In this paper, we present a high-speed pick-and-place method for cell-assembly applications. In addition to the range of motion and accuracy, the agility of a manipulation system is an important parameter, but it has been so far underrated in the literature. To begin high-speed micromanipulation, obtaining 3-D positions of both the target microobject and the end effector rapidly is necessary. Controlling the residual vibration of the end effector, which is greater at high speed, is another arduous task. Successful releasing of objects in microscale is also demanding. We propose a new fast detection algorithm for both the target microobject and the end effector, which will enable us to achieve high-speed control of the system. Moreover, to realize stable grasping for very fast movements, the vibration of the system is compensated, and a controllable vibration is applied to the end effectors while performing the releasing task. High-speed control of the microhand system is demonstrated with extensive experiments consisting of pick-and-place actions of 40-60 μm microspheres; we aimed at performing the task in 1 s. A comparison with similar studies shows the advantage of the proposed automated high-speed micromanipulation system.

Before being exported, mangoes generally undergo rigorous external and internal quality inspection processes in which near-infrared (NIR) spectral approaches are favorable for grading purposes. A successful NIR-based grading system depends largely on high-quality spectral sensors and the reliability of the classifier. Motivated by the high economic impact of Cat Hoa Loc mangoes (Mangifera indica L.), we demonstrated that the sweetness of that mangoes could be precisely graded based on a random forest (RF) classifier in a three-phase approach with a low-cost Visible-Near infrared (VIS-NIR) multispectral sensor chipset. This approach is so-called RPR because RF, Partial Least Squares regression, and RF were respectively applied to consecutively determine the significant VIS-NIR responses, the good features as input variables, and the reliable RF classifier via our formulated discriminant index (DI). The experimental results confirmed that higher classification accuracy was achieved by using the extracted latent features rather than the raw VIS-NIR data. The DI was effectively used as a reliability measure to select the optimal classifier among those of identical training and testing accuracies of 100% and 82.1%, respectively. Performance comparison between the optimal RF classifier with a Support Vector Machines classifier and a multinomial logistic regression showed that the developed RF classifier was superior in various performance indices. Therefore, it is promising to extend the proposed approach to more complicated fruit grading problems with sufficient VIS-NIR datasets that are acquired from low-cost multispectral sensors.

Effects of the temperature of chitosan (CS) solutions as well as the temperature of the chamber on an electrospinning process were investigated. CS with a low molecular weight was dissolved in the solvent of trifluoroacetic acid/dichloromethane (70/30 v/v) at a concentration of 80 mg/mL for electrospinning. Both CS solution and chamber temperatures strongly affected the morphology of electrospun CS nanofibers. At the solution temperature and chamber temperature of 32°C, uniform CS nanofibers with an average diameter of 200 nm could be obtained. Although the chamber temperature is generally regarded as an unimportant parameter in the electrospinning of polymers, the experimental results demonstrated its critical effect on the electrospinning of CS.

Motivated by many exclusive and useful features of cellulose nanocrystals (CNC) and the underutilized resource of waste newspaper, this study aimed to extract CNC from waste newspapers by means of alkali and bleaching treatments followed by acid hydrolysis. The morphological structure of the obtained CNC was analysed by optical microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) methods. The remarkable removal of surface contamination and the reduction in fiber diameter during alkali and bleaching treatment were observed and the nano-dimension of the extracted CNC was revealed with the average diameter of 12.3 ± 2.8 nm. Characterization of the extracted CNC showed a high whiteness index of 80%, and high transparency of about 80% of the light at 600 nm calculated for a 0.02 mm thick nanocellulose film. Fourier transform infrared spectroscopy (FTIR) indicated that lignin, hemicellulose and other coloring agents were successfully removed. A comparably high crystallinity index of 80.15% was calculated from x-ray diffraction data. Thermogravimetric analysis showed that the product had a typical maximum thermal degradation at 300 °C. The analysis results indicated the successful extraction of good CNC from waste newspaper with the shortest processing time ever reported for acid hydrolysis with conventional alkali and bleaching pretreatment. The findings also strongly support for further research of nanocomposites reinforced by CNC produced from waste newspaper.