Single-atom catalysts anchoring offers a desirable pathway for efficiency maximization and cost-saving for photocatalytic hydrogen evolution. However, the single-atoms loading amount is always within 0.5% in most of the reported due to the agglomeration at higher loading concentrations. In this work, the highly dispersed and large loading amount (>1 wt%) of copper single-atoms were achieved on TiO2, exhibiting the H2 evolution rate of 101.7 mmol g−1 h−1 under simulated solar light irradiation, which is higher than other photocatalysts reported, in addition to the excellent stability as proved after storing 380 days. More importantly, it exhibits an apparent quantum efficiency of 56% at 365 nm, a significant breakthrough in this field. The highly dispersed and large amount of Cu single-atoms incorporation on TiO2 enables the efficient electron transfer via Cu2+-Cu+ process. The present approach paves the way to design advanced materials for remarkable photocatalytic activity and durability.

Graphical models are widely used to model stochastic dependences among large collections of variables. We introduce a new method of estimating undirected conditional independence graphs based on the score matching loss, introduced by Hyvärinen (2005), and subsequently extended in Hyvärinen (2007). The regularized score matching method we propose applies to settings with continuous observations and allows for computationally efficient treatment of possibly non-Gaussian exponential family models. In the well-explored Gaussian setting, regularized score matching avoids issues of asymmetry that arise when applying the technique of neighborhood selection, and compared to existing methods that directly yield symmetric estimates, the score matching approach has the advantage that the considered loss is quadratic and gives piecewise linear solution paths under ℓ1 regularization. Under suitable irrepresentability conditions, we show that ℓ1-regularized score matching is consistent for graph estimation in sparse high-dimensional settings. Through numerical experiments and an application to RNAseq data, we confirm that regularized score matching achieves state-of-the-art performance in the Gaussian case and provides a valuable tool for computationally efficient estimation in non-Gaussian graphical models.

Assembly of different metal-organic framework (MOF) building blocks into hybrid MOF-on-MOF heterostructures is promising in chemistry and materials science, however the development of ternary MOF-on-MOF heterostructures with controllable architectural and compositional complexity is challenging. Here we report the synthesis of three types of ternary MOF-on-MOF heterostructures via a multiple selective assembly strategy. This strategy relies on the choice of one host MOF with more than one facet that can arrange the growth of a guest MOF, where the arrangement is site-selective without homogenous growth of guest MOF or homogenous coating of guest on host MOF. The growth of guest MOF on a selected site of host MOF in each step provides the opportunity to further vary the combinations of arrangements in multiple steps, leading to ternary MOF-on-MOF heterostructures with tunable complexity. The developed strategy paves the way towards the rational design of intricate and unprecedented MOF-based superstructures for various applications.

A site-specific epitaxial-growth strategy is developed to synthesize two types of MOF-on-MOF heterostructures with elaborately designed architectures, which are comprised of two MOFs with totally different compositions.

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Pulmonary gas exchange is impaired after lower limb ischemia-reperfusion associated with the clinical use of a tourniquet for lower limb surgery. Ischemic preconditioning preceding tourniquet-induced ischemia attenuates lipid peroxidation and systemic inflammatory response and mitigates pulmonary dysfunction.