Study on Thin Lamination of Carbon Fiber Based on Mechanical Broadening.

Carbon fiber has excellent mechanical properties and plays an important role in modern industry. However, due to the complexity of the carbon fiber widening process, the industrial application of carbon fiber is limited. By designing the carbon fiber widening equipment of automaton, the relationship between the widening width of carbon fiber and the process parameters is studied, and the optimum developing process parameters are obtained, to improve the performance of carbon fiber composites to a certain extent. In this study, the widening process of carbon fiber was studied based on the mechanical broadening method. Firstly, an automatic broadening equipment was designed, and the effects of the initial tension, the number of straight rods, the number of convex rods, and the drawing speed on the widened width during the broadening process were discussed. The widening effect was evaluated by SEM imaging and mechanical testing. At the same time, the factors affecting the broadening width and broadening defects during the broadening process were analyzed, and the optimal broadening process parameters were obtained. The results showed that within a specific range, a higher initial tension, a greater number of convex rods, and an appropriate speed resulted in relatively smaller damage to the broadening of carbon fibers. Through the design of automatic broadening, this experiment explores optimal broadening process parameters, provides a reference for the improvement of the carbon fiber broadening process and further promotes large-scale industrial applications of carbon fiber.

Design of Twin Builder-Based Digital Twin Online Monitoring System for Crane Girders.

The crossbeam is frequently subjected to alternating loads during work as an essential load-bearing part of the crane. However, due to the large volume and the limitations of detection technology, it is impossible to realize online monitoring of the mechanical state. The ongoing advancement of ROMing and digital twin technology plays a pivotal role in facilitating the resolution of this particular issue. In this paper, we take the crane beam as the physical entity and combine the Twin Builder reduced-order technology and Deployer digital twin deployment technology to establish a digital twin of the beam. The load recognition model within the twin system exhibits a prediction error rate of ±5%. Furthermore, the accuracy of the ROM surpasses that of conventional machine learning models by a factor of 25. Upon deployment on the web platform, the results are delivered within 0.5 s, representing a substantial improvement as it is merely 1/15 of the time required for traditional 3D displays. The digital twin online monitoring system has the advantages of high accuracy and low requirements for monitoring equipment, which can be widely used in engineering practice to solve the problem that the mechanical state of large parts cannot be accurately monitored online.

Microparticle Manipulation Based on the Bulk Acoustic Wave Combined with the Liquid Crystal Backflow Effect Driving in 2D/3D Platforms.

Microparticle manipulation has been widely used in clinical diagnosis, cell separation, and biochemical analysis via optics, electronics, magnetics, or acoustic wave driving. Among them, the bulk acoustic wave (BAW) driving method has been increasingly adopted because of non-contact, easy control, and precise manipulation. However, its low manipulation efficiency limits the usage of the BAW driving in high viscosity solutions. Therefore, in order to obtain larger driving force and more flexible manipulation of microparticles, both two-dimensional (2D) and three-dimensional (3D) platforms based on the BAW and liquid crystal backflow effect (LCBE) driving in liquid crystal (LC) solutions are proposed. The driving forces applied on the microparticles allow for the change of microparticle moving direction, which is also ascertained through theory analysis combined with various driving methods. Specifically, the maximum moving speed (68.78 µm/s) of the polystyrene particles is obtained by the BAW (13 Vpp) combined with LCBE (30 V) at a low frequency of 7.2 kHz in the 2D platform. Precise position manipulation in 3D is also fulfilled through a programmable logic control model using polystyrene particles as a demonstration. In addition, red blood cells mixed with LC solutions are arranged in a line or gathered in the pressure nodes of the BAW forces along with sinusoid signals generated by various transducer combinations. Therefore, it is approved that the LC solution that induces the LCBE force could increase the microparticle manipulation efficiency in both 2D and 3D platforms. The proposed method will open up new avenues in particle manipulation and benefit a variety of applications in cell separation, drug synthesis, analytical chemistry, and others.

A plantar wearable pressure sensor based on hybrid lead zirconate-titanate/microfibrillated cellulose piezoelectric composite films for human health monitoring.

Flexible and wearable electronic sensors hold great promise for improving the quality of life, especially in the field of healthcare monitoring, owing to their low cost, flexibility, high electromechanical coupling performance, high sensitivity, and biocompatibility. To achieve high piezoelectric performance similar to that of rigid materials while satisfying the flexible requirements for wearable sensors, we propose novel hybrid films based on lead zirconate titanate powder and microfibrillated cellulose (PZT/MFC) for plantar pressure measurements. The flexible films made using the polarization process are tested. It was found that the maximum piezoelectric coefficient was 31 pC N-1 and the maximum tensile force of the flexible films was 26 N. A wide range of bending angles between 15° and 180° proves the flexibility capability of the films. In addition, the charge density shows a proportional relation with the applied mechanical force, and it could sense stress of 1 kPa. Finally, plantar pressure sensors are arranged and packaged with a film array followed by connection with the detection module. Then, the pressure curves of each point on the plantar are obtained. Through analysis of the curve, several parameters of human body motions that are important in the rehabilitation of diabetic patients and the detection of sports injury can be performed, including stride frequency, length and speed. Overall, the proposed PZT/MFC wearable plantar pressure sensor has broad application prospects in the field of sports injury detection and medical rehabilitation training.

Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue.

In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

Assessment of taste attributes of peanut meal enzymatic-hydrolysis hydrolysates using an electronic tongue.

Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry.