Temperature effects for isothermal polymer crystallization kinetics

We adopt the cluster size distribution model to investigate the effect of temperature on homogeneous nucleation and crystal growth for isothermal polymer crystallization. The model includes the temperature effects of interfacial energy, nucleation rate, growth and dissociation rate coefficients, and equilibrium solubility. The time dependencies of polymer concentration, number and size of crystals, and crystallinity (in Avrami plots) are presented for different temperatures. The denucleation (Ostwald ripening effect) is also investigated by comparing moment and numerical solutions of the population balance equations. Agreement between the model results and temperature-sensitive experimental measurements for different polymer systems required strong temperature dependence for the crystal-melt interfacial energy.     

Polysaccharide-based hemostats: recent developments,challenges, and future perspectives

Cellulose - Excessive hemorrhage is a leading cause of death to trauma patients, especially in combat and civilian accidents. The main priority in the medical treatment of trauma patients is to...     

Removal of oxygen from silicon by electron beam melting

Small amounts of multicrystalline silicon were melted in an electron beam furnace in different experimental conditions in order to investigate the oxygen evaporation behavior during the electron beam melting (EBM) process. The oxygen content level before and after EBM was determined by secondary ion mass spectroscopy. The oxygen content was reduced from 6.177 to 1.629 ppmw when silicon was melted completely at 15 kW with removal efficiency up to 73.6 %. After that, it decreased continually to <0.0517 ppmw when the refining time exceeded 600 s with a removal efficiency of more than 99.08 %. During the melting process, the evaporation rate of silicon is 1.10 × 10^?5 kg/s. The loss of silicon could be reduced up to 1.7 % during oxygen removal process to a desirable figure, indicating EBM is an effective method to remove oxygen from silicon and decrease the loss of silicon.     

Multi-Step Continuous-Flow Organic Synthesis: Opportunities and Challenges

Continuous-flow multi-step synthesis takes the advantages of microchannel flow chemistry and may transform the conventional multi-step organic synthesis by using integrated synthetic systems. To realize the goal, however, innovative chemical methods and techniques are urgently required to meet the significant remaining challenges. In the past few years, by using green reactions, telescoped chemical design, and/or novel in-line separation techniques, major and rapid advancement has been made in this direction. This minireview summarizes the most recent reports (2017–2020) on continuous-flow synthesis of functional molecules. Notably, several complex active pharmaceutical ingredients (APIs) have been prepared by the continuous-flow approach. Key technologies to the successes and remaining challenges are discussed. These results exemplified the feasibility of using modern continuous-flow chemistry for complex synthetic targets, and bode well for the future development of integrated, automated artificial synthetic systems.     

Superballistic wavepacket spreading in double kicked rotors

We investigate possible ways in which a quantum wavepacket spreads. We show that in a general class of double kicked rotor system, a wavepacket may undergo superballistic spreading; i.e., its variance increases as the cubic of time. The conditions for the observed superballistic spreading and two related characteristic time scales are studied. Our results suggest that the symmetry of the studied model and whether it is a Kolmogorov-Arnold-Moser system are crucial to its wavepacket spreading behavior. Our study also sheds new light on the exponential wavepacket spreading phenomenon previously observed in the double kicked rotor system.     

Resource allocation model and double-sphere crowding distance for evolutionary multi-objective optimization

Convergence speed and diversity of nondominated solutions are two important performance indicators for Multi-Objective Evolutionary Algorithms (MOEAs). In this paper, we propose a Resource Allocation (RA) model based on Game Theory to accelerate the convergence speed of MOEAs, and a novel Double-Sphere Crowding Distance (DSCD) measure to improve the diversity of nondominated solutions. The mechanism of RA model is that the individuals in each group cooperate with each other to get maximum benefits for their group, and then individuals in the same group compete for private interests. The DSCD measure uses hyper-spheres consisting of nearest neighbors to estimate the crowding degree. Experimental results on convergence speed and diversity of nondominated solutions for benchmark problems and a real-world problem show the efficiency of these two proposed techniques.