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Journal of Materials Chemistry A

ISSN: 2050-7496

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Datasets associated with articles published in Journal of Materials Chemistry A

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1970
2024
1970 2024
915 results
  • Research Data Repository for Ambient-Air-Stable Inorganic Cs2SnI6 Double Perovskite Thin Films via Aerosol-Assisted Chemical Vapour Deposition
    Research Data Repository for the published paper entitled "Ambient-Air-Stable Inorganic Cs2SnI6 Double Perovskite Thin Films via Aerosol-Assisted Chemical Vapour Deposition".
    • Dataset
  • Dithienosilole-based non-fullerene acceptors for efficient organic photovoltaics 数据集
    Dithienosilole-based non-fullerene acceptors for efficient organic photovoltaics† Zhongbo Zhang ab and Xiaozhang Zhu *ab Author affiliations * Corresponding authors a Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China E-mail: xzzhu@iccas.ac.cn b University of Chinese Academy of Sciences, Beijing 100049, China
    • Dataset
  • Supplementary information files for Tracking the solid-state incorporation of Sn into the framework of dealuminated zeolite beta, and consequences for catalyst design
    Supplementary files for article Tracking the solid-state incorporation of Sn into the framework of dealuminated zeolite beta, and consequences for catalyst design Sn-Beta has emerged as a state-of-the-art catalyst for a range of sustainable chemical transformations. Conventionally prepared by bottom-up hydrothermal synthesis methods, recent research has demonstrated the efficiency of several top-down methods of preparation. One attractive top-down approach is Solid-State Incorporation, where a dealuminated Beta zeolite is physically mixed with a solid Sn precursor, in particular Sn(ii) acetate, prior to heat treatment at 550 °C. This procedure is fast and benign, and metal incorporation requires no solvents and hence produces no aqueous Sn-containing waste streams. Although the performances of these catalysts have been well explored in recent years, the mechanism of heteroatom incorporation remains unknown, and hence, opportunities to improve the synthetic procedure via a molecular approach remain. Herein, we use a range of in situ spectroscopic techniques, alongside kinetic and computational methods, to elucidate the mechanisms that occur during preparation of the catalyst, and then improve the efficacy of the synthetic protocol. Specifically, we find that successful incorporation of Sn into the lattice occurs in several distinct steps, including (i) preliminary coordination of the metal ion to the vacant lattice sites of the zeolite during physical grinding; (ii) partial incorporation of the metal ion into the zeolite framework upon selective decomposition of the acetate ligands, which occurs upon heating the physical mixture in an inert gas flow from room temperature to 550 °C; and (iii) full isomorphous substitution of Sn into the lattice alongside its simultaneous oxidation to Lewis acidic Sn(iv), when the physically mixed material is exposed to air during a short (<1 h) isotherm period. Long isotherm steps are shown to be unnecessary, and fully oxidised Sn(iv) precursors are shown to be unsuitable for successful incorporation into the lattice. We also find that the formation of extra-framework Sn oxides is primarily dependent on the quantity of Sn present in the initial physical mixture. Based on these findings, we demonstrate a faster synthetic protocol for the preparation of Sn-Beta materials via Solid-State Incorporation, and benchmark their catalytic performance for the Meerwein-Ponndorf-Verley transfer hydrogenation reaction and the isomerisation of glucose to fructose.
    • Dataset
  • Data Sets Utilized in "Simple Local Environment Descriptors for Accurate Prediction of Hydrogen Absorption and Migration in Metal Alloys" Article.
    The dataset is sourced from the article "Simple Local Environment Descriptors for Accurate Prediction of Hydrogen Absorption and Migration in Metal Alloys." It comprises seven distinct data sets employed for training and testing machine learning models in the article. These datasets are named as follows: 1. HEA_TEST.csv 2. TS_TRAIN_ALL.csv 3. HEA_TRAINING_OCTA_TETRA_TS.csv 4. ZPE_HEA_TRAINING_OCTA_TETRA.csv 5. TEST_ZPE_SET.csv 6. TS_TEST_ALL.csv 7. Intermetallics.csv Each dataset contains columns related to hydrogen absorption energy or zero-point energy and various descriptors, including site information, electronegativity average, average number of valence electrons, average ionization energies, mean d-band center, d-band center, mean d-band filling, mean d-band width, maximum phonon center, mean phonon center, sum phonon center, average ionic radius, average atomic radius, average van der Waals radius, average metallic radius, pore radius (in Me units), ideal pore radius (in Me units), interstitial pore type, and alloy composition. This set of descriptors is versatile and can be effectively utilized for predicting hydrogen absorption energy (or zero-point energy) in different interstitial positions within High Entropy Alloys (HEA) and intermetallic alloys. The descriptors capture crucial information about the local environment, facilitating accurate predictions of hydrogen-related properties in various atomic arrangements and configurations found in these materials.
    • Dataset
  • New approaches to three-dimensional positive electrodes enabling scalable high areal capacity (dataset)
    Journal of Materials Chemistry A Accepted Manuscript. Journal of Materials Chemistry A, 2024, DOI: 10.1039/D3TA07139A doi to be added in proofing
    • Dataset
  • Supplementary information files for Tracking the solid-state incorporation of Sn into the framework of dealuminated zeolite beta, and consequences for catalyst design
    Supplementary files for article Tracking the solid-state incorporation of Sn into the framework of dealuminated zeolite beta, and consequences for catalyst design Sn-Beta has emerged as a state-of-the-art catalyst for a range of sustainable chemical transformations. Conventionally prepared by bottom-up hydrothermal synthesis methods, recent research has demonstrated the efficiency of several top-down methods of preparation. One attractive top-down approach is Solid-State Incorporation, where a dealuminated Beta zeolite is physically mixed with a solid Sn precursor, in particular Sn(ii) acetate, prior to heat treatment at 550 °C. This procedure is fast and benign, and metal incorporation requires no solvents and hence produces no aqueous Sn-containing waste streams. Although the performances of these catalysts have been well explored in recent years, the mechanism of heteroatom incorporation remains unknown, and hence, opportunities to improve the synthetic procedure via a molecular approach remain. Herein, we use a range of in situ spectroscopic techniques, alongside kinetic and computational methods, to elucidate the mechanisms that occur during preparation of the catalyst, and then improve the efficacy of the synthetic protocol. Specifically, we find that successful incorporation of Sn into the lattice occurs in several distinct steps, including (i) preliminary coordination of the metal ion to the vacant lattice sites of the zeolite during physical grinding; (ii) partial incorporation of the metal ion into the zeolite framework upon selective decomposition of the acetate ligands, which occurs upon heating the physical mixture in an inert gas flow from room temperature to 550 °C; and (iii) full isomorphous substitution of Sn into the lattice alongside its simultaneous oxidation to Lewis acidic Sn(iv), when the physically mixed material is exposed to air during a short (<1 h) isotherm period. Long isotherm steps are shown to be unnecessary, and fully oxidised Sn(iv) precursors are shown to be unsuitable for successful incorporation into the lattice. We also find that the formation of extra-framework Sn oxides is primarily dependent on the quantity of Sn present in the initial physical mixture. Based on these findings, we demonstrate a faster synthetic protocol for the preparation of Sn-Beta materials via Solid-State Incorporation, and benchmark their catalytic performance for the Meerwein-Ponndorf-Verley transfer hydrogenation reaction and the isomerisation of glucose to fructose.
    • Dataset
  • Data Sets Utilized in "Simple Local Environment Descriptors for Accurate Prediction of Hydrogen Absorption and Migration in Metal Alloys" Article.
    The dataset is sourced from the article "Simple Local Environment Descriptors for Accurate Prediction of Hydrogen Absorption and Migration in Metal Alloys." It comprises seven distinct data sets employed for training and testing machine learning models in the article. These datasets are named as follows: 1. HEA_TEST.csv 2. TS_TRAIN_ALL.csv 3. HEA_TRAINING_OCTA_TETRA_TS.csv 4. ZPE_HEA_TRAINING_OCTA_TETRA.csv 5. TEST_ZPE_SET.csv 6. TS_TEST_ALL.csv 7. Intermetallics.csv Each dataset contains columns related to hydrogen absorption energy or zero-point energy and various descriptors, including site information, electronegativity average, average number of valence electrons, average ionization energies, mean d-band center, d-band center, mean d-band filling, mean d-band width, maximum phonon center, mean phonon center, sum phonon center, average ionic radius, average atomic radius, average van der Waals radius, average metallic radius, pore radius (in Me units), ideal pore radius (in Me units), interstitial pore type, and alloy composition. This set of descriptors is versatile and can be effectively utilized for predicting hydrogen absorption energy (or zero-point energy) in different interstitial positions within High Entropy Alloys (HEA) and intermetallic alloys. The descriptors capture crucial information about the local environment, facilitating accurate predictions of hydrogen-related properties in various atomic arrangements and configurations found in these materials.
    • Dataset
  • Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based cathodes used in CO2 electrolysers"
    Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based CO2 electrolysers", published in Journal of Materials Chemistry A 2023 11:5083–5094, doi: 10.1039/D2TA06965B Folder names describe the type of data content.
    • Dataset
  • 木质素磺酸功能化g-C3N4/碳化木海绵高效清除重金属离子
    The data of characterization, performance, mechanism and application of LS-C3N4 charcoal gel are in the folder of LS-C3N4 charcoal gel.
    • Dataset
  • A hierarchical hybrid monolith: MoS42-intercalated NiFe layered double hydroxide nanosheet arrays assembled on carbon foam for highly efficient heavy metal removal
    The characterization, properties and experimental mechanism of NiFe layered double hydroxide nanosheet array materials are placed in the folder.
    • Dataset
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