Two-dimensional (2D) materials have emerged as promising candidates for various optoelectronic applications based on their diverse electronic properties, ranging from insulating to superconducting. However, cooperative phenomena such as ferroelectricity in the 2D limit have not been well explored. Here, we report room-temperature ferroelectricity in 2D CuInP2S6 (CIPS) with a transition temperature of ∼320 K. Switchable polarization is observed in thin CIPS of ∼4 nm. To demonstrate the potential of this 2D ferroelectric material, we prepare a van der Waals (vdW) ferroelectric diode formed by CIPS/Si heterostructure, which shows good memory behaviour with on/off ratio of ∼100. The addition of ferroelectricity to the 2D family opens up possibilities for numerous novel applications, including sensors, actuators, non-volatile memory devices, and various vdW heterostructures based on 2D ferroelectricity.

[1] This study proposes a Bayesian model averaging (BMA) method to address parameter estimation uncertainty arising from nonuniqueness in parameterization methods. BMA is able to incorporate multiple parameterization methods for prediction through the law of total probability and to obtain an ensemble average of hydraulic conductivity estimates. Two major issues in applying BMA to hydraulic conductivity estimation are discussed. The first problem is using Occam's window in usual BMA applications to measure approximated posterior model probabilities. Occam's window only accepts models in a very narrow range, tending to single out the best method and discard other good methods. We propose a variance window to replace Occam's window to cope with this problem. The second problem is the Kashyap information criterion (KIC) in the approximated posterior model probabilities, which tends to prefer highly uncertain parameterization methods by considering the Fisher information matrix. With sufficient amounts of observation data, the Bayesian information criterion (BIC) is a good approximation and is able to avoid controversial results from using KIC. This study adopts multiple generalized parameterization (GP) methods such as the BMA models to estimate spatially correlated hydraulic conductivity. Numerical examples illustrate the issues of using KIC and Occam's window and show the advantages of using BIC and the variance window in BMA application. Finally, we apply BMA to the hydraulic conductivity estimation of the ''1500-foot'' sand in East Baton Rouge Parish, Louisiana.Citation: Tsai, F. T.-C., and X. Li (2008), Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window, Water Resour. Res., 44, W09434,

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Nonoxidative coupling of methane (NOCM) is a highly important process to simultaneously produce multicarbons and hydrogen. Although oxide-based photocatalysis opens opportunities for NOCM at mild condition, it suffers from unsatisfying selectivity and durability, due to overoxidation of CH4 with lattice oxygen. Here, we propose a heteroatom engineering strategy for highly active, selective and durable photocatalytic NOCM. Demonstrated by commonly used TiO2 photocatalyst, construction of Pd–O4 in surface reduces contribution of O sites to valence band, overcoming the limitations. In contrast to state of the art, 94.3% selectivity is achieved for C2H6 production at 0.91 mmol g–1 h–1 along with stoichiometric H2 production, approaching the level of thermocatalysis at relatively mild condition. As a benchmark, apparent quantum efficiency reaches 3.05% at 350 nm. Further elemental doping can elevate durability over 24 h by stabilizing lattice oxygen. This work provides new insights for high-performance photocatalytic NOCM by atomic engineering.