Preparation and properties of a metal-organic frameworks polymer material based on Sa-son seed gum capable of simultaneously absorbing liquid water and water vapor.

Atmospheric water harvesting (AWH) technology has attracted significant attention as an effective strategy to tackle the global shortage of freshwater resources. Work has focused on the use of hydrogel-based composite adsorbents in water harvesting and water conservation. The approaches adopted to make use of hygroscopic inorganic salts which subject to a "salting out" effect. In this study, we report the first use of modified UIO-66-NH2 as a functional steric cross-linker and Sa-son seed gum was used as polymeric substrate to construct super hygroscopic hydrogels by free radical copolymerization. The maximum water uptake on SMAGs (572 cm3·g-1) outperforms pure UIO-66-NH2 (317 cm3·g-1). Simultaneously, our first attempt to use it for anti-evaporation applications in an arid environment (Lanzhou, China) simulating sandy areas. The evaporation rate of the anti-evaporation material treated with 0.20 % super moisture-absorbent gels (SMAGs) decreased by 6.1 % over 64 h period under natural condition in Lanzhou, China. The prepared material can not only absorb liquid water but also water vapor, which can provide a new way for water collection and conservation technology. The design strategy of this material has wide applications ranging from atmospheric water harvesting materials to anti-evaporation technology.

Preparation and water erosion resistance properties of tara gum-g-poly (acrylic acid-co-methyl methacrylate) emulsion.

The main purpose of this paper is to synthesize a new kind of green and environmental protection emulsion, which can be used as water erosion resistant materials. Here, a non-toxic polymer was prepared by grafting acrylic acid (AA) and methyl methacrylate (MMA) onto the long chains of tara gum (TG) to synthesize a copolymer emulsion (TG-g-P (AA-co-MMA)). The structure, thermal stability, morphology and wettability of the polymer were characterized by conventional methods, and the effects of key synthesis conditions on the performance of the emulsion (viscosity) were optimized. The erosion resistance and compressive strength of polymer-treated loess and laterite soils were evaluated under laboratory conditions. The results showed that the successful grafting of AA and MMA monomers onto TG improved its thermal stability and viscosity. In soil performance tests with low amounts of polymer additive, a 0.3 wt% application of TG-g-P (AA-co-MMA) to loess could resist continuous precipitation for >30 h with an erosion rate of 2.0 %. The compressive strength of the laterite treated with 0.4 % TG-g-P (AA-co-MMA) was 3.7 MPa, which was about three times that of the untreated soil. The results from this study suggest that TG-g-P (AA-co-MMA) emulsions have good potential for soil remediation applications.

Combined application of high-throughput sequencing and UHPLC-Q/TOF-MS-based metabolomics in the evaluation of microorganisms and metabolites of dry-cured ham of different origins.

Ham fermentation relies on environmental and indigenous microorganisms forming a rich microbiome, which is pivotal to taste and flavor formation. Previous studies have focused on the appearance of differences of microorganisms and metabolites, this study aims to establish the relationship between microorganisms and metabolites over a period of two years in the fermentation of hams from Jinghua (JH2), Xuanwei (XW2), Rugao (RG2), Iberian (IB2) and Parma (PA2). We profiled bacterial communities by sequencing the V3-V4 region of the 16S rRNA genes and metabolites were analyzed using LC-Q-TOF-MS. LefSe analysis showed that different biomarkers in five ham groups. OPLS analysis showed that most differential metabolites are amino acids and were associated with four metabolic pathways. Correlation analysis implies a firm positive relationship between microorganisms and metabolites. This study provides novel insights into the taste and flavor quality of dry-cured hams of different origins due to fermentation.

Preparation and fertilizer retention/anti-leakage performances of superabsorbent composite based on hydroxypropyl methyl cellulose.

To reduce the preparation cost of superabsorbent composites (SACs) and improve the water and fertilizer retention properties of soil, hydroxypropyl methylcellulose grafted with acrylic acid, polyaspartic acid and palygorskite (HPMC-g-P(AA-co-PASP)/ATP) was synthesized by aqueous solution polymerization and used to reduce of preventing water leakage in soil. The structure, surface morphology and thermal stability of the optimized SACs were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy and thermal gravimetric analysis. Under optimum synthesis conditions, the maximum equilibrium absorption of SACs was 1785 g·g-1, 254 g·g-1, 138 g·g-1 in deionized water, tap water and NaCl solution (0.9 wt%) respectively. Application of the SACs to soils increased their water holding and water retention capacities. In addition, the reduced leaching of added urea and low water permeability of the treated soils indicated that the SACs has the potential for applications in future sustainable agriculture.

Learning with convex loss and indefinite kernels.

We consider a kind of kernel-based regression with general convex loss functions in a regularization scheme. The kernels used in the scheme are not necessarily symmetric and thus are not positive semidefinite; l(1)-norm of the coefficients in the kernel ensembles is taken as the regularizer. Our setting in this letter is quite different from the classical regularized regression algorithms such as regularized networks and support vector machines regression. Under an established error decomposition that consists of approximation error, hypothesis error, and sample error, we present a detailed mathematical analysis for this scheme and, in particular, its learning rate. A reweighted empirical process theory is applied to the analysis of produced learning algorithms, which plays a key role in deriving the explicit learning rate under some assumptions.