Physical characteristics of AFEX-pretreated and densified switchgrass,prairie cord grass,and corn stover

The goal of the study was to evaluate and compare the physical properties of control, pretreated and densified corn stover, switchgrass, and prairie cord grass samples. Ammonia Fiber Expansion (AFEX) pretreated switchgrass, corn stover, and prairie cord grass samples were densified by using the comPAKco device developed by Federal Machine Company of Fargo, ND. The densified biomass were referred as “PAKs” in this study. All feedstocks were ground into three different grind size of 2, 4 and 8 mm prior to AFEX pretreatment and the impact of grinding on pellet properties was studied. The results showed that the physical properties of AFEX-PAKed material were not influenced by the initial grind size of the feedstocks. The bulk density of the AFEX-PAKed biomass increased by 1.2–6 fold as compared to untreated and AFEX-pretreated materials. The durability of the AFEX-PAKed materials were between 78.25 and 95.2%, indicating that the AFEX-PAKed biomass can be transported easily. To understand the effect of storage on the physical properties of these materials, samples were stored in the ambient condition (20 ± 2 °C and 70 ± 5% relative humidity) for six months. After storage, thermal properties of the biomass did not change but glass transition temperature decreased. The water absorption index and water solubility index of AFEX-treated and AFEX-PAKed biomass showed mixed trends after storage. Moisture content decreased and durability increased upon storage.     

Questioning numerical integration methods for microsphere (and microplane) constitutive equations

In the last few years, more and more complex microsphere models have been proposed to predict the mechanical response of various polymers. Similarly than for microplane models, they consist in deriving a one-dimensional force vs. stretch equation and to integrate it over the unit sphere to obtain a three-dimensional constitutive equation. In this context, the focus of authors is laid on the physics of the one-dimensional relationship, but in most of the case the influence of the integration method on the prediction is not investigated.Here we compare three numerical integration schemes: a classical Gaussian scheme, a method based on a regular geometric meshing of the sphere, and an approach based on spherical harmonics. Depending on the method, the number of integration points may vary from 4 to 983,040! Considering simple quantities, i.e. principal (large) strain invariants, it is shown that the integration method must be carefully chosen. Depending on the quantities retained to described the one-dimensional equation and the required error, the performances of the three methods are discussed. Consequences on stress–strain prediction are illustrated with a directional version of the classical Mooney–Rivlin hyperelastic model. Finally, the paper closes with some advices for the development of new microsphere constitutive equations.     

基于小角度高位技术的钻孔初采期瓦斯治理

回采工作面初采期瓦斯涌出不均衡,易出现瓦斯超限现象。针对新源煤矿近距离煤层群开采、瓦斯涌出量大等特点,提出了小角度高位钻孔抽采治理初采期瓦斯的方法;2219工作面应用实践表明,初采期内,小角度高位钻孔抽采瓦斯纯量最高5.25m3/min,瓦斯抽采率64.02%,回风流瓦斯浓度最高为0.32%,上隅角最高瓦斯浓度0.96%,未出现瓦斯超限现象,取得了很好的初采期瓦斯治理效果。     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Quality enhancement of the abundant under‐valued crustacean,lobster krill (Munida spp.), during its chilled storage

Lobster krill (Munida genus) represents an under‐valued crustacean frequently caught on European fishing banks. In this work, its sensory, microbiological and biochemical qualities were evaluated during chilled storage. Additionally, the effects of a prestorage antimelanosic treatment consisting of soaking in sodium metabisulphite (SMB) solutions at two different concentrations (0.25% and 0.75%) were also studied. SMB prestorage treatment provided lobster specimens that still exhibited acceptable sensory quality after 10 days of storage, while control specimens were unacceptable at that time. SMB treatment also resulted in a significant (P < 0.05) inhibition of microbial growth, mainly of Enterobacteriaceae, psychrotrophes and proteolytic bacteria. Low lipid oxidation levels were observed for all batches; however, a significantly higher (P < 0.05) retention of polyunsaturated fatty acids was found in SMB‐treated lobster, especially in the 0.75% SMB batch. The results presented here open the way to the potential commercialisation of currently under‐utilised lobster krill as a chilled product.     

Electronic structure and absolute band edge position of tetragonal AgInS2 photocatalyst: A hybrid density functional study

Energy bands, effective mass of carriers, absolute band edge positions and optical properties of tetragonal AgInS₂ were calculated using a first-principles approach with the exchange correlation described by B3LYP hybrid functional. The results indicate that tetragonal AgInS₂ has a direct band gap of 1.93 eV, which reproduce well experimental value. Calculated effective masses of electrons and holes are both small which are beneficial to separation and migration of electron and hole pairs. This implies that AgInS₂ has good photocatalytic performance. The calculated optical characteristics indicate that AgInS₂ has a slight anisotropy for both the real and imaginary parts of the dielectric function and exhibits large optical absorption in the visible light region. Furthermore, the calculated band edge positions in (100), (010) and (001) surfaces indicate that tetragonal AgInS₂ is beneficial to the reduction and oxidation of water to hydrogen and oxygen under visible light irradiation.     

Mg0.5NixZn0.5-xFe2O4 spinel as a sustainable magnetic nano-photocatalyst with dopant driven band shifting and reduced recombination for visible and solar degradation of Reactive Blue-19

Focussing on visible light active ferrites for high performance removal of noxious pollutants, we report the synthesis of Mg_0.5Ni_xZn_0.5-xFe₂O₄ (x = 0.1, 0.2, 0.3, 0.4, & 0.5) ferrite nanoparticle for degradation of reactive blue-19 (RB-19). Lattice parameters calculated using intense X-ray diffraction (XRD) peaks and Nelson-Riley plots (N-R plot) are in well agreement with each other. The sample Mg_0.5Ni_0.4Zn_0.1Fe₂O₄ (M5N4) exhibits best performance with 99.5% RB-19 degradation in 90 min under visible light. Photoluminescence (PL) results confirm that recombination of charge carriers is highly reduced in the photocatalyst. Scavenging experiments suggest that O₂⁻ radicals were the dominant species responsible for photocatalytic performance. The photocatalytic mechanism was explained in terms of dopant driven shifting of conduction bands and valence bands (calculated by Mott-Schottky plots). The thermodynamic probability of radical generation along with role of redox cycles of metal ions has been discussed in the mechanism. The dye degradation was ascertained by detection of intermediates via mass spectrometry analysis and a possible degradation route was also predicted. The findings in this work provide intriguing opportunities to modify the electronic band structure of spinel ferrites for visible and solar light photocatalytic activity for environmental detoxification.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.