Effect of amine double-functionalization on CO<Subscript>2</Subscript> adsorption behaviors of silica gel-supported adsorbents

Amine double-functionalized adsorbents were fabricated using silica gel as supports and their capabilities for CO₂ capture were examined. Aminopropyltrimethoxysilane (1N-APS), and N¹-(3-trimethoxysilylpropyl)diethylenetriamine (3N-APS) were used as grafted amine compounds, and tetraethylenepentamine and polyethyleneimine were used as impregnated species. The influence of double-functionalization method on the CO₂ adsorption performance and textural properties of adsorbents was investigated. The adsorption capacity, the amine efficiency, and the thermal stability of double-functionalized sorbents depend strongly upon molecular variables associated with two different functional states (i.e., chemically grafted and physically impregnated amines). The temperature dependence of adsorption isotherms reveals that the CO₂ adsorption behavior in the double-functionalized adsorbents follow the diffusion limitation model proposed by Xu et al. (Energy Fuels 16:1463–1469, 2002) where the CO₂ adsorption is helped by the diffusion of impregnated amines. It is also found that the adsorption isotherm in the double-functionalized sorbent system with a proper choice for grafted and impregnated amines is nearly independent of temperature, which may offer a novel means to fabricate practically useful sorbents that can be used in a wide range of temperature without loss of CO₂ adsorption capacity.     

Mesophilic Acidogenesis of Food Waste-Recycling Wastewater: Effects of Hydraulic Retention Time,pH, and Temperature

The effects of hydraulic retention time (HRT), pH, and operating temperature (T _OP) on the degradation of food waste-recycling wastewater (FRW) were investigated in laboratory-scale hydrolysis/acidogenesis reactors. Response surface analysis was used to approximate the production of volatile organic acids and degradation of volatile suspended solids (VSS), carbohydrate, protein, and lipid with regard to the independent variables (1?≤?HRT?≤?3 days, 4?≤?pH?≤?6, 25?≤?T _OP?≤?45 °C). Partial cubic models adequately approximated the corresponding response surfaces at α?<?5 %. The physiological conditions for maximum acidification (0.4 g TVFA?+?EtOH/g VS_added) and the maximal degradation of VSS (47.5 %), carbohydrate (92.0 %), protein (17.7 %), and lipid (73.7 %) were different. Analysis of variance suggested that pH had a great effect on the responses in most cases, while T _OP and HRT, and their interaction, were significant in some cases. Denaturing gradient gel electrophoresis analysis revealed that Sporanaerobacter acetigenes, Lactobacillus sp., and Eubacterium pyruvivorans-like microorganisms might be main contributors to the hydrolysis and acidogenesis of FRW. Biochemical methane potential test confirmed higher methane yield (538.2 mL CH₄/g VS_added) from an acidogenic effluent than from raw FRW.     

Free Superoxide is an Intermediate in the Production of H2O2 by Copper(I)‐Aβ Peptide and O2

Oxidative stress is considered as an important factor and an early event in the etiology of Alzheimer's disease (AD). Cu bound to the peptide amyloid‐β (Aβ) is found in AD brains, and Cu‐Aβ could contribute to this oxidative stress, as it is able to produce in vitro H₂O₂ and HO^. in the presence of oxygen and biological reducing agents such as ascorbate. The mechanism of Cu‐Aβ‐catalyzed H₂O₂ production is however not known, although it was proposed that H₂O₂ is directly formed from O₂ via a 2‐electron process. Here, we implement an electrochemical setup and use the specificity of superoxide dismutase‐1 (SOD1) to show, for the first time, that H₂O₂ production by Cu‐Aβ in the presence of ascorbate occurs mainly via a free O₂^.? intermediate. This finding radically changes the view on the catalytic mechanism of H₂O₂ production by Cu‐Aβ, and opens the possibility that Cu‐Aβ‐catalyzed O₂^.? contributes to oxidative stress in AD, and hence may be of interest.     

Estimation of Polymerization Conditions Needed to Make Ethylene/1‐olefin Copolymers with Specific Microstructures Using Artificial Neural Networks

Two artificial neural network models (forward and inverse) are developed to describe ethylene/1‐olefin copolymerization with a catalyst having two site types using training and testing datasets obtained from a polymerization kinetic model. The forward model is applied to predict the molecular weight and chemical composition distributions of the polymer from a set of polymerization conditions, such as ethylene concentration, 1‐olefin concentration, cocatalyst concentration, hydrogen concentration, and polymerization temperature. The results of the forward model agree well with those from the kinetic model. The inverse model is applied to determine the polymerization conditions to produce polymers with desired microstructures. Although the inverse model generates multiple solutions for the general case, unique solutions are obtained when one of the three key process parameters (ethylene concentration, 1‐olefin concentration, and polymerization temperature) is kept constant. The proposed model can be used as an efficient tool to design materials from a set of polymerization conditions.

     

Effect of Prepolymerization on the Kinetics of Ethylene Polymerization and Ethylene/1‐Hexene Copolymerization with a Ziegler–Natta Catalyst in Slurry Reactors

The effect of prepolymerization on ethylene homopolymerization and ethylene/1‐hexene copolymerization with a commercial TiCl₄/MgCl₂ catalyst was investigated and the apparent homo‐ and copolymerization rate constants were estimated by varying polymerization temperature, pressure, time, and 1‐hexene/ethylene molar ratio during the prepolymerization. The apparent rate constants for activation, propagation, and deactivation depend on the prepolymerization conditions, showing that the prepolymerization stage strongly regulates the behavior of the catalyst in the main polymerization. Interestingly, the surface morphology of the prepolymer particles correlates to and explains these changes in polymerization kinetics behavior.

     

Efficient Hole‐Transporting Materials with Triazole Core for High‐Efficiency Perovskite Solar Cells

Efficient hole‐transporting materials (HTMs), TAZ‐[MeOTPA]₂ and TAZ‐[MeOTPATh]₂ incorporating two electron‐rich diphenylamino side arms, through direct linkage or thiophen bridges, respectively, on the C3‐ and C5‐positions of a 4‐phenyl‐1,2,4‐triazole core were synthesized. These synthetic HTMs with donor–acceptor type molecular structures exhibited effective intramolecular charge transfer for improving the hole‐transporting properties. The structural modification of HTMs by thiophene bridging might increase intermolecular π–π stacking in the solid state and afford a better spectral response because of their increased π‐conjugation length. Perovskite‐based cells using TAZ‐[MeOTPA]₂ and TAZ‐[MeOTPATh]₂ as HTMs afforded high power conversion efficiencies of 10.9 % and 14.4 %, respectively, showing a photovoltaic performance comparable to that obtained using spiro‐OMeTAD. These synthetically simple and inexpensive HTMs hold promise for replacing the more expensive spiro‐OMeTAD in high‐efficiency perovskite solar cells.     

Theoretical studies on farnesyl transferase: evidence for thioether product coordination to the active-site zinc sphere

Farnesyltransferase (FTase), an interesting zinc metaloenzyme, has been the subject of great attention in anticancer research over the last decade. However, despite the major accomplishments in the field, some very pungent questions on the farnesylation mechanism still persist. In this study, the authors have analyzed a mechanistic paradox that arises from the existence of several contradicting and inconclusive experimental evidence regarding the existence of direct coordination between the active-site zinc cation and the thioether from the farnesylated peptide product, which include UV-vis spectroscopy data on a Co(2+)-substituted FTase, two X-ray crystallographic structures of the FTase-product complex, and extended X-ray absorption fine structure results. Using high-level theoretical calculations on two models of different sizes, and QM/MM calculations on the full enzyme, the authors have shown that the farnesylated product is Zn coordinated, and that a subsequent step where this Zn bond is broken is coherent with the available kinetic results. Furthermore, an explanation for the contradicting experimental evidence is suggested.     

Solvent control of charge localization in 11-bond bridged dinitroaromatic radical anions

The optical spectra of 4,4'-dinitrostilbene (1-) and 4,4'-dinitrotolane (2-) radical anions show the narrow band widths and partially resolved vibrational structure exhibited by charge-delocalized dinitroaromatic radical anions in the solvents THF, HMPA, and DMPU (dimethylpropyleneurea). Both show the broad, nearly Gaussian-shaped bands found for charge-localized intervalence compounds in DMF, DMSO, and MeCN, with the transition energy of the band maximum, which equals the vertical reorganization energy (lambda) for localized intervalence compounds, increasing in that order. In contrast, 4,4'-dinitroazobenzene (3-) remains delocalized in these solvents, although the line width required to simulate the vibrational structure increases by 200 cm-1 in DMF and 400 cm-1 in MeCN compared to HMPA. The change from localized to delocalized spectra as a function of solvent establishes the transition energy for which delocalization occurs and demonstrates that, as predicted, the Hush method substantially underestimates the electronic coupling for compounds that lie near the borderline.     

Study of the application of multiway multivariate techniques to model data from an industrial fermentation process

Several multivariate statistical techniques have been extensively proposed for monitoring industrial processes. In this paper, multiway extensions of two such techniques: multiway principal component analysis (MPCA) and multiway partial least squares regression (MPLS) were applied to a large data set from an industrial pilot-scale fermentation process to improve process knowledge. The MPCA model is able to diagnose faults occurring in the process whether they affect or not process productivity while the MPLS model enables the prediction of final product concentration and the detection of faults that will influence the fermentation productivity.