To improve shrink-proofing performance and hydrophilicity of wool fabrics, the wool fibers were modified by poly(ethylene glycol) dimethacrylate(PEGDMA) through thiol-ene click chemistry reaction. Firstly, wool fabric...To improve shrink-proofing performance and hydrophilicity of wool fabrics, the wool fibers were modified by poly(ethylene glycol) dimethacrylate(PEGDMA) through thiol-ene click chemistry reaction. Firstly, wool fabrics were reduced at room temperature with a high-efficiency disulfide bond reducing agent, tris(2-carbonxyethyl) phosphine hydrochloride(TCEP). Then the thiol-ene click chemistry reaction was initiated by dimethyl 2, 2’-azobis(2-methylpropionate)(AIBME) through the heating method. Fourier transform infrared(FTIR) spectroscopy, Raman spectroscopy, and scanning electron microscopy test results all showed that PEGDMA was successfully grafted onto wool fabric surface. Physical properties, hydrophilicity, and shrink-proofing performance were assessed. The wetting time of PEGDMA grafted wool fabrics decreased to about 3 s. After being grafted with PEGDMA, the felting shrinkage of wool fabrics rapidly decreased to about 8%. The anti-pilling properties of wool fabrics were also greatly improved to 5 class after 2 000 times of friction. Meanwhile, the load retention rate of fabrics could reach 90%. It provides a method of wool modification to improve hydrophilicity and anti-felting performance.展开更多
Based on a linear finite element space,two symmetric finite volume schemes for eigenvalue problems in arbitrary dimensions are constructed and analyzed.Some relationships between the finite element method and the fini...Based on a linear finite element space,two symmetric finite volume schemes for eigenvalue problems in arbitrary dimensions are constructed and analyzed.Some relationships between the finite element method and the finite difference method are addressed,too.展开更多
We show that the eigenfunctions of Kohn-Sham equations can be decomposed as ■ = F ψ, where F depends on the Coulomb potential only and is locally Lipschitz, while ψ has better regularity than ■.
Partial eigenvalue decomposition(PEVD) and partial singular value decomposition(PSVD) of large sparse matrices are of fundamental importance in a wide range of applications, including latent semantic indexing, spectra...Partial eigenvalue decomposition(PEVD) and partial singular value decomposition(PSVD) of large sparse matrices are of fundamental importance in a wide range of applications, including latent semantic indexing, spectral clustering, and kernel methods for machine learning. The more challenging problems are when a large number of eigenpairs or singular triplets need to be computed. We develop practical and efficient algorithms for these challenging problems. Our algorithms are based on a filter-accelerated block Davidson method.Two types of filters are utilized, one is Chebyshev polynomial filtering, the other is rational-function filtering by solving linear equations. The former utilizes the fastest growth of the Chebyshev polynomial among same degree polynomials; the latter employs the traditional idea of shift-invert, for which we address the important issue of automatic choice of shifts and propose a practical method for solving the shifted linear equations inside the block Davidson method. Our two filters can efficiently generate high-quality basis vectors to augment the projection subspace at each Davidson iteration step, which allows a restart scheme using an active projection subspace of small dimension. This makes our algorithms memory-economical, thus practical for large PEVD/PSVD calculations. We compare our algorithms with representative methods, including ARPACK, PROPACK, the randomized SVD method, and the limited memory SVD method. Extensive numerical tests on representative datasets demonstrate that, in general, our methods have similar or faster convergence speed in terms of CPU time, while requiring much lower memory comparing with other methods. The much lower memory requirement makes our methods more practical for large-scale PEVD/PSVD computations.展开更多
基金National Natural Science Foundation of China (No.31771039)Scientific Research Fund of National Innovation Center of Advanced Dyeing and Finishing Technology,China (No.ZJ2021B03)。
文摘To improve shrink-proofing performance and hydrophilicity of wool fabrics, the wool fibers were modified by poly(ethylene glycol) dimethacrylate(PEGDMA) through thiol-ene click chemistry reaction. Firstly, wool fabrics were reduced at room temperature with a high-efficiency disulfide bond reducing agent, tris(2-carbonxyethyl) phosphine hydrochloride(TCEP). Then the thiol-ene click chemistry reaction was initiated by dimethyl 2, 2’-azobis(2-methylpropionate)(AIBME) through the heating method. Fourier transform infrared(FTIR) spectroscopy, Raman spectroscopy, and scanning electron microscopy test results all showed that PEGDMA was successfully grafted onto wool fabric surface. Physical properties, hydrophilicity, and shrink-proofing performance were assessed. The wetting time of PEGDMA grafted wool fabrics decreased to about 3 s. After being grafted with PEGDMA, the felting shrinkage of wool fabrics rapidly decreased to about 8%. The anti-pilling properties of wool fabrics were also greatly improved to 5 class after 2 000 times of friction. Meanwhile, the load retention rate of fabrics could reach 90%. It provides a method of wool modification to improve hydrophilicity and anti-felting performance.
基金the National Natural Science Foundation of China(Grant No.10425105)the National Basic Research Program of China(Grant No.2005CB321704)
文摘Based on a linear finite element space,two symmetric finite volume schemes for eigenvalue problems in arbitrary dimensions are constructed and analyzed.Some relationships between the finite element method and the finite difference method are addressed,too.
基金supported by National Natural Science Foundation of China (Grant No. 91330202)the Funds for Creative Research Groups of China (Grant No. 11321061)+1 种基金National Basic Research Program of China (Grant No. 2011CB309703)the National Center for Mathematics and Interdisciplinary Sciences of the Chinese Academy of Sciences
文摘We show that the eigenfunctions of Kohn-Sham equations can be decomposed as ■ = F ψ, where F depends on the Coulomb potential only and is locally Lipschitz, while ψ has better regularity than ■.
基金supported by National Science Foundation of USA (Grant Nos. DMS1228271 and DMS-1522587)National Natural Science Foundation of China for Creative Research Groups (Grant No. 11321061)+1 种基金the National Basic Research Program of China (Grant No. 2011CB309703)the National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences
文摘Partial eigenvalue decomposition(PEVD) and partial singular value decomposition(PSVD) of large sparse matrices are of fundamental importance in a wide range of applications, including latent semantic indexing, spectral clustering, and kernel methods for machine learning. The more challenging problems are when a large number of eigenpairs or singular triplets need to be computed. We develop practical and efficient algorithms for these challenging problems. Our algorithms are based on a filter-accelerated block Davidson method.Two types of filters are utilized, one is Chebyshev polynomial filtering, the other is rational-function filtering by solving linear equations. The former utilizes the fastest growth of the Chebyshev polynomial among same degree polynomials; the latter employs the traditional idea of shift-invert, for which we address the important issue of automatic choice of shifts and propose a practical method for solving the shifted linear equations inside the block Davidson method. Our two filters can efficiently generate high-quality basis vectors to augment the projection subspace at each Davidson iteration step, which allows a restart scheme using an active projection subspace of small dimension. This makes our algorithms memory-economical, thus practical for large PEVD/PSVD calculations. We compare our algorithms with representative methods, including ARPACK, PROPACK, the randomized SVD method, and the limited memory SVD method. Extensive numerical tests on representative datasets demonstrate that, in general, our methods have similar or faster convergence speed in terms of CPU time, while requiring much lower memory comparing with other methods. The much lower memory requirement makes our methods more practical for large-scale PEVD/PSVD computations.
