New versatile zincic sorbent for tobacco specific nitrosamines and lead ion capture.

New carbon-doped ferric zinc oxide sorbents were fabricated to capture the environment carcinogen tobacco specific nitrosamines (TSNA) efficiently in solution, following new adsorption model of electrostatic attraction instead of traditional geometric constraints. The influence of ferric content on the structure-property of the sorbents was systemically studied with XRD, N2 adsorption-desorption and SEM methods combined with the adsorption of TSNA in different solutions. New sorbent captured 99% of 4-methylnitrosamino-1-3-pyridyl-1-butanone (NNK) in simulated surface water and 40% of TSNA in the tobacco extract solution, more than activated carbon or zeolites. Ferric ZnO sorbent took about 15 min to reach the adsorption equilibrium in the NNK or Pb(Ⅱ) solution, faster than NaZSM-5 zeolite. Moreover, the adsorbed NNK on ferric ZnO sorbent decomposed at mild conditions for the first time, providing a new way to control environment pollution.

New shape-selectivity discovered on graphene-based materials in catching tobacco specific nitrosamines.

New shape-selectivity of graphene-based materials was discovered on this article. To explore the new selectivity, the structure and surface state of graphene and carbon nanotube were examined firstly, and their specific selectivity was verified and was compared with that of ZSM-5 zeolite in aqueous solutions of tobacco specific nitrosamines (TSNA) along with dyes. These two adsorbents trapped about 55% and 70% of 4-methylnitrosamino-1-3-pyridyl-1-butanone (NNK) but only 3% of N'-nitrosonornicotine (NNN) in solution, having an obvious selectivity for the former, due to its stronger interaction with graphene. NNK on graphene sheet obtained more electrons (0.015 e) and owned larger adsorption energy (15.63 kcal mol-1) than that of NNN (0.003 e, 9.19 kcal mol-1), according to theoretical calculation and FTIR results. More 95 or 136 mg g -1 acid red 88 than methyl orange was captured by graphene or carbon nanotube, demonstrating this special and abnormal selectivity again. With new selectivity, graphene showed a higher capacity (6.9%) and shorter adorption equilibrium time (5 min) for TSNA than the typical selecive sorbent ZSM-5 zeolite (1.7% and 20 min) in tobacco solution but kept the similar selctivity to NNK, paving a new way to control the carcinogens like TSNA in environment.

Applying MSSIM combined chaos game representation to genome sequences analysis.

Converting DNA sequence to image by using chaos game representation (CGR) is an effective genome sequence pretreatment technology, which provides the basis for further analysis between the different genes. In this paper, we have constructed 10 mammal species, 48 hepatitis E virus (HEV), and 10 kinds of bacteria genetic CGR images, respectively, to calculate the mean structural similarity (MSSIM) coefficient between every two CGR images. From our analysis, the MSSIM coefficient of gene CGR images can accurately reflect the similarity degrees between different genomes. Hierarchical clustering analysis was used to calculate the class affiliation and construct a dendrogram. Large numbers of experiments showed that this method gives comparable results to the traditional Clustal X phylogenetic tree construction method, and is significantly faster in the clustering analysis process. Meanwhile MSSIM combined CGR method was also able to efficiently clustering of large genome sequences, which the traditional multiple sequence alignment methods (e.g. Clustal X, Clustal Omega, Clustal W, et al.) cannot classify.

[Study on denoising near infrared spectra of wood based on wavelet transform].

Near infrared (NIR) spectra of wood samples are often confused by a series of noise, which greatly influences accurate analytical result. In order to improve analytical precision, the authors need to pretreat the spectrum data. Derivative can correct baseline and background effects, increasing the resolution ratio of the spectra. However, it also increases the noise at the same time. The present paper aims at using wavelet transform to eliminate the noise of the near infrared first derivative spectrum of wood with the methods of 9 point smoothing spectrum, 25 point smoothing spectrum, the nonlinear wavelet hard-threshold spectrum, the nonlinear wavelet soft-threshold spectrum, 9 point smoothing+wavelet transform and 25 point smoothing spectrum+ wavelet transform. The results show that the wavelet transform has particular advantage on noise elimination of the near infrared spectra while reserving the useful information of spectrum. It can also improve the signal to noise ratio of spectrum, promising the prospect of a wide application in the wood near infrared spectroscopic analysis.