In Situ Observation of Initial Stage in Dielectric Growth and Deposition of Ultrahigh Nucleation Density Dielectric on Two-Dimensional Surfaces.

Several proposed beyond-CMOS devices based on two-dimensional (2D) heterostructures require the deposition of thin dielectrics between 2D layers. However, the direct deposition of dielectrics on 2D materials is challenging due to their inert surface chemistry. To deposit high-quality, thin dielectrics on 2D materials, a flat lying titanyl phthalocyanine (TiOPc) monolayer, deposited via the molecular beam epitaxy, was employed to create a seed layer for atomic layer deposition (ALD) on 2D materials, and the initial stage of growth was probed using in situ STM. ALD pulses of trimethyl aluminum (TMA) and H2O resulted in the uniform deposition of AlOx on the TiOPc/HOPG. The uniformity of the dielectric is consistent with DFT calculations showing multiple reaction sites are available on the TiOPc molecule for reaction with TMA. Capacitors prepared with 50 cycles of AlOx on TiOPc/graphene display a capacitance greater than 1000 nF/cm(2), and dual-gated devices have current densities of 10(-7)A/cm(2) with 40 cycles.

A global land cover training dataset from 1984 to 2020.

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.