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Systematicly tuning the porosities of porous materials is crucial for targeted gas mixture separation, yet it remains a long-standing challenge. As a common strategy, altering the phenylene ring count in the organic building units has been exploited to adjust pore apertures in metal organic frameworks (MOFs) and covalent organic frameworks (COFs), resulting in only a stepwise pore size variation of approximately 2.8 Å. Unlike MOFs and COFs, the porosities for supramolecular assembly frameworks are highly dependent on the packings of the molecular building units in the lattice. The above strategy cannot be applied to supramolecular assembly frameworks and other strategies are required to adjust pore apertures in supramolecular frameworks, especially to achieve porous materials with the optimal pore size for D2/H2separation (∼3 Å). Here, we successfully modulate the porosity of supramolecular frameworks of [CoII12CoIII8L12(μ3-OH)24(X)]11+clusters (L = mono-deprotonated 1-iminoisoindole-3-amine, X = ClO4−or Cl−) by controlling the duration of self-assembly processes. By simply adjusting the reaction time, we obtain three [CoII12CoIII8L12(μ3-OH)24(X)]11+supramolecular frameworks: [CoII12CoIII8(μ3-OH)24(ClO4)L12)]·(Cl)4·(HCO2)7(1), [CoII12CoIII8L12(μ3-OH)24(Cl)]·(ClO4)4·(Cl)6·(HCO2)·(H2O)10(2), and [CoII12CoIII8L12(μ3-OH)24(Cl)]·(Cl)11·(H2O)12(3). The different stacking patterns of the [CoII12CoIII8L12(η3-OH)24(X)]11+clusters in these frameworks lead to significantly varied stabilities and porosities. Compound 1exhibits permanent porosity with interconnected channels and maintains stability under harsh conditions. In contrast, compound 2is nearly nonporous, and compound 3becomes unstable upon desolvation. The pores of compound 1show higher affinity to D2than H2, and the pore aperture diameter of compound 1(∼3.06 Å) meets the optimal porosity for D2/H2separation. Consequently, compound 1demonstrates moderate D2/H2separation at 77 K (retention time: ∼7 min/g for D2/H2/Ne (10/10/80 vol%) in a flow of 5 mL/min). These results provide a new strategy to tune the porosities of porous supramolecular materials for target gas mixture separation.

The serious CO poisoning for traditional Pt-based catalysts often results in a large deterioration in activity and stability of formic acid oxidation reaction (FAOR), which severely restricts the development of direct formic acid fuel cells. It is of great significance and challenging to enhance the CO tolerance for an advanced Pt-based catalyst. Whereupon, in this work, successful synthesis of a brush-like hierarchical structure composed by ordered ultrathin PtTe2intermetallic compounds is achieved. Benefiting from the electron transfer from Pt to Te, the relatively poor electron density of Pt can assist the down shift of the d-band center of PtTe2, which greatly weakens the adsorption affinity toward CO and enhances the antipoisoning ability. Besides, in situ infrared spectroscopy and density functional theory simulation further confirm the special electron structure can promote the dehydrogenation pathway during the whole FAOR proceeding. Specifically, the mass activity of PtTe2is up to 4.72 A mgpt–1, which is about 9.25-fold higher than that of the commercial Pt/C. In addition, PtTe2still has higher catalytic activity than commercial Pt/C even after 3 h. This work inspires targeted enhancement of the antipoisoning properties of Pt-based catalysts through the design of atomic level ordered structure and ultrathin two-dimensional morphology and is beneficial to the considerable progress of fuel cell catalysis.

The multi-view data with incomplete information hinder effective data analysis. Existing multi-view imputation methods, which learn the mapping between a complete view and a completely missing view, are not able to deal with the typical multi-view data with missing feature information. In this paper, we propose a unified generative imputation model named ${\sf UGit}$UGit with optimal transport theory to simultaneously impute the missing features/values of all incomplete views. This imputation is conditional on all the observed values from the multi-view data. ${\sf UGit}$UGit consists of two modules, i.e., a unified multi-view generator (UMG) and a masking energy discriminator (MED). To effectively and efficiently impute missing features across all views, the generator UMG employs a unified autoencoder in conjunction with the cross-view attention mechanism to learn the data distribution from all observed multi-view data. The discriminator MED leverages a novel masking energy divergence function to make ${\sf UGit}$UGit differentiable for imputation accuracy enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, ${\sf UGit}$UGit speeds up the model training by 4.28x with more than 41% accuracy gain on average, compared to the state-of-the-art approaches.

Designing all-inorganic double perovskites through element mixing is a promising strategy to enhance their optoelectronic performance and structural stability. The complex interplay between multilevel structures and optoelectronic properties in element-mixed double perovskites necessitates further in-depth theoretical exploration. In this study, we employ screening strategies and multiscale simulations combining first-principles methods and device-scale continuum models to identify two novel element-mixed compounds, Rb2AgInCl3I3and Cs2AgInCl3I3, as promising candidates for photovoltaic applications. These compounds exhibit favorable structural factors and suitable direct band gaps. Theoretical investigations using first-principles methods with the HSE06 functional reveal direct band gaps of 0.98 and 1.26 eV for Rb2AgInCl3I3and Cs2AgInCl3I3, respectively, with corresponding optical absorption coefficients exceeding 105cm–1in the visible light range. Cs2AgInCl3I3features high charge mobilities of approximately 20 cm2·V–1·s–1and a notable single-junction spectroscopic limited maximum efficiency (SLME) of 25.54%. Further analysis using the device-scale continuum model simulated the nonradiative recombination effects on power conversion efficiency, integrating quantum-mechanically calculated optoelectronic properties. These theoretical investigations, which bridge composition engineering with multiscale simulations, provide valuable insights into screening novel, lead-free, halogen-mixed double metal perovskite optoelectronic devices, highlighting their potential for high-performance solar energy applications.