The quality of gas sensor array is directly related to the performance of the electronic nose, which makes the optimization of sensor array a key issue in the study of electronic noses. A new sensor array optimization method is proposed based on Random Forest by using the Gini importance as the new measure of sensor contributions. An optimal sensor array of two sensors is built up targeting to classify CO, CH4 and their mixtures from an initial array composed of six sensors based on the method. Recognition results with the selected and other sensors by Random Forest, Back Propagation Neural Network and Support Vector Machine prove the effectiveness of the proposed array optimization algorithm.

Chemical functionalization is an effective approach to address interfacial deterioration caused by environmental exposure in cellulose nanofiber (CNF)-epoxy nanocomposites. However, how functionalization affects interfacial deterioration and durability of nanocomposites in erosive environment is still lacked. In this work, the global mechanical properties and local interfacial intermolecular behavior of pristine and functionalized CNF-reinforced nanocomposites are investigated through molecular dynamics simulations. The results show that functionalization can enhance the interfacial energy barrier and debonding stress by 43% and 57%, respectively. Functionalized CNF inhibits the slippage of epoxy chains, ensuring better interfacial adhesion and efficient stress transfer between fiber and matrix. Functional groups promote the formation of interfacial bridging and topological structures and weaken the hydrogen bonding ability of water molecules, leading to stronger intermolecular adsorption effect and better interfacial integrity. The epoxy molecular configuration evolution and intermolecular interactions, caused by the functionalization of CNF in the interfacial region, enhance the interfacial erosion resistance, contributing to the durability of the nanocomposites. This study reveals the in-depth interfacial deterioration mechanism of functionalized nanocomposites under erosive environment, inspiring a novel strategy for the design of durable CNF-reinforced nanocomposites.

Due to the high load-bearing capacity and light weight, composite leaf spring with variable width and variable thickness has been increasingly used in the automobile industry to replace the conventional steel leaf spring with a heavy weight. The optimum structural design of composite leaf spring is particularly favorable for the weight reduction. In this study, an effective algorithm is developed for structural optimization of composite leaf spring. The mechanical performance of composite leaf spring with designed dimensions is characterized using a combined experimental and computational approach. Specifically, the composite leaf spring with variable width and variable thickness was prepared using the filament winding process, and the three-dimensional finite element (FE) model of the designed composite leaf spring is developed. The experimental sample and FE model of composite leaf spring are tested under the three-point bending method. From experimental and simulation results, it is shown that the bending stiffness of the designed leaf spring meets the design requirement in the automotive industry, while the results of stress calculation along all directions meet the requirements of material strength requirement. The developed algorithm contributes to the design method for optimizing the stiffness and strength performance of the composite leaf spring.