Machine learning potential for modelling H2 adsorption/diffusion in MOFs with open metal sites.

Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO2 capture, natural gas purification and H2 storage. This has been ascribed to strong specific interactions between OMS and the guest molecules that enable the MOF to achieve an effective capture even under low gas pressure conditions. In particular, the presence of OMS in MOFs was demonstrated to substantially boost the H2 binding energy for achieving high adsorbed hydrogen densities and large usable hydrogen capacities. So far, there is a critical bottleneck to computationally attain a full understanding of the thermodynamics and dynamics of H2 in this sub-class of MOFs since the generic classical force fields (FFs) are known to fail to accurately describe the interactions between OMS and any guest molecules, in particular H2. This clearly hampers the computational-assisted identification of MOFs containing OMS for a target adsorption-related application since the standard high-throughput screening approach based on these generic FFs is not applicable. Therefore, there is a need to derive novel FFs to achieve accurate and effective evaluation of MOFs for H2 adsorption. On this path, as a proof-of-concept, the soc-MOF-1d containing OMS, previously envisaged as a potential platform for H2 adsorption, was selected as a benchmark material and a machine learning potential (MLP) was derived for the Al-soc-MOF-1d from a dataset initially generated by ab initio molecular dynamics (AIMD) simulations. This MLP was further implemented in MD simulations to explore the H2 binding modes as well as the temperature dependence distribution of H2 in the MOF pores from 10 K to 80 K. MLP-Grand Canonical Monte Carlo (GCMC) simulations were then performed to predict the H2 sorption isotherm of Al-soc-MOF-1d at 77 K that was further confirmed using sorption data we collected on this sample. As a further step, MLP-based molecular dynamics (MD) simulations were conducted to anticipate the kinetics of H2 in this MOF. This work delivers the first MLP able to describe accurately the interactions between the challenging H2 guest molecule and MOFs containing OMS. This innovative strategy applied to one of the most complex molecules owing to its highly polarizable nature, paves the way towards a more systematic accurate and efficient in silico assessment of MOFs containing OMS for H2 adsorption and beyond to the low-pressure capture of diverse molecules.

A database of low-energy atomically precise nanoclusters.

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies.

Heteropoly Blue/Carbon Nanotubes Nanocomposites as High-Performance Photothermal Conversion Materials.

To realize the direct and full use of the widely distributed solar energy, developing novel materials with superb photothermal conversion capability is essential. Although heteropoly blue has intrinsic outstanding solar absorption and photothermal conversion properties, its spectral absorption in the infrared region is weak. Here, composites of heteropoly blue and carbon nanotubes (HPB/CNTs) are synthesized depending on electrostatic interactions by facile microwave sonication and freeze-drying. The doped CNTs can dramatically improve the spectral absorption performance of HPB ontology in the infrared region. As a result, the light absorption of the optimized HPB/CNTs (20 %) reaches more than 95 % in the range of 200-2400 nm, showing promising prospects as high-performance photothermal conversion material in the applications of solar desalination and wastewater treatment.

ZrN6 -Doped Graphene for Ammonia Synthesis: A Density Functional Theory Study.

Considering the pivotal role of ammonia in the modern chemical industry, designing effective catalysts for the N2 -to-NH3 conversion stimulates great research enthusiasms. In this work, by means of density functional theory calculations, we systematically investigated the electrocatalysis of six-coordinated transition metal atom anchored graphene for nitrogen fixation. The free energy analysis shows that the ZrN6 configuration has a good activity toward ammonia synthesis under overpotential of 0.51 V. According to the electron transfer analysis, ZrN6 site plays a bridging role in charge transfer between the functional graphene and the reactant. Furthermore, the presence of N6 coordination increases the electron accumulation on the NNHx intermediates, which weakens the intermolecular N-N bond, reducing the thermodynamic barrier of protonation process. This work provides a basic understanding of the interaction between transition metal and the adjacent coordination in tuning the reactivity.

CT imaging features of 34 patients infected with COVID-19.

OBJECTIVE: To retrospectively analyze the CT findings in patients infected with Coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: The thirty-four cases, 15 females and 19 males, with an age ranging from 7 to 88 years old, confirmed by real-time reverse-transcriptase-polymerase chain reaction (RT-PCR), were used for our study. All thin-section CT scans of the lungs were performed in all of patients. The clinical, laboratory and CT imaging were available to evaluate in all patients. RESULTS: The patients present with fever (85.29%, n = 29), cough (67.65%, n = 23), fatigue or myalgia (26.47%, n = 9), and pharyngalgia (8.82%, n = 3). The 4 patients (11.76%) with no symptoms were identified during screening for close contacts, who had typical CT findings. On initial CT scans, the bilateral lung involved was shown in 24 cases (70.59%), while 29 (82.35%) cases were distributed in peripheral. The pure ground glass opacity (GGO) was shown in 18 cases (52.94%), the GGO with consolidation was in 12 cases (35.29%), and full consolidation only in 3 cases. The lesion with air bronchogram was seen in 14 (41.18%) cases, with enlarged blood vessel in 17 (50.00%) cases, with crazy-paving pattern in 8 (23.53%) cases, with fine reticular pattern in 4 (11.77%) cases, and with intralesional vacuole sign in 6 (17.65%) cases. The pleural effusion was seen in one patient. Follow-up imaging in 19 patients during the study time window demonstrated mild, moderate or severe progression of disease, as manifested by increasing extent and density of lung opacities. CONCLUSIONS: The bilateral GGO with air bronchogram, enlarged blood vessel, fine reticular pattern, and peripheral distribution are the early CT findings of COVID-19. The crazy-paving pattern and intralesional vacuole sign are the features of progressive stage.

Determining the adsorption energies of small molecules with the intrinsic properties of adsorbates and substrates.

Adsorption is essential for many processes on surfaces; therefore, an accurate prediction of adsorption properties is demanded from both fundamental and technological points of view. Particularly, identifying the intrinsic determinants of adsorption energy has been a long-term goal in surface science. Herein, we propose a predictive model for quantitative determination of the adsorption energies of small molecules on metallic materials and oxides, by using a linear combination of the valence and electronegativity of surface atoms and the coordination of active sites, with the corresponding prefactors determined by the valence of adsorbates. This model quantifies the effect of the intrinsic properties of adsorbates and substrates on adsorbate-substrate bonding, derives naturally the well-known adsorption-energy scaling relations, and accounts for the efficiency and limitation of engineering the adsorption energy and reaction energy. All involved parameters are predictable and thus allow the rapid rational design of materials with optimal adsorption properties.