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Two-dimensional (2D) superlattices, formed by stacking sublattices of 2D materials, have emerged as a powerful platform for tailoring and enhancing material properties beyond their intrinsic characteristics. However, conventional synthesis methods are limited to pristine 2D material sublattices, posing a significant practical challenge when it comes to stacking chemically modified sublattices. Here we report a chemical synthesis method that overcomes this challenge by creating a unique 2D graphene superlattice, stacking graphene sublattices with monodisperse, nanometer-sized, square-shaped pores and strategically doped elements at the pore edges. The resulting graphene superlattice exhibits remarkable correlations between quantum phases at both the electron and phonon levels, leading to diverse functionalities, such as electromagnetic shielding, energy harvesting, optoelectronics, and thermoelectrics. Overall, our findings not only provide chemical design principles for synthesizing and understanding functional 2D superlattices but also expand their enhanced functionality and extensive application potential compared to their pristine counterparts.

 

Various forms of ecological monitoring and disease diagnosis rely upon the detection of amphiphiles, including lipids, lipopolysaccharides, and lipoproteins, at ultralow concentrations in small droplets. Although assays based on droplets’ wettability provide promising options in some cases, their reliance on the measurements of surface and bulk properties of whole droplets (e.g., contact angles, surface tensions) makes it difficult to monitor trace amounts of these amphiphiles within small-volume samples. Here, we report a design principle in which self-assembled monolayer–functionalized microstructured surfaces coated with silicone oil create locally disordered regions within a droplet’s contact lines to effectively concentrate amphiphiles within the areas that dominate the droplet static friction. Remarkably, such surfaces enable the ultrasensitive, naked-eye detection of amphiphiles through changes in the droplets’ sliding angles, even when the concentration is four to five orders of magnitude below their critical micelle concentration. We develop a thermodynamic model to explain the partitioning of amphiphiles at the contact line by their cooperative association within the disordered, loosely packed regions of the self-assembled monolayer. Based on this local analyte concentrating effect, we showcase laboratory-on-a-chip surfaces with positionally dependent pinning forces capable of both detecting industrially and biologically relevant amphiphiles (e.g., bacterial endotoxins), as well as sorting aqueous droplets into discrete groups based on their amphiphile concentrations. Furthermore, we demonstrate that the sliding behavior of amphiphile-laden aqueous droplets provides insight into the amphiphile’s effective length, thereby allowing these surfaces to discriminate between analytes with highly disparate molecular sizes.

 

There has been a growing interest in incorporating auxiliary summary information from external studies into the analysis of internal individual‐level data. In this paper, we propose an adaptive estimation procedure for an additive risk model to integrate auxiliary subgroup survival information via a penalized method of moments technique. Our approach can accommodate information from heterogeneous data. Parameters to quantify the magnitude of potential incomparability between internal data and external auxiliary information are introduced in our framework while nonzero components of these parameters suggest a violation of the homogeneity assumption. We further develop an efficient computational algorithm to solve the numerical optimization problem by profiling out the nuisance parameters. In an asymptotic sense, our method can be as efficient as if all the incomparable auxiliary information is accurately acknowledged and has been automatically excluded from consideration. The asymptotic normality of the proposed estimator of the regression coefficients is established, with an explicit formula for the asymptotic variance‐covariance matrix that can be consistently estimated from the data. Simulation studies show that the proposed method yields a substantial gain in statistical efficiency over the conventional method using the internal data only, and reduces estimation biases when the given auxiliary survival information is incomparable. We illustrate the proposed method with a lung cancer survival study.

 

Asymmetric interactions such as entropic (e.g., encoded by nonspherical shapes) or surface forces (e.g., encoded by patterned surface chemistry or DNA hybridization) provide access to functional states of colloidal matter, but versatile approaches for engineering asymmetric van der Waals interactions have the potential to expand further the palette of materials that can be assembled through such bottom-up processes. We show that polymerization of liquid crystal (LC) emulsions leads to compositionally homogeneous and spherical microparticles that encode van der Waals interactions with complex symmetries (e.g., quadrupolar and dipolar) that reflect the internal organization of the LC. Experiments performed using kinetically controlled probe colloid adsorption and complementary calculations support our conclusion that LC ordering can program van der Waals interactions by ~20 k B T across the surfaces of microparticles. Because diverse LC configurations can be engineered by confinement, these results provide fresh ideas for programming van der Waals interactions for assembly of soft matter.