Enzymatic dyeing and functional finishing of textile fibres with ferulic acid

The catalyzed polymerization of ferulic acid (FA) by laccase from Rhus vernicifera has been studied, and its polymeric products are used for the dyeing and functional finishing of silk, wool, nylon, viscose and cotton fabrics by two methods, namely simultaneous enzymatic polymerization of FA and dyeing at 50 oC (one-step method), and enzymatic polymerization of FA at 50 oC followed by dyeing at 90 oC (two-step method). The analyses of UV-Visible and FTIR spectra show the formation of yellow poly(ferulic acid) (PFA) in which FA units are mainly linked together with C–C bonds. The colouration of PFA on fabrics occurs due to physical adsorption, and not because of interaction of covalent bond between PFA and fibres. The enzymatically dyed fabrics display yellow to orange colour hues, and pale to moderate colour depth, depending on fibre species and dyeing methods. The dyed fabrics show excellent rub fastness and staining fastness during washing, relatively weak light fastness and colour change fastness during washing; the two-step method shows better wash fastness ratings for colour change. The enzymatic dyeing of FA provides fabrics with multifunctional properties of antioxidant activity, UV-protection and deodorization

Two-Stage Metric Learning

In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points. Then, we define the distance in the input data space as the Fisher information distance on the associated statistical manifold. This induces in the input data space a new family of distance metric with unique properties. Unlike kernelized metric learning, we do not require the similarity measure to be positive semi-definite. Moreover, it can also be interpreted as a local metric learning algorithm with well defined distance approximation. We evaluate its performance on a number of datasets. It outperforms significantly other metric learning methods and SVM.Comment: Accepted for publication in ICML 201

Impact of Biochar on the Bioremediation and Phytoremediation of Heavy Metal(loid)s in Soil

Anthropogenic activities, such as mining/smelting, result in the release and accumulation of heavy metal(loid)s in soil, posing serious human health and ecological risks. Due to the persistence of metal(loid)s, not undergoing any chemical and biological degradation, they can only be either immobilized or removed by, bioremediation and phytoremediation. Biochar is increasingly being recognized as a promising, effective material that can be used to remediate various contaminations including excessive heavy metals in soil. This chapter provides an overview of the state of the art on biochar resources, production processes and result of pyrolysis, surface characteristics of biochar, interactions of biochar with soil, and associated biota (microbes and plant). Furthermore, the understanding of characteristics of biochar and the interactions of biochar with soil and biota is necessary to assess the impacts of biochar on bioremediation and phytoremediation of heavy metal contaminated soil

Valence-programmable nanoparticle architectures.

Nanoparticle-based clusters permit the harvesting of collective and emergent properties, with applications ranging from optics and sensing to information processing and catalysis. However, existing approaches to create such architectures are typically system-specific, which limits designability and fabrication. Our work addresses this challenge by demonstrating that cluster architectures can be rationally formed using components with programmable valence. We realize cluster assemblies by employing a three-dimensional (3D) DNA meshframe with high spatial symmetry as a site-programmable scaffold, which can be prescribed with desired valence modes and affinity types. Thus, this meshframe serves as a versatile platform for coordination of nanoparticles into desired cluster architectures. Using the same underlying frame, we show the realization of a variety of preprogrammed designed valence modes, which allows for assembling 3D clusters with complex architectures. The structures of assembled 3D clusters are verified by electron microcopy imaging, cryo-EM tomography and in-situ X-ray scattering methods. We also find a close agreement between structural and optical properties of designed chiral architectures