An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Herein, we present a new series of CuI-based hybrid materials with tunable structures and semiconducting properties. The CuI inorganic modules can be tailored into a one-dimensional (1D) chain and two-dimensional (2D) layer and confined/stabilized in coordination frameworks of potassium isonicotinic acid (HINA) and its derivatives (HINA-R, R = OH, NO2, and COOH). The resulting CuI-based hybrid materials exhibit interesting semiconducting behaviors associated with the dimensionality of the inorganic module; for instance, the structures containing the 2D-CuI module demonstrate significantly enhanced photoconductivity with a maximum increase of five orders of magnitude compared to that of the structures containing the 1D-CuI module. They also represent the first CuI-bearing hybrid chemiresistive gas sensors for NO2 with boosted sensing performance and sensitivity at multiple orders of magnitude over that of the pristine CuI. Particularly, the sensing ability of CuI-K-INA containing both 1D- and 2D-CuI modules is comparable to those of the best NO2 chemiresistors reported thus far.
Hydrogen storage by means of catalytic hydrogenation of suitable organic substrates helps to elevate the volumetric density of hydrogen energy. In this regard, utilizing cheaper industrial crude hydrogen to fulfill the goal of hydrogen storage would show economic attraction. However, because CO impurities in crude hydrogen can easily deactivate metal active sites even in trace amounts such a process has not yet been realized. Here, we develop a robust RuNi/TiO2 catalyst that enables the efficient hydrogenation of toluene to methyl-cyclohexane under simulated crude hydrogen feeds with 1000-5000 ppm CO impurity at around 180 °C under atmospheric pressure. We show that the co-localization of Ru and Ni species during reduction facilitated the formation of tightly coupled metallic Ru-Ni clusters. During the catalytic hydrogenation process, due to the distinct bonding properties, Ru and Ni served as the active sites for CO methanation and toluene hydrogenation respectively. Our work provides fresh insight into the effective utilization and purification of crude hydrogen for the future hydrogen economy.
Polyethylene terephthalate (PET) and CO2 , two chemical wastes that urgently need to be transformed in the environment, are converted simultaneously in a one-pot catalytic process through the synergistic coupling of three reactions: CO2 hydrogenation, PET methanolysis and dimethyl terephthalate (DMT) hydrogenation. More interestingly, the chemical equilibria of both reactions were shifted forward due to a revealed dual-promotion effect, leading to significantly enhanced PET depolymerization. The overall methanol yield from CO2 hydrogenation exceeded the original thermodynamic equilibrium limit since the methanol was in situ consumed in the PET methanolysis. The degradation of PET by a stoichiometric ratio of methanol was significantly enhanced because the primary product, DMT was hydrogenated to dimethyl cyclohexanedicarboxylate (DMCD) or p-xylene (PX). This synergistic catalytic process provides an effective way to simultaneously recycle two wastes, polyesters and CO2 , for producing high-value chemicals.
Abstract Capped chelating organic molecules are presented as a design principle for tuning heterogeneous nanoparticles for electrochemical catalysis. Gold nanoparticles (AuNPs) functionalized with a chelating tetradentate porphyrin ligand show a 110‐fold enhancement compared to the oleylamine‐coated AuNP in current density for electrochemical reduction of CO 2 to CO in water at an overpotential of 340 mV with Faradaic efficiencies (FEs) of 93 %. These catalysts also show excellent stability without deactivation (<5 % productivity loss) within 72 hours of electrolysis. DFT calculation results further confirm the chelation effect in stabilizing molecule/NP interface and tailoring catalytic activity. This general approach is thus anticipated to be complementary to current NP catalyst design approaches.
Abstract Conversion of syngas (CO, H 2 ) to hydrocarbons, commonly known as the Fischer–Tropsch (FT) synthesis, represents a fundamental pillar in today's chemical industry and is typically carried out under technically demanding conditions (1–3 MPa, 300–400 °C). Photocatalysis using sunlight offers an alternative and potentially more sustainable approach for the transformation of small molecules (H 2 O, CO, CO 2 , N 2 , etc.) to high‐valuable products, including hydrocarbons. Herein, a novel series of Fe‐based heterostructured photocatalysts (Fe‐ x ) is successfully fabricated via H 2 reduction of ZnFeAl‐layered double hydroxide (LDH) nanosheets at temperatures ( x ) in the range 300–650 °C. At a reduction temperature of 500 °C, the heterostructured photocatalyst formed (Fe‐500) consists of Fe 0 and FeO x nanoparticles supported by ZnO and amorphous Al 2 O 3 . Fe‐500 demonstrates remarkable CO hydrogenation performance with very high initial selectivities toward hydrocarbons (89%) and especially light olefins (42%), and a very low selectivity towards CO 2 (11%). The intimate and abundant interfacial contacts between metallic Fe 0 and FeO x in the Fe‐500 photocatalyst underpins its outstanding photocatalytic performance. The photocatalytic production of high‐value light olefins with suppressed CO 2 selectivity from CO hydrogenation is demonstrated here.
Geological mapping involves the identification of elements such as rocks, soils, and surface water, which are fundamental tasks in Geological Environment Remote Sensing (GERS) interpretation. High-precision intelligent interpretation technology can not only reduce labor requirements and significantly improve the efficiency of geological mapping but also assist geological disaster prevention assessment and resource exploration. However, the high interclass similarity, high intraclass variability, gradational boundaries, and complex distributional characteristics of GERS elements coupled with the difficulty of manual labeling and the interference of imaging noise, all limit the accuracy of DL-based methods in wide-area GERS interpretation. We propose a Transformer-based multi-stage and multi-scale fusion network, RSWFormer (Rock–Soil–Water Network with Transformer), for geological mapping of spatially large areas. RSWFormer first uses a Multi-stage Geosemantic Hierarchical Sampling (MGHS) module to extract geological information and high-dimensional features at different scales from local to global, and then uses a Multi-scale Geological Context Enhancement (MGCE) module to fuse geological semantic information at different scales to enhance the understanding of contextual semantics. The cascade of the two modules is designed to enhance the interpretation and performance of GERS elements in geologically complex areas. The high mountainous and hilly areas located in western China were selected as the research area. A multi-source geological remote sensing dataset containing diverse GERS feature categories and complex lithological characteristics, Multi-GL9, is constructed to fill the significant gaps in the datasets required for extensive GERS. Using overall accuracy as the evaluation index, RSWFormer achieves 92.15% and 80.23% on the Gaofen-2 and Landsat-8 datasets, respectively, surpassing existing methods. Experiments show that RSWFormer has excellent performance and wide applicability in geological mapping tasks.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
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