Highly Dispersed Mn-Doped Ceria Supported on N-Doped Carbon Nanotubes for Enhanced Oxygen Reduction Reaction.

The weak adsorption of oxygen on transition metal oxide catalysts limits the improvement of their electrocatalytic oxygen reduction reaction (ORR) performance. Herein, a dopamine-assisted method is developed to prepare Mn-doped ceria supported on nitrogen-doped carbon nanotubes (Mn-Ce-NCNTs). The morphology, dispersion of Mn-doped ceria, composition, and oxygen vacancies of the as-prepared catalysts were analyzed using various technologies. The results show that Mn-doped ceria was formed and highly dispersed on NCNTs, on which oxygen vacancies are abundant. The as-prepared Mn-Ce-NCNTs exhibit a high ORR performance, on which the average electron transfer number is 3.86 and the current density is 24.4% higher than that of commercial 20 wt % Pt/C. The peak power density of Mn-Ce-NCNTs is 68.1 mW cm-2 at the current density of 138.9 mA cm-2 for a Zn-air battery, which is close to that of 20 wt % Pt/C (69.4 mW cm-2 at 106.1 mA cm-2). Density functional theory (DFT) calculations show that the oxygen vacancy formation energies of Mn-doped CeO2(111) and pure CeO2(111) are -0.55 and 2.14 eV, respectively. Meanwhile, compared with undoped CeO2(111) (-0.02 eV), Mn-doped CeO2(111) easily adsorbs oxygen with the oxygen adsorption energy of only -0.68 eV. This work provides insights into the synergetic effect of Mn-doped ceria for facilitating oxygen adsorption and enhancing ORR performance.

Efficient photoelectrocatalytic degradation of pollutants over hydrophobic carbon felt loaded with Fe-doped porous carbon nitride via direct activation of molecular oxygen.

Developing a photoelectric cathode capable of efficiently activating molecular oxygen to degrade pollutants is a coveted yet challenging goal. In pursuit of this, we synthesize a Fe doped porous carbon nitride catalyst (Fe-CN) using an ionothermal strategy and subsequently loaded it on the hydrophobic carbon felt (CF) to fabricate the Fe-CN/CF photoelectric cathode. This cathode benefits from the synergistic effects between the porous CN support and the highly dispersed Fe species, which enhance O2 absorption and activation. Additionally, the hydrophobic CF serves as a gas diffusion layer, accelerating O2 mass transfer. These features enable the Fe-CN/CF cathode to demonstrate notable photoelectrocatalytic (PEC) degradation efficiency. Specifically, under optimal conditions (cathodic bias of -0.3 VAg/AgCl, pH 7, and a catalyst loading of 3 mg/cm2), the system achieves a 76.4% removal rate of tetracycline (TC) within 60 min. The general application potential of this system is further underscored by its ability to remove approximately 98% of 4-chlorophenol (4-CP) and phenol under identical conditions. Subsequent investigations into the active species and degradation pathways reveal that 1O2 and h+ play dominant role during the PEC degradation process, leading to gradually breakdown of TC into less toxicity, smaller molecular intermediates. This work presents a straightforward yet effective strategy for constructing efficient PEC systems that leverage molecular oxygen activation to degrade pollutants.

Engineering Oxygen Vacancies on Mixed-Valent Mesoporous α-MnO2 for High-Performance Asymmetric Supercapacitors.

Intrinsically poor conductivity and sluggish ion-transfer kinetics limit the further development of electrochemical storage of mesoporous manganese dioxide. In order to overcome the challenge, defect engineering is an effective way to improve electrochemical capability by regulating electronic configuration at the atomic level of manganese dioxide. Herein, we demonstrate effective construction of defects on mesoporous α-MnO2 through simply controlling the degree of redox reaction process, which could obtain a balance between Mn3+/Mn4+ ratio and oxygen vacancy concentration for efficient supercapacitors. The different structures of α-MnO2 including the morphology, specific surface area, and composition are successfully constructed by tuning the mole ratio of KMnO4 to Na2SO3. The electrode materials of α-MnO2-0.25 with an appropriate Mn3+/Mn4+ ratio and abundant oxygen vacancy showed an outstanding specific capacitance of 324 F g-1 at 0.5 A g-1, beyond most reported MnO2-based materials. The asymmetric supercapacitors formed from α-MnO2-0.25 and activated carbon can present an energy density as high as of 36.33 W h kg-1 at 200 W kg-1 and also exhibited good cycle stability over a wide voltage range from 0 to 2.0 voltage (kept at approximately 98% after 10 000 cycles in galvanostatic cycling tests) and nearly 100% Coulombic efficiency. Our strategy lays a foundation for fine regulation of defects to improve charge-transfer kinetics.

Understanding deep dehydrogenation and cracking of n-butane on Ni(111) by a DFT study.

Steam reforming is a main industrial process for hydrogen production. In particular, with the carbon chain increasing to n-butane, a main component in liquefied petroleum gas (LPG) and shale oil gas, chemically different C-C bonds ((C-C)α,ß and (C-C)ß,ß') will be involved in cleavages. In addition, understanding the role of catalysis in these pathways is critical toward the advancement in technology, yet is largely lacking. As such, we have performed density functional theory (DFT) calculations to study the two possible C-C cleavage pathways of n-butane on Ni(111), i.e., the (C-C)α,ß cleavage from the n-butane deep dehydrogenation product of 1-butyne, and the (C-C)ß,ß' cleavage from 2-butyne. The results indicate that these two different pathways have distinct dehydrogenations to butyne, and that Ni is suitable for the deep dehydrogenation. The C-C cleavage in both pathways serves as the rate-determining step with a higher energy barrier than that for the preceding C-H bond cleavage. In addition, the 1-butyne pathway was found to be more favorable than that of 2-butyne in thermodynamics and kinetics. Our results provide insights into the alkane dehydrogenation and cracking of long-chain hydrocarbons on Ni-based catalysts.

Comparison Study on the Prediction of Multiple Molecular Properties by Various Neural Networks.

Various neural networks, including a single layer neural network (SLNN), a deep neural network (DNN) with multilayers, and a convolution neural network (CNN) have been developed and investigated to predict multiple molecular properties simultaneously. The data set of this work contains∼134 kilo molecules and their 15 properties (including rotational constant A, B, and C, dipole moment, isotropic polarizability, energy of HOMO, energy of LUMO, HOMO-LUMO gap energy, electronic spatial extent, zero point vibrational energy, internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, free energy at 298.15 K, and heat capacity at 298.15 K) at the hybrid density functional theory (DFT) level from the QM9 database. Coulomb matrix (CM) converted from the database representing every molecule uniquely and its eigenvalue are respectively used as the input of machine learning. The accuracies of predictions have been compared among SLNN, DNN and CNN by analyzing their mean absolute errors (MAEs). Using eigenvalues as input, both SLNN and DNN can give higher accuracy for the prediction of specific energy properties ( U0, U, H, and G). For the prediction of all 15 molecular properties at a time, DNN with a 3-layers network exhibits the best results using the full CM as input. The number of layers in DNN play a key role in the prediction of multiple molecular properties simultaneously. This work may provide possibility and guidance for the selection of different neural networks and input data forms for prediction and validation of multiple parameters according to different needs.

Boosting photo-thermal co-catalysis CO2 methanation by tuning interface electron transfer via Mott-Schottky heterojunction effect.

Photo-thermal co-catalytic reduction of CO2 to synthesize value-added chemicals presents a promising approach to addressing environmental issues. Nevertheless, traditional catalysts exhibit low light utilization efficiency, leading to the generation of a reduced number of electron-hole pairs and rapid recombination, thereby limiting catalytic performance enhancement. Herein, a Mott-Schottky heterojunction catalyst was developed by incorporating nitrogen-doped carbon coated TiO2 supported nickel (Ni) nanometallic particles (Ni/x-TiO2@NC). The optimal Ni/0.5-TiO2@NC sample displayed a conversion rate of 71.6 % and a methane (CH4) production rate of 65.3 mmol/(gcat·h) during photo-thermal co-catalytic CO2 methanation under full-spectrum illumination, with a CH4 selectivity exceeding 99.6 %. The catalyst demonstrates good stability as it shows no decay after two reaction cycles. The Mott-Schottky heterojunction catalysts display excellent efficiency in separating photo-generated electron-hole pairs and elevate the catalysts' temperature, thus accelerating the reaction rate. The in-situ experiments revealed that light-induced electron transfer effectively facilitates H2 dissociation and enhances surface defects, thereby promoting CO2 adsorption. This study introduces a novel approach for developing photo-thermal catalysts for CO2 reduction, aiming to enhance solar energy utilization and facilitate interface electron transfer.

Few-Layer Meets Crystalline Structure: Collaborative Efforts for Improving Photocatalytic H2O2 Generation over Carbon Nitride.

Although the conversion of O2 and H2O to H2O2 over graphite carbon nitride (g-C3N4) has been realized by means of the photocatalytic process, the catalytic activity of pristine g-C3N4 is still restricted by the rapid charge recombination and inadequate exposure of the active site. In this work, we propose a straightforward strategy to solve these limitations by decreasing the thickness and improving the crystallinity of g-C3N4, resulting in the preparation of few-layered crystalline carbon nitride (FL-CCN). Benefiting from the minimal thickness and highly ordered in-plane triangular cavities within the structure, FL-CCN processes an extended π-conjugated system with a reduced charge transfer resistance and expanded specific surface area. These features accelerate the efficiency of photogenerated charge separation in FL-CCN and contribute to explore of its surface active sites. Consequently, FL-CCN exhibits a significantly improved H2O2 evolution rate (63.95 µmol g-1 h-1), which is 7.8 times higher than that of pristine g-C3N4 (8.15 µmol g-1 h-1), during the photocatalytic conversion of O2 and H2O. This systematic investigation offers valuable insights into the mechanism of photocatalytic H2O2 generation and the development of efficient catalysts.

Methane Dry Reforming by Ni-Cu Nanoalloys Anchored on Periclase-Phase MgAlOx Nanosheets for Enhanced Syngas Production.

Stable and efficient syngas production via methane dry reforming is highly desirable as it utilizes two greenhouse gases simultaneously. In this work, active Ni-Cu nanoalloys stably anchored on periclase-phase MgAlOx nanosheets were successfully synthesized by a hydrothermal method. These highly dispersed small Ni-Cu alloys strongly interacted with the periclase-phase MgAlOx nanosheets, on which abundant base sites were accessible. On the optimal catalyst (6Ni6CuMgAl-S), methane and carbon dioxide conversion always reached 85 and 90% at 700 °C under a gas hour speed velocity of 40,000 mL/gcat h for more than 70 h. The hydrogen production rate was maintained at 1.8 mmol/min, and the ratio of H2/CO was kept at approximately 0.96 under a CH4 and CO2 flow rate of 25 mL/min. Coke deposition and Ni sintering were effectively suppressed by the formation of a Ni-Cu alloy, the laminar structure, and the periclase phase of the MgAlOx support. Moreover, the alloy nanoparticles were reconstructed into a segregated Ni-Cu alloy structure in response to the reaction environment, and this structure was more stable and still active. Density functional theory calculations showed that carbon adsorption was inhibited on the segregated Ni-Cu alloy. Furthermore, the experimental thermogravimetric and O2-TPO results confirmed the significant decrease in carbon deposition on the Ni-Cu alloy catalysts.

Ni-modified MoS2 nanoflake arrays with stepped sites on carbon nanotubes for efficient hydrodesulfurization of coal-to-liquid fuel.

Carbon nanotube (CNT)-supported Ni-modified MoS2 catalysts with ultra-high loading were synthesized with the assistance of citric acid. The morphology of the nanoflake arrays could be controlled to give abundant stepped sites, which favored the hydrogenation desulfurization pathway of dibenzothiophene. The catalyst exhibited excellent performance and stability for hydrodesulfurization of model oil and coal-to-liquid fuel.