Computational design of BC3N2 based single atom catalyst for dramatic activation of inert CO2 and CH4 gases into CH3COOH with ultralow CH4 dissociation barrier

Mechanistic study of the competition between carbon dioxide reduction and hydrogen evolution reaction and selectivity tuning via loading single-atom catalysts on graphitic carbon nitride

In the context of catalytic CO2 reduction (CO2RR), the interference of the inherent hydrogen evolution reaction (HER) and the possible selectivity towards CO have posed a significant challenge to the generation of formic acid. To address this hurdle, in this work, we have investigated the impact of different single-atom metal catalysts on tuning selectivity by employing density functional theory (DFT) calculations to scrutinize the reaction pathways. Single-atom catalysts supported on carbon-based systems have proven to be pivotal in altering both the activity and selectivity of the CO2RR. In this study, a series of single-atom-metal-loaded g-C3N4 monolayers (MCN, M = Ni, Cu, Zn, Ga, Cd, In, Sn, Pb, Ag, Au, Bi, Pd and Pt) were systematically examined. Through detailed DFT calculations, we explored their influence on reaction selectivity between the *COOH and *OCHO intermediates. Notably, NiCN favors the reaction via the *OCHO route, with a significantly lower rate-determining potential of 0.36 eV, which is approximately 73.5% lower than that of the CN system (1.36 eV). Most importantly, the Ni single-atom catalyst with lower coordination significantly enhances CO2 adsorption, promoting CO2RR over HER. Overall, this study, guided by DFT calculations, provides a theoretical prediction of how the selection of single-atom metal catalysts can effectively modulate the reaction pathway, thereby offering a potential solution for achieving high product selectivity in CO2RR.

The chemical recognition of hydrogen fluoride via B24N24 nanocage: quantum chemical approach

CONTEXT

Ab initio calculations were employed in this investigation to scrutinize the adsorption characteristics of a linear chain (HF)n on a BN nanocage (B24N24), wherein the chain lengths varied (n = 1, 2, 3, and 4). The overarching aim was to assess the efficiency of this setup in detecting and adhering to (HF)n under both liquid and gaseous scenarios. This study encompassed an array of aspects, encompassing adsorption energy, optimal configuration determination, work function analysis, and charge exchange assessment. Furthermore, an exploration was conducted into the impact of HF linear chain dimensions on electrical attributes and adsorption energy. According to the values of adsorption energy, the dimer form of HF adsorbed onto BN nanocages displayed the highest stability.

METHODS

This scrutiny was undertaken utilizing density functional theory (DFT), employing the B3LYP functional and the 6-31 +  + G(d,p) basis set. Notably, the choice of the 6-31 +  + G(d,p) basis set is particularly apt for delving into nanostructure analyses. The HOMO-LUMO energy gap was significantly reduced by (HF)n upon adsorption onto the nanocage, falling from 6.48 to 5.43 eV and enhancing electrical conductivity as a result. Additionally, BN nanocages may be used as sensors to find (HF)n among other environmental pollutants.

MOFs as Versatile Catalysts: Synthesis Strategies and Applications in Value-Added Compound Production

Catalysts played a crucial role in advancing modern human civilization, from ancient times to the industrial revolution. Due to high cost and limited availability of traditional catalysts, there is a need to develop cost-effective, high-activity, and nonprecious metal-based electrocatalysts. Metal-organic frameworks (MOFs) have emerged as an ideal candidate for heterogeneous catalysis due to their physicochemical properties, hybrid inorganic/organic structures, uncoordinated metal sites, and accessible organic sections. MOFs are high nanoporous crystalline materials that can be used as catalysts to facilitate polymerization reactions. Their chemical and structural diversity make them effective for various reactions compared to traditional catalysts. MOFs have been applied in gas storage and separation, ion-exchange, drug delivery, luminescence, sensing, nanofilters, water purification, and catalysis. The review focuses on MOF-enabled heterogeneous catalysis for value-added compound production, including alcohol oxidation, olefin oligomerization, and polymerization reactions. MOFs offer tunable porosity, high spatial density, and single-crystal XRD control over catalyst properties. In this review, MOFs were focused on reactions of CO2 fixation, CO2 reduction, and photoelectrochemical water splitting. Overall, MOFs have great potential as versatile catalysts for diverse applications in the future.

Modeling the CO2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks

Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO2), which is an environmental pollutant with a greenhouse effect. The CO2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO2 permeability in PMP/ZnO, PMP/Al2O3, PMP/TiO2, and PMP/TiO2-NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO2-NT (functionalized nanotube with titanium dioxide) is the best medium for CO2 separation.

Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks

Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).

DFT study of adsorbing SO2, NO2, and NH3 gases based on pristine and carbon-doped Al24N24 nanocages

The adsorption of SO₂, NO₂, and NH₃ toxic gases on Al₂₄N₂₄ and Al₂₄N₂₃C nanocages was investigated by using density functional theory (DFT) calculations. The adsorption energies, frontier orbitals, charge transfer using natural bonding orbital (NBO) analysis, dipole moment, the partial density of states (PDOS), thermodynamic relationships, non-covalent interaction (NCI), and quantum theory of atoms in molecules (QTAIM) were considered. The results reveal that carbon-doped Al₂₄N₂₄ nanocage increases the adsorption energies for SO₂ and NO₂ gases while decreasing the adsorption energy of NH₃ gas. The ΔG for all configurations were negative except the configurations A1 and G2 confirming the weak adsorption of these two complexes. In conclusion, Al₂₄N₂₄ and Al₂₄N₂₃C nanocages are in general promising adsorbents for the removal of SO₂, NO₂, and NH₃ toxic gases. The Al₂₄N₂₄ and Al₂₄N₂₃C nanocages are ideal electronic materials.

The elimination and extraction of organosulfur compounds from real water and soil samples using metal organic framework/graphene oxide as a novel and efficient nanocomposite

In the present work, an efficient metal organic framework/graphene oxide (MOF-801/GO) sorbent was fabricated and employed for the detection of organosulfur pesticides (OSPs) in real samples using gas chromatography-flame photometric detection (GC-FPD). The optimal extraction parameters for the suggested solid-phase extraction (SPE) include sorbent amount (60 mg), extraction solvent (acetonitrile) and extraction time (5 min). The linear dynamic ranges and detection limits for organosulfur pesticides (OSPs) samples under above extraction conditions were ranged from 0.5 to 300 μg L^-1 and 0.1-1.1 μg L^-1, respectively. Moreover, the proposed SPE/GC-FDP method was applied for the analysis of pesticides in different real environmental water and soil samples. The obtained recoveries of the analytes in were between 92.0 and 106.8% and relative standard deviation (RSD) values were lower than 9.2%. The application of the MOF-801/GO as a sorbent in dSPE of OSPs analytes showed to be reliable, fast and sensible methodology for pesticides monitoring in different environmental samples.

Synthesis of a magnetic polystyrene-supported Cu(II)-containing heterocyclic complex as a magnetically separable and reusable catalyst for the preparation of N-sulfonyl-N-aryl tetrazoles

In this work, a cost-effective, environmentally friendly, and convenient method for synthesizing a novel heterogeneous catalyst via modification of polystyrene using tetrazole-copper magnetic complex [Ps@Tet-Cu(II)@Fe₃O₄] has been successfully developed. The synthesized complex was analyzed using TEM (transmission electron microscopy), HRTEM (high resolution-transmission electron microscopy), STEM (scanning transmission electron microscopy), FFT (Fast Fourier transform), XRD (X-ray diffraction), FT-IR (Fourier transform-infrared spectroscopy), TG/DTG (Thermogravimetry and differential thermogravimetry), ICP-OES (Inductively coupled plasma-optical emission spectrometry), Vibrating sample magnetometer (VSM), EDS (energy dispersive X-ray spectroscopy), and elemental mapping. N-Sulfonyl-N-aryl tetrazoles were synthesized in high yields from N-sulfonyl-N-aryl cyanamides and sodium azide using Ps@Tet-Cu(II)@Fe₃O₄ nanocatalyst. The Ps@Tet-Cu(II)@Fe₃O₄ complex can be recycled and reused easily multiple times using an external magnet without significant loss of catalytic activity.

Magnetic chitosan stabilized Cu(II)-tetrazole complex: an effective nanocatalyst for the synthesis of 3-imino-2-phenylisoindolin-1-one derivatives under ultrasound irradiation

In the present research, a recyclable catalyst has been prepared via a simple approach using chitosan as a linear polysaccharide. This paper reports the synthesis of novel copper(II) complex of 5-phenyl-1H-tetrazole immobilized on magnetic chitosan (MCS@PhTet@Cu(II)) as an effective catalyst. Transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), vibrating sample magnetometer (VSM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and inductively coupled plasma mass spectrometry (ICP-MS) techniques were applied for the characterization of the catalyst. The catalytic activity of MCS@PhTet@Cu(II) was evaluated in the ultrasound-assisted synthesis of 3-imino-2-phenylisoindolin-1-one derivatives via the reaction between benzoyl chloride and arylcyanamides in ethanol at ambient temperature. Utilizing a wide variety of arylcyanamides under mild conditions, no use of toxic organic solvents, moderate reaction time, high yields along with catalyst excellent reusability and easy separation of the products without any tedious separation techniques, made this method a novel and simple process. The resulting heterogeneous catalyst showed valuable advantages such as easier work-up, better stability, and greater separation ability using an external magnet. The catalyst showed high efficacy and recyclability even after five cycles with no significant loss of its efficacy. The present methodology provides a path for the preparation of structurally diverse heterocyclic compounds, which may exhibit important biological activity.