrosion rate of the diamond/aluminum composite. After corrosion, the bending strength and thermal conductivity of the nanoscale W-coated diamond/aluminum composite are considerably higher than those of the uncoated diamond/aluminum composite. The corrosion loss of the material is mainly related to the hydrolysis of the interface product Al4C3, accompanied by the corrosion of the matrix aluminum. Our work provides guidance for improving the life of electronic devices in corrosive environments.

SuccSPred2.0: A Two-Step Model to Predict Succinylation Sites Based on Multifeature Fusion and Selection Algorithm.

Protein succinylation is a novel type of post-translational modification in recent decade years. It played an important role in biological structure and functions verified by experiments. However, it is time consuming and laborious for the wet experimental identification of succinylation sites. Traditional technology cannot adapt to the rapid growth of the biological sequence data sets. In this study, a new computational method named SuccSPred2.0 was proposed to identify succinylation sites in the protein sequences based on multifeature fusion and maximal information coefficient (MIC) method. SuccSPred2.0 was implemented based on a two-step strategy. At first, high-dimension features were reduced by linear discriminant analysis to prevent overfitting. Subsequently, MIC method was employed to select the important features binding classifiers to predict succinylation sites. From the compared experiments on 10-fold cross-validation and independent test data sets, SuccSPred2.0 obtained promising improvements. Comparative experiments showed that SuccSPred2.0 was superior to previous tools in identifying succinylation sites in the given proteins.

i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification.

Parasites can cause enormous damage to their hosts. Studies have shown that antiparasitic peptides can inhibit the growth and development of parasites and even kill them. Because traditional biological methods to determine the activity of antiparasitic peptides are time-consuming and costly, a method for large-scale prediction of antiparasitic peptides is urgently needed. We propose a computational approach called i2APP that can efficiently identify APPs using a two-step machine learning (ML) framework. First, in order to solve the imbalance of positive and negative samples in the training set, a random under sampling method is used to generate a balanced training data set. Then, the physical and chemical features and terminus-based features are extracted, and the first classification is performed by Light Gradient Boosting Machine (LGBM) and Support Vector Machine (SVM) to obtain 264-dimensional higher level features. These features are selected by Maximal Information Coefficient (MIC) and the features with the big MIC values are retained. Finally, the SVM algorithm is used for the second classification in the optimized feature space. Thus the prediction model i2APP is fully constructed. On independent datasets, the accuracy and AUC of i2APP are 0.913 and 0.935, respectively, which are better than the state-of-arts methods. The key idea of the proposed method is that multi-level features are extracted from peptide sequences and the higher-level features can distinguish well the APPs and non-APPs.