Adsorption of 4-chlorophenol from aqueous solutions by xad-4 resin: isotherm, kinetic, and thermodynamic analysis

Removal of 4-chlorophenol (4-CP) from synthetic aqueous solutions through adsorption on Amberlite XAD-4 resin, a non-ionic macroreticular resins, under batch equilibrium experimental conditions at 298, 308 and 318K was investigated. It is necessary to propose a suitable model to a better understanding on the mechanism of 4-CP adsorption. For this purpose, Langmiur, Freundlich, Toth, and Redlich-Peterson (RP) isotherm models were compared. The two and three parameters in the adopted adsorption isotherm models were determined by the help of MATLAB package program. It was determined that best fitted adsorption isotherm models were obtained to be in the order: Redlich-Peterson>Langmuir>Toth>Freundlich isotherms. The pseudo-second-order kinetic model provided the best correlation to the experimental results. Results of the intra-particle diffusion model show that the pore diffusion is not the only rate limiting step. The lower correlation of the data to the Bangham's equation also represents that the diffusion of the adsorbate into pores of the sorbent is not the only rate-controlling step. The thermodynamic constants of adsorption phenomena; DeltaG degrees, DeltaH degrees, and DeltaS degrees were found as -4.17 (at 298K) kJ/mol, -42.01 kJ/mol, and -0.127 kJ/(mol K), respectively. The results showed that adsorption of 4-CP on Amberlite XAD-4, a nonionic polymeric resin was exothermic and spontaneous.     

Prediction of the space-frequency correlation function for basestation diversity reception

A model is proposed to estimate the signal statistics for urban-area base stations with highly elevated antennas. Based on the model, the envelope correlation of the received signal is estimated in the case of hybrid space-frequency diversity reception at the base. It is shown that the spatial correlation or the complex signals is determined of the mean angle of arrival and the width of the incoming beam. Useful closed-form approximations for inline and broadside antenna orientations are obtained. Comparisons between the theoretical model and available experimental data provide adequate justification for the model. It is shown that the lowest spatial correlations among all the possible angular orientations an obtained when the mean direction of the incoming signals is perpendicular to the axis of the base antennas. For this case, an antenna separation of 26λ is required to achieve an envelope correlation of 0.7 when the width of the incoming beam is 0.7°, and this separation reduces to 9λ for a width of 3°. The results indicate that the required spacings can be further reduced by using frequency diversity in conjunction with space diversity. Generalization of the model to incorporate local scattering effects is also proposed     

Neural Models for the Resonant Frequency of Electrically Thin and Thick Circular Microstrip Antennas and the Characteristic Parameters of Asymmetric Coplanar Waveguides Backed with a Conductor

Neural models for computing the resonant frequency of electrically thin and thick circular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Five learning algorithms, delta-bar-delta, extended delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The resonant frequency results of neural models are in very good agreement with the experimental results available in the literature. In this paper, the characteristic impedance and the effective permittivity of the asymmetric coplanar waveguide backed with a conductor are also computed by using only one neural model trained by the backpropagation with momentum and the extended delta-bar-delta algorithms. When the performances of neural models are compared with each other, the best results for test are obtained from the multilayered perceptrons trained by the extended delta-bar-delta algorithm.     

不同非热加工技术对百香果果浆杀菌效果及品质变化的比较

为评价不同非热杀菌方式对果蔬原料杀菌效果及品质的影响,该研究分别采用超高压技术（High Hydrostatic Pressure,HHP）、低温等离子体技术（Cold Plasma,CP）和辐照技术三种方式对百香果鲜榨果浆进行杀菌处理。结果表明,三种非热处理方式对果浆中的微生物（2.15 lg(CFU/g)、2.44 lg(CFU/g)、2.34 lg(CFU/g)）均有一定的抑制和杀灭作用,其中300 MPa及以上压力处理和3 kGy辐照量处理后果浆的菌落数均＜1 lg(CFU/g),这两种方式对百香果果浆均有较好的杀菌效果。为了进一步探究超高压处理对果浆贮藏期的影响,该研究还探讨了超高压处理前后的果浆在-20 ℃的条件下储存9个月内微生物含量的变化,实验得出,500 MPa和600 MPa处理的百香果果浆在-20 ℃下储存9个月后微生物含量仍＜2 lg(CFU/g),符合国家安全规定。在理化性质方面,超高压技术和低温等离子体技术能有效保持果浆的色泽（ΔE＜2）和风味（p＞0.05）,且能抑制果浆POD酶、PPO酶的活性,但辐照处理无法钝化PPO酶。综上所述,超高压技术能有效杀灭百香果果浆中的菌落,并能更好地保持果浆的品质,因此超高压技术在百香果果浆中具有较好的商业应用前景。     

Engraftment of Prevascularized,Tissue Engineered Constructs in a Novel Rabbit Segmental Bone Defect Model

The gold standard treatment of large segmental bone defects is autologous bone transfer, which suffers from low availability and additional morbidity. Tissue engineered bone able to engraft orthotopically and a suitable animal model for pre-clinical testing are direly needed. This study aimed to evaluate engraftment of tissue-engineered bone with different prevascularization strategies in a novel segmental defect model in the rabbit humerus. Decellularized bone matrix (Tutobone) seeded with bone marrow mesenchymal stromal cells was used directly orthotopically or combined with a vessel and inserted immediately (1-step) or only after six weeks of subcutaneous “incubation” (2-step). After 12 weeks, histological and radiological assessment was performed. Variable callus formation was observed. No bone formation or remodeling of the graft through TRAP positive osteoclasts could be detected. Instead, a variable amount of necrotic tissue formed. Although necrotic area correlated significantly with amount of vessels and the 2-step strategy had significantly more vessels than the 1-step strategy, no significant reduction of necrotic area was found. In conclusion, the animal model developed here represents a highly challenging situation, for which a suitable engineered bone graft with better prevascularization, better resorbability and higher osteogenicity has yet to be developed.     

Allele-Specific Inhibition of Histone Demethylases

Histone demethylases play a critical role in mammalian gene expression by removing methyl groups from lysine residues in degree- and site-specific manner. To specifically interrogate members and isoforms of this class of enzymes, we have developed demethylase variants with an expanded active site. The mutant enzymes are capable of performing lysine demethylation with wild-type proficiency, but are sensitive to inhibition by cofactor-competitive molecules embellished with a complementary steric “bump”. The selected inhibitors show more than 20-fold selectivity over the wild-type demethylase, thus overcoming issues typical to pharmacological and genetic approaches. The mutant–inhibitor pairs are shown to act on a physiologically relevant full-length substrate. By engineering a conserved amino acid to achieve member-specific perturbation, this study provides a general approach for studying histone demethylases in diverse cellular processes.     

Cancer‐Targeting Graphene Quantum Dots: Fluorescence Quantum Yields,Stability, and Cell Selectivity

Folic acid, due to its high affinity toward folate receptors (FR), is recognized as one of the most promising cancer targeting vectors. However, the inherent defects of low water solubility (1.6 µg mL^?1), high sensitivity toward photo‐bleaching, low fluorescent quantum yields (QYs, <0.5%) seriously limit its practical application. Herein, ultrastable, highly luminescent graphene quantum dots (GQDs) that selectively target diverse cancer cells are prepared and tested. The new GQDs present step changes compared to common folic acid through an ≈6250 times increase in water solubility (to ≈10 mg mL^?1), more than 150 times in QYs (up to ≈77%), while maintaining luminescence stability up to 98% when subjected to UV, visible light, and heating over 360 min. It is shown that the suppression of nonradiative transitions by amino groups pyrolyzed from pterin plays a key role in the mechanism of high QYs and excellent stability. The functional groups that are likely responsible for the selective targeting of cancer cells with different levels of folate receptor expression on the surface are identified. Collectively with these promising properties, the new functional graphene quantum dots may open a new avenue for cancer diagnosis, drug delivery, and therapies.     

Nanocrystalline CoCrFeNiMn high-entropy alloy with tunable ferromagnetic properties

A nanocrystalline CoCrFeNiMn high-entropy alloy(nc-HEA)with nano-multiphase structure was pre-pared by inert gas condensation(IGC)using a laser evaporation source.Encouragingly,the laser-IGC nc-HEA exhibits unexpected ferromagnetic behavior and the Curie temperature(Tc)increased nearly 10 times compared to any CoCrFeNiMn HEAs prepared by various other methods.In addition,the saturation magnetization(Ms)and Tc of the laser-IGC nc-HEA can be controlled via heat treatment,which is result-ing from the formation and structural evolution of magnetic nanophases during annealing.This work widens the design toolbox for high-performance nc-HEAs based upon laser-IGC technique.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.