Ag-embedded manganese oxide octahedral molecular sieve (Ag-OMS-2) nano-rods as efficient heterogeneous catalysts for hydration of nitriles to amides in aqueous solution.

Silver-embedded manganese oxide octahedral molecular sieve (Ag-OMS-2) nano-rods were synthesized using a pre-incorporation approach, and unambiguously characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). A highly uniform distribution of Ag nanoparticles embedded in the porous structure of OMS-2, was found to be in favor of high catalytic activity of the composite for hydration of nitriles to corresponding amides in aqueous solution. By using a catalyst dosage of 30 mg per mmol of substrate, in the temperature range of 80-100 °C, and reaction times of 4-9 h, excellent yields (73-96%) of the desired amides (13 examples) were obtained. Also, the catalyst was easy to recycle, and showed a slight decrease in efficiency after six consecutive runs.

Efficient one-pot synthesis and dehydrogenation of tricyclic dihydropyrimidines catalyzed by OMS-2-SO3H, and application of the functional-chromophore products as colorimetric chemosensors.

An efficient and convenient one-pot multicomponent reaction (MCR) for the synthesis and dehydrogenation of tricyclic dihydropyrimidine derivatives, catalyzed by -SO3H functionalized octahedral manganese oxide molecular sieves (OMS-2-SO3H) as a novel solid acid catalyst, is reported. All of the organic products and the catalyst were unambiguously characterized with conventional techniques including Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), X-ray diffraction analysis (XRD), 1H NMR, and 13C NMR spectroscopy. The targeted dehydrogenated chromophore compounds were successfully used as colorimetric chemosensors for detection of transition metals in aqueous solution. For example, 1-[4-(4-hydroxy-3-methoxy-phenyl)-2-methyl-benzo[4,5]imidazo[1,2-a]pyrimidin-3-yl]-ethanone (7d), exhibited high sensitivity and selectivity toward detection of Cr3+ over a panel of other transition metal cations. The interference of foreign ions was found to be negligible. It was found that a 1 : 1 complex of Cr3+ and 7d is responsible for the color change of the solution from ochre to brown. These newly devised chemosensors can also exhibit significant wavelength shifts (up to 100 nm) when used as pH indicators. 7d for example, showed a vivid and sharp color change from pink to yellow in the pH range of 4 to 6.

Chemoselective reduction of nitro and nitrile compounds using an Fe3O4-MWCNTs@PEI-Ag nanocomposite as a reusable catalyst.

Multi-walled carbon nanotubes (MWNTs) were modified with carboxylic acid functional groups (MWCNTs-(COOH) n ) prior to decoration with Fe3O4 nanoparticles. A further modification step by polyethyleneimine (PEI) resulted in Fe3O4-MWCNTs@PEI which provided a suitable platform for coordination and in situ reduction of silver ions to obtain an Fe3O4-MWCNTs@PEI-Ag nanocomposite with highly dispersed Ag nanoparticles. The Fe3O4-MWCNTs@PEI-Ag hybrid material was characterized by various techniques such as Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), transmission electron microscopy (TEM), vibrating sample magnetometry (VSM), X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA), and was used as an efficient catalyst for chemoselective reduction of nitroaromatic and nitrile compounds to their corresponding amines in aqueous solution at ambient temperature. Nitrofurazone, a cytotoxic antibiotic, as a non-aromatic example was also reduced selectively at the nitro group without reduction of the other functionalities in the presence of Fe3O4-MWCNTs@PEI-Ag. The catalyst was magnetically recoverable and maintained its activity for at least six cycles without considerable loss of efficiency.

Stöber synthesis of salen-formaldehyde resin polymer- and carbon spheres with high nitrogen content and application of the corresponding Mn-containing carbon spheres as efficient electrocatalysts for the oxygen reduction reaction.

Salen-formaldehyde (SF) resin polymer spheres were synthesized by the Stöber method from 4,4'-dihydroxysalen (N,N'-bis-(4-hydroxysalicylidene)-ethylenediamine; a tetradentate N2O2 Schiff base ligand) and formaldehyde. The salen precursor was prepared by condensation of ethylenediamine with 2,4-dihydroxybenzaldehyde in methanol. The SF resin colloidal spheres were also prepared by using Pluronic F127 and ammonia as a porogenic agent and catalyst, respectively (SF-P). In addition, corresponding Mn(ii)-coordinated polymer spheres of the SF-P were synthesized (SF-P-Mn(ii)). Corresponding monodispersed carbon spheres of all of the abovementioned samples were also obtained by pyrolysis technique. All of the products were characterized with conventional microscopic and spectroscopic techniques, as well as other physical methods such as BET analysis. It was found that carbonization of the SF resin spheres results in carbon spheres with specific surface areas in the range of 499-528 m2 g-1 and average pore sizes in the range of 2.58-3.08 nm. Nitrogen content of the SF-MWHT (obtained hydrothermally in a methanol/water mixture), and SF-P-C@Mn (obtained from carbonization of SF-P-Mn(ii)) samples were as high as 27.5 wt% and 35.02 wt%, respectively. Finally, a glassy carbon electrode (GCE) modified with SF-P-C@Mn (SF-P-C@Mn/GCE) was prepared and its electrocatalytic activity was evaluated for oxygen reduction reaction (ORR) by linear sweep voltammetry (LSV). The LSV results showed that the SF-P-C@Mn/GCE has a higher current density and a lower negative potential in the ORR compared to GCE.

Modeling of azo dyes adsorption on magnetic NiFe2O4/RGO nanocomposite using response surface methodology.

BACKGROUND: Azo group dyes are the largest group of synthetics dyes that widely used in industries, especially in textile industry. The presence of these organic compounds in wastewaters and their discharge into environment without efficient treatment may cause adverse effect on human, living and aquatic environment. The purpose of this study was to optimize the adsorption of azo dye of Direct Red 81 (anionic dye) and Basic Blue 41 (cationic dye) from aqueous solution onto magnetic NiFe2O4/RGO nanocomposite. METHODS: In this study the response surface methodology (RSM) based on the central composite design (CCD), was used to optimization and modeling of adsorption process DR81 and BB41 dye on NiFe2O4/RGO. in order to investigating the effect of the operating parameters on the adsorption efficiency DR81 and BB41, four influential factors were chosen that includes of pH (3-9), contact time (5-25 min), adsorbent amount (0.02-0.05 g) and initial dye concentration (40-200 mg/L). A total of 30 experiments were performed for each dye in this study. The concentration of dye in solution was measured by spectrophotometer. The structure of synthesized adsorbent was investigated using Scanning Electron Microscope (SEM), X-ray diffraction (XRD), Fourier transform irradiation (FTIR), transmission electron microscope (TEM) and vibrating sample magnetometer (VSM). RESULTS: Analysis of variance (ANOVA) showed that regression model for both dye adsorption with value of P value <0.001 is significant statistically. The correlation coefficient (R2) for DR81 (R2 = 0.9968) and BB41 (R2 = 0.9948) indicated which there is a good agreement between predicted values and the results of the experiments and the model also well predict the adsorption efficiency. Furthermore, the factors of pH, dye concentration and adsorbent dose, have the greatest effect on adsorption, respectively, while contact time have the lowest effect on adsorption of both dyes. The adsorption behavior of the DR81 and BB41 onto NiFe2O4/RGO was best described by the Langmuir and Freundlich isotherm, respectively. The optimum conditions for maximum removal of DR81 (96.41%) was found to be at pH 3, contact time 19.68 min, adsorbent dose 0.02 g and initial dye concentration 40 mg/L. However, the optimum conditions for maximum removal of BB41 (97.87%) was found to be at pH 9 contact time 18.16 min, adsorbent dose 0.02 g and initial dye concentration 40 mg/L. CONCLUSION: The present study shows that magnetic NiFe2O4/RGO nanocomposite have much potential as a powerful adsorbent for the rapid adsorption of anionic (DR81) and cationic dyes (BB41) from aqueous solution.

The potential for machine learning in hybrid QM/MM calculations.

Hybrid quantum-mechanics/molecular-mechanics (QM/MM) simulations are popular tools for the simulation of extended atomistic systems, in which the atoms in a core region of interest are treated with a QM calculator and the surrounding atoms are treated with an empirical potential. Recently, a number of atomistic machine-learning (ML) tools have emerged that provide functional forms capable of reproducing the output of more expensive electronic-structure calculations; such ML tools are intriguing candidates for the MM calculator in QM/MM schemes. Here, we suggest that these ML potentials provide several natural advantages when employed in such a scheme. In particular, they may allow for newer, simpler QM/MM frameworks while also avoiding the need for extensive training sets to produce the ML potential. The drawbacks of employing ML potentials in QM/MM schemes are also outlined, which are primarily based on the added complexity to the algorithm of training and re-training ML models. Finally, two simple illustrative examples are provided which show the power of adding a retraining step to such "QM/ML" algorithms.

Immobilization of laccase on modified Fe3O4@SiO2@Kit-6 magnetite nanoparticles for enhanced delignification of olive pomace bio-waste.

Lignocellulose is considered a major source for the production of valuable chemicals. Efficient degradation of lignin as the natural carrier of the lignocellulosic biomass represents a key limiting factor in biomass digestibility. Recently, biological delignification methods have been promoted as sustainable and environmentally friendly alternatives to the traditional technologies. In this study, porous nanocomposite of Fe3O4@SiO2@KIT-6 with magnetic properties was synthesized. The immobilized laccase supported on nanocomposite with enhanced stability in a hydrophobic ionic liquid has been developed for both olive pomace delignification and degradation of phenolic extracts from the pomace. After 6h incubation, the degradation rate of lignin and phenol by the immobilized laccase was estimated to be 77.3% and 76.5%, respectively. The immobilized laccase retained 70% of its initial degradation ability after 11 successive batch treatments of olive pomace. Furthermore, the immobilized laccase retained >70% of its initial activity after 21days of storage at room temperature. The obtained results indicated that the immobilized laccase on magnetic mesoporous support together with (1-Butyl-3-methylimidazolium hexafluorophosphate) ([Bmim][PF6]) could potentially provide a promising procedure for an improved enzymatic degradation of lignin and phenol in the related industries.

Addressing uncertainty in atomistic machine learning.

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate of the uncertainty when the width is comparable to that in the training data. Intriguingly, we also show that the uncertainty can be localized to specific atoms in the simulation, which may offer hints for the generation of training data to strategically improve the machine-learned representation.

The Influence of Elastic Strain on Catalytic Activity in the Hydrogen Evolution Reaction.

Understanding the role of elastic strain in modifying catalytic reaction rates is crucial for catalyst design, but experimentally, this effect is often coupled with a ligand effect. To isolate the strain effect, we have investigated the influence of externally applied elastic strain on the catalytic activity of metal films in the hydrogen evolution reaction (HER). We show that elastic strain tunes the catalytic activity in a controlled and predictable way. Both theory and experiment show strain controls reactivity in a controlled manner consistent with the qualitative predictions of the HER volcano plot and the d-band theory: Ni and Pt's activities were accelerated by compression, while Cu's activity was accelerated by tension. By isolating the elastic strain effect from the ligand effect, this study provides a greater insight into the role of elastic strain in controlling electrocatalytic activity.

Ultrasound assisted, ruthenium-exchanged FAU-Y zeolite catalyzed alkylation of indoles with epoxides under solvent free conditions.

Ruthenium-exchanged FAU-Y zeolite (RuY) was used as a recyclable catalyst for regioselective ring-opening of epoxides with indoles under irradiation of sonic waves. It was found that a solvent free process, under the above mentioned conditions provides good yields of the desired 3-alkylated indole derivatives.