Szeged index of hollow hexagons

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Szeged index of hollow hexagons

We compute the values of Szeged index of hollow hexagons using the cut method. Hollow hexagons are primitive coronoid systems with exactly six angularly annelated hexagons. The cut method is used to compute the value of the index in terms of the Szeged index of weighted quotient graphs with respect to a c-partition of the edge set.


Abstract

The Szeged index of a connected graph G$$ G $$ (Sz(G)$$ Sz(G) $$) is a well known distance based topological index. A primitive coronoid system is a coronoid system formed by a single chain in a macro-cyclic arrangement consisting of linearly and angularly annelated hexagons. The angular hexagons are called corners. A hollow hexagon is a primitive coronoid system with exactly six corners. In this paper we calculate the values of Szeged index of hollow hexagons using the cut method.

Early Warning for the Electrolyzer: Monitoring CO2 Reduction via In‐Line Electrochemical Impedance Spectroscopy

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Early Warning for the Electrolyzer: Monitoring CO2 Reduction via In-Line Electrochemical Impedance Spectroscopy

Advanced failure warning: Combining in-line real-time multi-sine electrochemical impedance spectroscopy with selectivity and voltage data builds a framework for stable electrolysis enabling early failure detection and prevention.


Abstract

The electrochemical CO2 reduction reaction (CO2RR) to fuels and feedstocks presents an opportunity to decarbonize the chemical industry, and current electrolyzer performance levels approach commercial viability. However, stability remains below that required, in part because of the challenge of probing these electrolyzer systems in real time and the challenge of determining the root cause of failure. Failure can result from initial conditions (e. g., the over- or under-compression of the electrolyzer), gradual degradation of components (e. g., cathode or anode catalysts), the accumulation of products or by-products, or immediate changes such as the development of a hole in the membrane or a short circuit. Identifying and mitigating these assembly-related, gradual, and immediate failure modes would increase both electrolyzer lifetime and economic viability of CO2RR. We demonstrate the continuous monitoring of CO2RR electrolyzers during operation via non-disruptive, real-time electrochemical impedance spectroscopy (EIS) analysis. Using this technique, we characterise common failure modes - compression, salt formation, and membrane short circuits - and identify electrochemical parameter signatures for each. We further propose a framework to identify, predict, and prevent failures in CO2RR electrolyzers. This framework allowed for the prediction of anode degradation ~11 hours before other indicators such as selectivity or voltage.

Defect‐induced Ordered Mesoporous Titania Molecular Sieves: A Unique and Highly Efficient Hetero‐phase Photocatalyst for Solar Hydrogen Generation

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Defect-induced Ordered Mesoporous Titania Molecular Sieves: A Unique and Highly Efficient Hetero-phase Photocatalyst for Solar Hydrogen Generation

Solar hydrogen evolution using N-doped titania photocatalyst has gained significant interest in sustainable energy technologies. Here, we demonstrate the successful and reproducible synthesis of defect-enriched nitrogen-doped ordered mesoporous titania with a well-organized crystalline framework structure and point defects like trapped electrons and Ti3+ centers. The resulting materials demonstrate the superior performance owing to a synchronizing effect of the presence of intrinsic defects, efficient charge migration, and enhanced absorbance of light properties.


Abstract

The conversion of solar energy into fuel has gained significant interest, particularly in photocatalytic water splitting, and the materials that efficiently generate hydrogen from water or aqueous solution using solar irradiation are highly desired for the hydrogen economy. Photocatalysts made of N-doped TiO2 are frequently utilized for breaking of water molecules in the process of generating hydrogen. To achieve this target, a unique defect-induced nitrogen-doped highly organized 2D-hexagonal periodic mesoporous titania, TiO2-xNy with a well-crystallized framework is synthesized in a reproducible way using structure-directing agents, e. g., F108, F127, P123, and CTAB. The nitrogen is incorporated into these samples through a facile method involving the calcination of templated materials in an air. A systematic characterization of the resulting ordered mesoporous titania employing a battery of experimental techniques indicates the presence of considerable amounts of intrinsic defects, viz., trapped electrons in oxygen vacancy and/or Ti3+ centres via nitrogen-doping in the titania matrix. These defects in turn promote the charge separation of photogenerated excitons, and therefore exhibit excellent photocatalytic activity for the hydrogen evolution reaction as compared to commercial titania such as Aeroxide®P-25. The superior activity of the N-doped mesoporous TiO2 is attributed to the synergistic effect of facile charge migration with high carrier density, unique phase composition (bronze and anatase), slow recombination of photo-induced excitons, and enhanced absorbance from ultra-violet to the visible region.

The deposition of isolated Fe(3+) species in mesoporous silicon for oxidation of cyclohexane

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The balance of activity and selectivity in liquid alkane oxidation is challenging to design supported Fe catalysts. When Fe species leach to the solvent, uncontrolled free radical chain reactions happen. Herein, we have constructed isolated Fe(3+) species by forming a strong Fe-O-Si bond for the selective oxidation of cyclohexane to cyclohexanone by H2O2. Compared to the supported FeOx clusters, the strong Fe-O-Si bond between isolated Fe(3+) species and SiO2 prevents the leaching of Fe in strong oxidation (H2O2) reaction conditions and dominates the non-free radical mechanism. The turnover frequency over the 10FeOx/SBA-15 reached 15.2 h-1, higher than the reported Fe-based catalysts. The high selectivity of cyclohexanone is maintained at different conversions. Moreover, the (SiO)xFe3+(OH)3-x-OOH active species were detected by Raman and FTIR and are generated from the oxidation of isolated Fe species. The strong Fe-O-Si bond and non-free radical mechanism by (SiO)xFe3+(OH)3-x-OOH active species induce high activity and selectivity for the oxidation of many other alkanes.

Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

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Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

Machine learning analysis is used for predicting the multiple parameters of organic acceptors using RDkit descriptors. The developed methodology can help to predict the efficient acceptors in a short time and less computational cost.


Abstract

Machine learning (ML) analysis has gained huge importance among researchers for predicting multiple parameters and designing efficient donor and acceptor materials without experimentation. Data are collected from literature and subsequently used for predicting impactful properties of organic solar cells such as power conversion efficiency (PCE) and energy levels (HOMO/LUMO). Importantly, out of various tested models, hist gradient boosting (HGB) and the light gradient boosting (LGBM) regression models revealed better predictive capabilities. To achieve the prediction effectively, the selected (best) ML regression models are further tuned. For the prediction of PCE (test set), the LGBM shows the coefficient of determination (R 2) value of 0.787, which is higher than HGB (R 2 = 0.680). For the prediction of HOMO (test set), the LGBM shows R 2 value of 0.566, which is higher than HGB (R 2 = 0.563). However, for the prediction of LUMO (test set), the LGBM shows R 2 value of 0.605, which is lower than HGB (R 2 = 0.606). Among the three predicted properties, prediction ability is higher for PCE. These models help to predict the efficient acceptors in a short time and less computational cost.

Efficiency Analysis of the Discrete Element Method Model in Gas‐Fluidized Beds

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Efficiency Analysis of the Discrete Element Method Model in Gas-Fluidized Beds

The efficiency and accuracy of the Euler-Lagrange/discrete element method model by changing the stiffness coefficient and fluid time step for different particle numbers and diameters were investigated. According to the results, the higher stiffness coefficients improve the simulation accuracy slightly, however, the average computing time increased exponentially.


Abstract

The efficiency and accuracy of the Euler-Lagrange/discrete element method model were investigated. Accordingly, the stiffness coefficient and fluid time step were changed for different particle numbers and diameters. To derive the optimum parameters for simulations, the obtained results were compared with the measurements. According to the results, the application of higher stiffness coefficients improves the simulation accuracy slightly, however, the average computing time increases exponentially. For time intervals larger than 5 ms, the results indicated that the average computation time is independent of the applied fluid time step, while the simulation accuracy decreases extremely by increasing the size of the fluid time step. Nevertheless, using time steps smaller than 5 ms leads to negligible improvements in the simulation accuracy, though to an exponential rise in the average computing time.