Mixing of Racemic Poly(L-lactide)/Poly(D-lactide) Blend with Miscible Poly(D,L-lactide):Toward All Stereocomplex-type Polylactide with Strikingly Enhanced SC Crystallizability

Stereocomplex-type polylactide(SC-PLA)consisting of alternatively arranged poly(L-lactide)(PLLA)and poly(D-lactide)(PDLA)chains has gained a good reputation as a sustainable engineering plastic with outstanding heat resistance and durability,however its practical applications have been considerably hindered by the weak SC crystallizability.Current methods used to enhance the SC crystallizability are generally achieved at the expense of the precious bio-renewability and/or bio-degradability of PLAs.Herein,we demonstrate a feasible method to address these challenges by incorporating small amounts of poly(D,L-lactide)(PDLLA)into linear high-molecular-weight PLLA/PDLA blends.The results show that the incorporation of the atactic PDLLA leads to a significant enhancement in the SC crystallizability because its good miscibility with the isotactic PLAs makes it possible to greatly improve the chain mixing between PLLA and PDLA as an effective compatibilizer.Meanwhile,the melt stability(i.e.,the stability of PLLA/PDLA chain assemblies upon melting)could also be improved substantially.Very intriguingly,SC crystallites are predominantly formed with increasing content and molecular weight of PDLLA.More notably,exclusive SC crystallization can be obtained in the racemic blends with 20 wt%PDLLA having weight-average molecular weight of above 1×10^(5)g/mol,where the chain mixing level and intermolecular interactions between the PLA enantiomers could be strikingly enhanced.Overall,our work could not only open a promising horizon for the development of all SC-PLA-based engineering plastic with exceptional SC crystallizability but also give a fundamental insight into the crucial role of PDLLA in improving the SC crystallizability of PLLA/PDLA blends.

A Feasible Decomposition Method for Constrained Equations and Its Application to Complementarity Problems

In this paper, by analyzing the propositions of solution of the convex quadratic programming with nonnegative constraints, we propose a feasible decomposition method for constrained equations. Under mild conditions, the global convergence can be obtained. The method is applied to the complementary problems. Numerical results are also given to show the efficiency of the proposed method.

An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition

In this paper,an adaptive three-term conjugate gradient method is proposed for solving unconstrained problems,which generates sufficient descent directions at each iteration.Different from the existent methods,a dynamical adjustment between Hestenes–Stiefel and Dai–Liao conjugacy conditions in our proposed method is developed.Under mild condition,we show that the proposed method converges globally.Numerical experimentation with the new method indicates that it efficiently solves the test problems and therefore is promising.