The computer code for the approach developed is available at Penn State Center for Statistical Genetics web site, http://statgen.psu.edu.

Hexagonal boron nitride (h-BN) has been predicted to exhibit an in-plane thermal conductivity as high as~550 W m −1 K −1 at room temperature, making it a promising thermal management material. However, current experimental results (220-420 W m −1 K −1) have been well below the prediction. Here, we report on the modulation of h-BN thermal conductivity by controlling the B isotope concentration. For monoisotopic 10 B h-BN, an in-plane thermal conductivity as high as 585 W m −1 K −1 is measured at room temperature,~80% higher than that of h-BN with a disordered isotope concentration (52%:48% mixture of 10 B and 11 B). The temperature-dependent thermal conductivities of monoisotopic h-BN agree well with first principles calculations including only intrinsic phonon-phonon scattering. Our results illustrate the potential to achieve high thermal conductivity in h-BN and control its thermal conductivity, opening avenues for the wide application of h-BN as a next-generation thin-film material for thermal management, metamaterials and metadevices.

Hexagonal boron nitride (hBN) with a single boron isotope have many enhanced physical, thermal and optical properties compared to the most common hBN with the natural distribution of boron (19.9 at. % 10B and 80.1 at. % 11B). These property differences can significantly improve the device performance in applications, such as neutron detectors, nanoscale electronics, and optical components. In this study, a new method for the growth of large-scale, high-quality monoisotopic hBN single crystals, i.e., h10BN and h11BN, was developed. hBN single crystals were grown using a nickel–chromium solvent and pure boron and nitrogen sources at atmospheric pressure. The clear and colorless crystals have a maximum domain size of around 1 mm. Raman measurements demonstrate that the crystals produced with this method are pure hBN phase with low defect density, and the spectral peaks vary with the boron isotope concentrations. X-ray photoelectron spectroscopy spectra show that the B–N bond in h11BN is slightly stronger than that in h10BN. The ability to produce crystals in this manner opens the door to isotopically engineering the properties and performance of hBN devices.

Polaritons formed by the coupling of light and material excitations enable light-matter interactions at the nanoscale beyond what is currently possible with conventional optics. However, novel techniques are required to control the propagation of polaritons at the nanoscale and to implement the first practical devices. Here we report the experimental realization of polariton refractive and meta-optics in the mid-infrared by exploiting the properties of low-loss phonon polaritons in isotopically pure hexagonal boron nitride interacting with the surrounding dielectric environment comprising the low-loss phase change material Ge3Sb2Te6. We demonstrate rewritable waveguides, refractive optical elements such as lenses, prisms, and metalenses, which allow for polariton wavefront engineering and sub-wavelength focusing. This method will enable the realization of programmable miniaturized integrated optoelectronic devices and on-demand biosensors based on high quality phonon resonators.

Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or fGWAS. Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. The fGWAS model, equipped with Bayesian group lassso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.