Defect-induced efficient dry reforming of methane over two-dimensional Ni/h-boron nitride nanosheet catalysts

Efficient enhancement of catalytic stability and coke-resistance is a crucial aspect for dry reforming of methane. Here, we report Ni nanoparticles embedded on vacancy defects of hexagonal boron nitride nanosheets (Ni/h-BNNS) can optimize catalytic performance by taming two-dimensional (2D) interfacial electronic effects. Experimental results and density functional theory calculations indicate that surface engineering on defects of Ni/h-BNNS catalyst can strongly influence metal-support interaction via electron donor/acceptor mechanisms and favor the adsorption and catalytic activation of CH4 and CO2. The Ni/h-BNNS catalyst exhibits superior catalytic performance during a 120 h durability test. Furthermore, in situ techniques further reveal possible recovery mechanism of the active Ni sites, identifying the enhanced catalytic activities of the Ni/h-BNNS catalyst. This work highlights promotional mechanism of defect-modified interface and should be equally applicable for design of thermochemically stable catalysts

The Activation of Methane on Ru, Rh, and Pd Decorated Carbon Nanotube and Boron Nitride Nanotube: A DFT Study

Methane decomposition catalyzed by an Ru, Rh, or Pd atom supported on a carbon or boron nitride nanotubes was analyzed by means of the density functional theory with the M06-L hybrid functional. The results suggested that the dissociative reaction of methane was a single-step mechanism. Based on the calculated activation energy, the Ru-decorated carbon nanotube showed superior catalytic activity with an activation barrier of 14.5 kcal mol−1, followed by the Rh-decorated carbon nanotube (18.1 kcal mol−1) and the Pd-decorated carbon nanotube (25.6 kcal mol−1). The catalytic performances of metals supported on a boron nitride nanotube were better than those on a carbon nanotube. The total activation barrier for the Ru, Rh, and Pd atoms on boron nitride nanotube was 10.2, 14.0, and 20.5 kcal mol−1, respectively. Dissociative adsorption complexes on the Ru–boron nitride nanotube were the most stable. The anionic state of the supported metal atom was responsible for decreasing the activation energy of methane decomposition. Our finding provides a crucial point for further investigation

Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts

A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O2•−), hydroxyl radical (•OH), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH•) served as the training data sets of a quantitative structure–activity relationship (QSAR) study. The steric and electronic descriptors obtained from quantum chemical calculations were related to the three O2•−, •OH, and DPPH• scavenging activities using the genetic algorithm combined with multiple linear regression (GA-MLR) and artificial neural networks (ANNs). The GA-MLR models resulted in good statistical values; the coefficient of determination (R2) of the training set was greater than 0.8, and the root mean square error (RMSE) of the test set was in the range of 0.3 to 0.6. The main molecular descriptors that play an important role in the three types of antioxidant activities are the bond length, HOMO energy, polarizability, and AlogP. In the QSAR-ANN models, a good R2 value above 0.9 was obtained, and the RMSE of the test set falls in a similar range to that of the GA-MLR models. Therefore, both the QSAR GA-MLR and QSAR-ANN models were used to predict the newly designed pyrrole derivatives, which were developed based on their starting reagents in the synthetic process

Prediction of the Glass Transition Temperature in Polyethylene Terephthalate/Polyethylene Vanillate (PET/PEV) Blends: A Molecular Dynamics Study

Polyethylene terephthalate (PET) is one of the most common polymers used in industries. However, its accumulation in the environment is a health risk to humans and animals. Polyethylene vanillate (PEV) is a bio-based material with topological, mechanical, and thermal properties similar to PET, allowing it to be used as a PET replacement or blending material. This study aimed to investigate some structural and dynamical properties as well as the estimated glass transition temperature (Tg) of PET/PEV blended polymers by molecular dynamics (MD) simulations with an all-atom force field model. Four blended systems of PET/PEV with different composition ratios (4/1, 3/2, 2/3, and 1/4) were investigated and compared to the parent polymers, PET and PEV. The results show that the polymers with all blended ratios have Tg values around 344–347 K, which are not significantly different from each other and are close to the Tg of PET at 345 K. Among all the ratios, the 3/2 blended polymer showed the highest number of contacting atoms and possible hydrogen bonds between the two chain types. Moreover, the radial distribution results suggested the proper interactions in this system, which indicates that this is the most suitable ratio model for further experimental studies of the PET/PEV polymer blend

Straightforward Design for Phenoxy-Imine Catalytic Activity in Ethylene Polymerization: Theoretical Prediction

The quantitative structure-activity relationship (QSAR) of 18 Ti-phenoxy-imine (FI-Ti)-based catalysts was investigated to clarify the role of the structural properties of the catalysts in polyethylene polymerization activity. The electronic properties of the FI-Ti catalysts were analyzed based on density functional theory with the M06L/6-31G** and LANL2DZ basis functions. The analysis results of the QSAR equation with a genetic algorithm showed that the polyethylene catalytic activity mainly depended on the highest occupied molecular orbital energy level and the total charge of the substituent group on phenylimine ring. The QSAR models showed good predictive ability (R2) and R2 cross validation (R2cv) values of greater than 0.927. The design concept is “head-hat”, where the hats are the phenoxy-imine substituents, and the heads are the transition metals. Thus, for the newly designed series, the phenoxy-imine substituents still remained, while the Ti metal was replaced by Zr or Ni transition metals, entitled FI-Zr and FI-Ni, respectively. Consequently, their polyethylene polymerization activities were predicted based on the obtained QSAR of the FI-Ti models, and it is noteworthy that the FI-Ni metallocene catalysts tend to increase the polyethylene catalytic activity more than that of FI-Zr complexes. Therefore, the new designs of the FI-Ni series are proposed as candidate catalysts for polyethylene polymerization, with their predicted activities in the range of 35,000–48,000 kg(PE)/mol(Cat.)·MPa·h. This combined density functional theory and QSAR analysis is useful and straightforward for molecular design or catalyst screening, especially in industrial research

Quantitative Structure–Electrochemistry Relationship (QSER) Studies on Metal–Amino–Porphyrins for the Rational Design of CO₂ Reduction Catalysts

The quantitative structure–electrochemistry relationship (QSER) method was applied to a series of transition-metal-coordinated porphyrins to relate their structural properties to their electrochemical CO2 reduction activity. Since the reactions mainly occur within the core of the metalloporphyrin catalysts, the cluster model was used to calculate their structural and electronic properties using density functional theory with the M06L exchange–correlation functional. Three dependent variables were employed in this work: the Gibbs free energies of H*, C*OOH, and O*CHO. QSER, with the genetic algorithm combined with multiple linear regression (GA–MLR), was used to manipulate the mathematical models of all three Gibbs free energies. The obtained statistical values resulted in a good predictive ability (R2 value) greater than 0.945. Based on our QSER models, both the electronic properties (charges of the metal and porphyrin) and the structural properties (bond lengths between the metal center and the nitrogen atoms of the porphyrin) play a significant role in the three Gibbs free energies. This finding was further applied to estimate the CO2 reduction activities of the metal–monoamino–porphyrins, which will prove beneficial in further experimental developments

Excited-State Geometries of Heteroaromatic Compounds: A Comparative TD-DFT and SAC-CI Study

The structures of low-lying singlet excited states of nine it conjugated heteroaromatic compounds have been investigated by the symmetry adapted cluster configuration interaction (SAC CI) method and the time dependent density functional theory (TDDFT) using the PBE0 functional (TD-PBE0). In particular, the geometry relaxation in some pi pi* and n pi* excited states of furan, pyrrole, pyridine, p-benzoquinone, uracil, adenine, 9,10-anthraquinone, coumarin, and 1,8-naphthalimide as well as the corresponding vertical transitions, including Rydberg excited states, have been analyzed in detail. The basis set and functional dependence of the results was also examined The SAC CI and TD-PBEO calculations showed reasonable agreement in both transition energies and excited state equilibrium structures for these heteroaromatic compounds

Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction

Quantitative structure–activity relationship (QSAR) analysis, an in silico methodology, offers enhanced efficiency and cost effectiveness in investigating anti-inflammatory activity. In this study, a comprehensive comparative analysis employing four machine learning algorithms (random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and artificial neural networks (ANNs)) was conducted to elucidate the activities of naturally derived compounds from durian extraction. The analysis was grounded in the exploration of structural attributes encompassing steric and electrostatic properties. Notably, the nonlinear SVR model, utilizing five key features, exhibited superior performance compared to the other models. It demonstrated exceptional predictive accuracy for both the training and external test datasets, yielding R2 values of 0.907 and 0.812, respectively; in addition, their RMSE resulted in 0.123 and 0.097, respectively. The study outcomes underscore the significance of specific structural factors (denoted as shadow ratio, dipole z, methyl, ellipsoidal volume, and methoxy) in determining anti-inflammatory efficacy. Thus, the findings highlight the potential of molecular simulations and machine learning as alternative avenues for the rational design of novel anti-inflammatory agents