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Incorporating conductive and porous components as pillaring materials into the interlayers of two‐dimensional (2D) materials offers a solution to the restacking issue and enables the creation of multifunctional hetero‐superstructures. Here, multilayer MXene superlattices intercalated with monolayer mesoporous carbon frameworks (MMCFs) are synthesized by colloidal co‐assembly of MXene Ti3C2Tx nanosheets and Fe3O4 nanoparticles. The intercalated MMCFs not only increase interlayer spacing and create porous channels for fast mass transport but also act as conductive pillars to facilitate electron transfer along the z‐direction. These unique structural features allow for the full utilization of unilamellar MXene, making the resulting Ti3C2Tx/MMCF superlattices particularly suitable for capacitive energy storage in organic electrolytes containing bulky ions. As a demonstration, supercapacitors made from Ti3C2Tx/MMCFs exhibit a volumetric capacitance of 317 F cm−3 in a tetraethylammonium tetrafluoroborate/propylene carbonate electrolyte. Furthermore, on‐chip micro‐supercapacitors (MSCs) fabricated from Ti3C2Tx/MMCFs, using the ionic liquid 1‐ethyl‐3‐methylimidazolium tetrafluoroborate as an electrolyte, achieve an areal energy density of 0.10 mWh cm−2, surpassing that of most state‐of‐the‐art MXene‐based MSCs developed to date. This study not only highlights the significant potential of Ti3C2Tx/MMCFs for efficient capacitive energy storage in organic electrolytes but also introduces a new method for synthesizing 2D porous hetero‐superstructures for various applications. [ABSTRACT FROM AUTHOR]

The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher's (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to.95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than.5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research. [ABSTRACT FROM AUTHOR]