CONTEXT: The intricate challenges posed by SO2 gas underscore the imperative for meticulous monitoring and detection due to its adverse effects on health, the environment, and equipment integrity. Hence, this research endeavors to delve deeply into the intricate realm of transition-metals functionalized sulfur-doped porphyrins (S@PPR) surfaces through a comprehensive computational study. The electronic properties revealed that upon adsorption, Ir_S@PPR surface reflects the least energy gap of 0.109 eV at the O-site of adsorptions, indicating an increase in electrical conductivity which is a better adsorption trait. Owing to the negative adsorption energy observed, the adsorption behavior is described as chemisorption, with the greatest adsorption energy of - 10.306 eV for Ir_S@PPR surface at the S-site of adsorption. Based on the mechanistic attributes, iridium-functionalized S@PPR surface is a promising detecting material towards the sensing of SO2 gas. This report will provide useful insight for experimental researchers in selecting and engineering materials to be used as detectors for SO2 gas pollutant. METHOD: All theoretical investigations were carried out using density functional theory (DFT), calculated at PW6B95-D3/GenECP/Def2svp/LanL2DZ computational method.
CONTEXT: This study employs a data-guided approach to evaluate zeolites for hydrogen storage, utilizing molecular simulations. The development of efficient and practical hydrogen storage materials is crucial for advancing clean energy technologies. Zeolites have shown promise as potential candidates due to their unique porous structure and tunable properties. However, the selection and design of suitable zeolites for hydrogen storage remain challenging. Therefore, this work aims to address this materials science question by utilizing molecular simulations and data-guided approaches to evaluate zeolites' performance for hydrogen storage. The results obtained from this study provide valuable insights into the evaluation of zeolites for hydrogen storage. Through molecular simulations, we analyze the adsorption behavior of hydrogen molecules in various zeolite structures. The performance of different zeolite frameworks in terms of hydrogen storage capacity, adsorption energy, and diffusion properties is assessed. Linde type A zeolite (LTA) had the highest capacity with a hydrogen capacity of 4.8wt% out of the 233 investigated zeolites. Furthermore, we investigate the influence of different factors such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) on the hydrogen storage performance of zeolites. The results show that Di, D, and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. This study, therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. The current study contributes to the understanding of zeolite-based materials for hydrogen storage applications, aiding in the development of more efficient and practical hydrogen storage systems. METHODS: Computational techniques were employed to investigate the hydrogen storage properties of zeolites. Molecular simulations were performed using classical force fields and molecular dynamics methods. The calculations were carried out at a force field level of theory with the GGA functional. To accurately capture the thermodynamics and kinetics of hydrogen adsorption, enhanced sampling techniques such as Monte Carlo simulations and molecular dynamics with metadynamics were utilized. We employed Grand Canonical Monte Carlo (GCMC) simulations to model hydrogen adsorption in zeolite structures for hydrogen storage. Our approach involved performing a substantial number of Monte Carlo steps (10,000) to ensure system equilibration and precise results. We defined a cutoff distance for particle interactions as 12.5 Ǻ and considered 0.000e framework charge per cell and 0.000e sorbate charge in energy calculations. The choice of an appropriate simulation cell size (50 × 50 × 50) Ǻ was crucial, mirroring real-world conditions. We specified lower and upper fugacity values (1 to 10 atm) to capture the range of gas pressures in the simulations. These methodical steps collectively enabled us to accurately model hydrogen adsorption within zeolites, forming the core of our hydrogen storage evaluation. In this research, we utilized DFT calculations to thoroughly investigate the interactions between zeolites and hydrogen. We employed pseudopotentials to describe electron behavior in zeolite systems, choosing them in line with DFT norms and basis set compatibility. Our simulation cell design replicated zeolite periodicity and eliminated boundary effects. Pre-geometry optimization was performed with HyperChem29, ensuring stable conformations with strict convergence criteria. We utilized 6-31 + G(d) and LanL2DZ basis sets for light and heavy atoms, aligning with field standards for computational efficiency and precision. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. Machine learning analysis was performed with the Scikit-learn library, an open-source Python tool. We employed a range of machine learning models, including SVMs, random forests, and neural networks, primarily for data analysis and feature extraction. Pearson correlation analysis, a classical statistical technique, was used to evaluate linear relationships between variables and assess the strength and direction of these relationships. It served as a complementary tool to understand the interplay of variables in our dataset, distinguishing it from machine learning algorithms. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. The simulations and data analysis were performed using BIOVIA material studio software, Gaussian, and Origin Pro software.
This comprehensive study was dedicated to augmenting the sensing capabilities of Ni@GP_PEDOT@H2S through the strategic functionalization with nitrogen, phosphorus, and sulfur heteroatoms. Governed by density functional theory (DFT) computations at the gd3bj-B3LYP/def2svp level of theory, the investigation meticulously assessed the performance efficacy of electronically tailored nanocomposites in detecting H2S gas-a corrosive byproduct generated by sulfate reducing bacteria (SRB), bearing latent threats to infrastructure integrity especially in the oil and gas industry. Impressively, the analysed systems, comprising Ni@GP_PEDOT@H2S, N_Ni@GP_PEDOT@H2S, P_Ni@GP_PEDOT@H2S, and S_Ni@GP_PEDOT@H2S, unveiled both structural and electronic properties of noteworthy distinction, thereby substantiating their heightened reactivity. Results of adsorption studies revealed distinct adsorption energies (- 13.0887, - 10.1771, - 16.8166, and - 14.0955 eV) associated respectively with N_Ni@GP_PEDOT@H2S, P_Ni@GP_PEDOT@H2S, S_Ni@GP_PEDOT@H2S, and Ni@GP_PEDOT systems. These disparities vividly underscored the diverse strengths of the adsorbed H2S on the surfaces, significantly accentuating the robustness of S_Ni@GP_PEDOT@H2S as a premier adsorbent, fuelled by the notably strong sulfur-surface interactions. Fascinatingly, the sensor descriptor findings unveiled multifaceted facets pivotal for H2S detection. Ultimately, molecular dynamic simulations corroborated the cumulative findings, collectively underscoring the pivotal significance of this study in propelling the domain of H2S gas detection and sensor device innovation.
CONTEXT: The increasing demand for fuels and chemicals in the world has prompted the exploration of various forms of renewable energy resources. Using C5-based furfural as the platform to replace the fossil energy resources is greatly attractive because of its abundance and environmental friendliness. Here we study the activity, selectivity, and possible reaction pathways for the Baeyer-Villiger oxidation of furfural over small Au clusters using hydrogen peroxide as oxidant. Furfural reacts with hydrogen peroxide in the presence of the catalysts with 93% selectivity towards maleic anhydride. Natural population analysis, frontier molecular orbital analysis, and spectroscopic analysis are used to illustrate the interaction mechanism between C5H4O2, H2O2, and Au. Reaction pathways leading to the formation of maleic anhydride are also explored. The reaction of C5H4O2 with H2O2 in the absence of a catalyst bears a relatively high transition state energy barrier of 2.98 eV for the first step involving absorption of H atom of H2O2 on the -OH group of C5H4O2. This is in agreement with the blank experiment where there were rare oxidation products observed in the absence of the metal cluster catalysts. On the other hand, transition state energies in the presence of the Au metal clusters are lower and the most feasible pathway is where the substrate and H2O2 co-bind on the Au catalyst and H2O2 molecule transfers an oxygen to the substrate, leading to the cleavage of the O-O bond. METHODS: DFT calculations were done with B3PW91 functional. 6-311G(df, p) basis set was used for C, O, and H and aug-cc-pVDZ-PP was used for gold atoms. Gaussian 09 software was used for the calculations. Multiwfn 3.7 dev was used for the quantum theory of atoms-in-molecules (QTAIM) investigations.
In view of the numerous environmental hazards and health challenges linked to sulfur (iv) oxide (SO2), an indirect greenhouse gas, and the resultant need to develop efficient gas nanosensor devices, this research had as its principal focus on the theoretical evaluation of the gas sensing potential of metals: Ag, Au and Cu functionalized silicon-doped quantum dots (Si@QD) for the detection and adsorption of SO2 gas investigated using the first-principles density functional theory (DFT) computation at the B3LYP-D3(BJ)/def2-SVP level of theory. Eight (8) possible adsorption modes: SO2_O_Si@QD, SO2_O_Ag_Si@QD, SO2_O_Au_Si@QD, SO2_O_Cu_Si@QD, SO2_S_Si@QD, SO2_S_Ag_Si@QD, SO2_S_Au_Si@QD, and SO2_S_Cu_Si@QD were considered based on SO2 interactions with the studied materials at the -S and -O sites of the SO2 molecule. The counterpoise correction (BSSE) showed that five of the eight interactions had favorable Ead + BSSE values ranging from -0.31 to -1.98 eV. All the eight interactions were observed to be thermodynamically favorable with ΔG and ΔH ranging from -129.01 to -200.24 kcal/mol and -158.26 to -229.73 kcal/mol respectively. Results from the topology analysis reveal that van der Waals forces occurred the greatest at the gas-sensor interphase while SO2_S_ Cu_Si@QD is predicted to have the highest sensing potency based on the conductivity and recovery time estimations. These results confirm the potential efficient feasibility of real-world device application of the metals (Ag, Au, Cu) functionalized Si-doped QDs.
Graphite , Nanostructures , Quantum Dots , Silicon , Metals
All dyes conduct but at different degrees of absorption; it is interesting to study the degree of conductivity and absorptivity of novel reactive azo-dyes in respect to dye-sensitized solar cells (DSSCs) to ascertain their viability for such applications. In this study, four novel reactive azo-dyes were experimentally synthesized from p-aminobenzaldehyde, 4-amino-3-nitrobenzaldehyde, and aniline through series of condensation and coupling reactions. The various functional groups, molecular connectivities, and molecular weight of the various fragments of the synthesized dyes were elucidated using the GC-MS, FT-IR, UV-vis, and NMR respectively. The experimentally determined structures were modeled and investigated using density functional theory (DFT) and time-dependent density functional theory (TD-DFT) approaches to computationally compute the electronic structure properties, reactivity, absorption and solvatochromism in four different phases: gas, ethanol, acetone, and water, and the photovoltaic properties for possible applications in dye-sensitized solar cells (DSSCs). By comparing the HOMO (E H) and the LUMO (E L) energies from the results obtained demonstrates that dye D has the highest E L energy value of -2.48 eV with a relatively lowest E H energy value of -5.63 eV such that it lies underneath the conduction band edge of TiO2 which is necessary to enable charge regeneration. Pi-electron delocalization was observed from the natural bond orbital (NBO) calculations between the different aromatic rings with dye B and A having the relatively highest and least second-order stabilization energies between σ* â σ* and LP* â LP interacting orbitals respectively. It is also observed in all the solvents that the Gibbs free energy of injection (ΔG inject) is greater than 0.2 eV and hence, all the studied azo structures in the four phases provided efficient electron injection and light harvesting efficiency (LHE), however, the value of ΔG inject for dyes B and D is greatest in all the four phases and thus, provided the highest electron injection of all the dyes. From the fact-findings of quantum theory of atoms-in-molecules (QTAIM), dyes A and C have extra-stability due to their relatively high numbers of intramolecular H-bond interactions along with some additional intra-atomic bonding between atoms within the studied compounds. Hence, all the four dyes are good for DSSCs applications.
Correction for 'A hexagonal Ni6 cluster protected by 2-phenylethanethiol for catalytic conversion of toluene to benzaldehyde' by Anthony M. S. Pembere et al., Phys. Chem. Chem. Phys., 2019, 21, 17933-17938.
We have synthesized single crystals of a 2-phenylethanethiol-protected Ni6 nanocluster, namely Ni6(C8H9S)12, which shows a hexagonal metallic core structure and reasonable stability. Interestingly this cluster is found to be an excellent candidate for the catalytic oxidation of toluene to benzaldehyde, with 100% conversion and 91% selectivity, showing application potential as an aromatic aldehyde in industry. Using DFT calculations, we rationalize the catalytic reaction mechanism for the conversion of toluene to benzaldehyde, and demonstrate that the presence of H2O2 initiates the Ni6 cluster via a highly exothermic step to form a Ni6O* intermediate which then results in active sites for the oxidation of toluene. What is interesting is that the likely steric geometry matching of the six-ring Ni6 core, a dozen phenylethanethiol ligands and the aromatic reactant toluene could attain a synergistic effect to facilitate the production of benzaldehyde free of catalyst support.
We present here a further insight on the hydrogen evolution reactions (HER) of aluminum clusters with one and multiple water molecules. Along with natural bond orbital (NBO) and frontier molecular orbital (FMO) analysis, we compared the reactivities of both anionic and neutral Al13, Al12, Al7, and Al6 clusters with water in gas phase. It is found that electron flow interactions between these typical Al clusters and H2O initiate their reactions, allowing varied charge distribution on the cluster. With an emphasis on the typical Al6 cluster, we checked out the reactive intermediates, activated complexes, transition states, bond breaking and stereochemistry for it to react with two and four water molecules, respectively. The kinetic- and thermodynamic- allowed reaction pathways are coincident with the experimental observation of Al n(OH)4- being dominant products for Al n- clusters reacting with water. It is illustrated how additional water molecules function as catalysts enabling strengthened HER activity.
