Arsenate Anion-π Interactions on Amine-Modified Polydivinylbenzene in Aqueous Systems: Experimental and Theoretical Investigation.

Anion-π interactions aiding in the adsorption of anions in the solution phase, though challenging to quantify, have attracted a lot of attention in supramolecular chemistry. We present the design of a polymer adsorbent that quantifies the adsorption of arsenate ions experimentally by optimizing anion-π interactions in a purely aqueous system and use density functional theory to compare these results with theoretical data. Arsenate anions are removed from water by amine-functionalized polydivinylbenzene using the comonomer 1-vinyl-1,2,4-triazole, which was cross-linked with divinylbenzene via radical polymerization in a hydrothermal procedure. The amine-functionalized polydivinylbenzene successfully removed arsenate anions from water with a capacity of 46 mg g-1, a 70% increase compared to the nonfunctionalized polydivinylbenzene (27 mg g-1) capacity under the same conditions. Adsorption is best described by the Sips isotherm model with a correlation coefficient R2 factor of 0.99, indicating that adsorption sites are homogeneous, and adsorption occurred by forming a monolayer. Kinetic studies indicated that adsorption is second order in the amine-functionalized polydivinylbenzene. Computational studies using density functional theory showed that the 1-vinyl-1,2,4-triazole comonomer improved the thermodynamic stability of the anionic-π interactions of polydivinylbenzene with arsenate anions. Electrostatic interactions dominate the mechanism of adsorption in polydivinylbenzene compared to the anion-induced interactions that dominate adsorption in amine-functionalized polydivinylbenzene.

Synthesis of Large Mesoporous-Macroporous and High Pore Volume, Mixed Crystallographic Phase Manganese Oxide, Mn2O3/Mn3O4 Sponge.

The controlled synthesis of mixed crystallographic phase Mn2O3/Mn3O4 sponge material by varying heating rates and isothermal segments provides valuable information about the morphological and physical properties of the obtained sample. The well-characterized Mn2O3/Mn3O4 sponge and applicability of difference in reactivity of H2 and CO2 desorbed during the synthesis provide new developments in the synthesis of metal oxide materials with unique morphological and surface properties. We report the preparation of a Mn2O3/Mn3O4 sponge using a metal nitrate salt, water, and Dextran, a biopolymer consisting of glucose monomers. The Mn2O3/Mn3O4 sponge prepared at 1 °C·min-1 heating rate to 500 °C and held isothermally for 1 h consisted of large mesopores-macropores (25.5 nm, pore diameter) and a pore volume of 0.413 mL/g. Furthermore, the prepared Mn2O3/Mn3O4 and 5 mol %-Fe-Mn2O3/Mn3O4 sponges provide potential avenues in the development of solid-state catalyst materials for alcohol and amine oxidation reactions.

Photocatalytic Water Splitting-The Untamed Dream: A Review of Recent Advances.

Photocatalytic water splitting using sunlight is a promising technology capable of providing high energy yield without pollutant byproducts. Herein, we review various aspects of this technology including chemical reactions, physiochemical conditions and photocatalyst types such as metal oxides, sulfides, nitrides, nanocomposites, and doped materials followed by recent advances in computational modeling of photoactive materials. As the best-known catalyst for photocatalytic hydrogen and oxygen evolution, TiO2 is discussed in a separate section, along with its challenges such as the wide band gap, large overpotential for hydrogen evolution, and rapid recombination of produced electron-hole pairs. Various approaches are addressed to overcome these shortcomings, such as doping with different elements, heterojunction catalysts, noble metal deposition, and surface modification. Development of a photocatalytic corrosion resistant, visible light absorbing, defect-tuned material with small particle size is the key to complete the sunlight to hydrogen cycle efficiently. Computational studies have opened new avenues to understand and predict the electronic density of states and band structure of advanced materials and could pave the way for the rational design of efficient photocatalysts for water splitting. Future directions are focused on developing innovative junction architectures, novel synthesis methods and optimizing the existing active materials to enhance charge transfer, visible light absorption, reducing the gas evolution overpotential and maintaining chemical and physical stability.

Partial oxidation of methane to methanol on boron nitride at near critical acetonitrile.

Direct catalytic conversion of methane to methanol with O2 has been a fundamental challenge in unlocking abundant natural gas supplies. Metal-free methane conversion with 17% methanol yield based on the limiting reagent O2 at 275 °C was achieved with near supercritical acetonitrile in the presence of boron nitride. Reaction temperature, catalyst loading, dwell time, methane-oxygen molar ratio, and solvent-oxygen molar ratios were identified as critical factors controlling methane activation and the methanol yield. Extension of the study to ethane (C2) showed moderate yields of methanol (3.6%) and ethanol (4.5%).

Role of catalytic nitrile decomposition in tricopper complex mediated direct partial oxidation of methane to methanol.

Synthetic homogeneous system known to date performing methane to methanol conversion using O2 as terminal oxidant is unique and based on copper complex with piperazine-based ligand (Cu3L in Fig. 1) in a medium of acetonitrile. Prior work have shown that in order to achieve catalytic turnover, hydrogen peroxide is needed to regenerate the active site. We show in this paper that reaction solvent based on organic nitrile decompose concurrently with methane activation and that in the absence of either acetonitrile, Cu complex or hydrogen peroxide, the catalytic turnover does not happen. We show in this manuscript that the direct methane oxidation to methanol might have been mediated by catalytic Radziszewski oxidation between acetonitrile and H2O2. Additionally we have discovered that in the absence of methane, peroxide mediated acetonitrile decomposition also makes methanol via a background reaction which was hitherto unknown.

Energy-Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach.

There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. Here, we introduce a multistep method based on machine learning algorithms to estimate total energy on the basis of spatial coordinates and charges for various chemical structures, including organic molecules, inorganic molecules, and ions. This method quickly calculates total energy with 0.76 au in root-mean-square error (RMSE) and 1.5% in mean absolute percent error (MAPE) when tested on a database of optimized and unoptimized structures. Using similar molecular representations, experimental thermochemical properties were estimated, with MAPE as low as 6% and RMSE of 8 cal/mol·K for heat capacity in a 10-fold cross-validation.

Trends in Solid Adsorbent Materials Development for CO2 Capture.

A recent report from the United Nations has warned about the excessive CO2 emissions and the necessity of making efforts to keep the increase in global temperature below 2 °C. Current CO2 capture technologies are inadequate for reaching that goal, and effective mitigation strategies must be pursued. In this work, we summarize trends in materials development for CO2 adsorption with focus on recent studies. We put adsorbent materials into four main groups: (I) carbon-based materials, (II) silica/alumina/zeolites, (III) porous crystalline solids, and (IV) metal oxides. Trends in computational investigations along with experimental findings are covered to find promising candidates in light of practical challenges imposed by process economics.

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs).

Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focused on structural descriptors for use with ML. In this work, the use of chemical descriptors, in addition to structural descriptors, was introduced for adsorption analysis. Evaluation of structural and chemical descriptors coupled with various ML algorithms, including decision tree, Poisson regression, support vector machine and random forest, were carried out to predict methane uptake on hypothetical metal organic frameworks. To highlight their predictive capabilities, ML models were trained on 8% of a data set consisting of 130,398 MOFs and then tested on the remaining 92% to predict methane adsorption capacities. When structural and chemical descriptors were jointly used as ML input, the random forest model with 10-fold cross validation proved to be superior to the other ML approaches, with an R2 of 0.98 and a mean absolute percent error of about 7%. The training and prediction using the random forest algorithm for adsorption capacity estimation of all 130,398 MOFs took approximately 2 h on a single personal computer, several orders of magnitude faster than actual molecular simulations on high-performance computing clusters.