The ordered coassembly of mixed-dimensional species—such as zero-dimensional (0D) nanocrystals and 2D microscale nanosheets—is commonly deemed impracticable, as phase separation almost invariably occurs. Here, by manipulating the ligand grafting density, we achieve ordered coassembly of 0D nanocrystals and 2D nanosheets under standard solvent evaporation conditions, resulting in macroscopic, freestanding hybrid-dimensional superlattices with both out-of-plane and in-plane order. The key to suppressing the notorious phase separation lies in hydrophobizing nanosheets with molecular ligands identical to those of nanocrystals but having substantially lower grafting density. The mismatched ligand density endows the two mixed-dimensional components with a molecular recognition-like capability, driving the spontaneous organization of densely capped nanocrystals at the interlayers of sparsely grafted nanosheets. Theoretical calculations reveal that the intercalation of nanocrystals can substantially reduce the short-range repulsions of ligand-grafted nanosheets and is therefore energetically favorable, while subsequent ligand-ligand van der Waals attractions induce the in-plane order and kinetically stabilize the laminate superlattice structure.
Statistical power is important for genetically informed research, especially when using publicly available datasets. Such datasets can make research conclusions more generalizable, but accurate records of zygosity are not always obtainable. Some researchers tend to fit models with other kin pairs rather than MZ and DZ twins, who have a less than .5 genetic relatedness difference (ΔR). However, no research has systematically investigated the impact of using such two groups of kin pairs on ACE model performance. In our study, we did mathematical derivations and simulations to illustrate how genetic relatedness of same-sex twins (RSS) and sample sizes influence ACE model performance. Specifically, we analyzed those factors’ impact on statistical power of heritability (h2) estimation, the overall power, and the frequency of negative estimates based on univariate ACE models. Our algebraic and simulation results suggest that heritability power, overall power, and reduction of negative estimates are positively associated with larger RSS and larger sample sizes. We also found addressing sex limitations would cause slightly worse model performance under most circumstances. Simulation results were discussed from both statistical and empirical perspectives, and suggestions are proposed for studies using kin pairs with ΔR < .5.
The lifespan of proton‐exchange membrane fuel cells heavily relies on the durability of the carbon support of cathode catalysts. However, commercial carbon supports like ketjenblack (KB) and Vulcan carbon (VC) face the challenge of balancing porosity, surface area, and electrochemical stability. To address this issue, a 3D porous wrinkled graphitic carbon (PWGC) is designed and synthesized using a catalyst‐free, plasma‐enhanced chemical vapor deposition approach. The resulting PWGC possesses a hierarchically porous structure with a high surface area, a high degree of graphitization, and exceptional corrosion resistance. As a result, the Pt/PWGC catalysts with the use of PWGC as the carbon support demonstrate superior high potential stability compared to those made with KB and VC as the carbon support. Additionally, a sacrificial layer strategy is introduced to further reduce PWGC corrosion, resulting in Pt@C/PWGC catalysts that show significantly improved durability in membrane electrode assembly tests. After 5K voltage cycles from 1.0 to 1.5 V, the retention of electrochemically active surface area approaches 56.8%, surpassing the 23.6% retention of commercial Pt/C catalysts tested under the same conditions.
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