Translating XPS Measurement Procedure for Band Alignment into Reliable Ab-initio Calculation Method

Band alignment between solids is a crucial issue in condensed matter physics and electronic devices. Although the XPS method has been used as a routine method for determination of the band alignment, the theoretical calculations by copying the XPS band alignment procedure usually fail to match the measured results. In this work, a reliable ab-initio calculation method for band alignment is proposed on the basis of the XPS procedure and in consideration of surface polarity and lattice deformation. Application of our method to anatase and rutile TiO2 shows well agreement between calculation and experiment. Furthermore, our method can produce two types of band alignment: the coupled and the intrinsic, depending on whether the solid/solid interface effect is involved or not. The coupled and intrinsic band alignments correspond to alignments measured by XPS and electrochemical impedance analysis, respectively, explaining why band alignments reported by these two experiments are rather inconsistent

Zoonosis at the Huanan Seafood Market: A Critique

Since the Hunan Seafood Market (HSM) in Wuhan, China was first suggested as the source of the COVID-19 pandemic in late January 2020, debate has continued over the evidence supporting this claim. Here, we assess the evidence provided in support of zoonotic spillover at the HSM as the origin of human infection of SARS-CoV-2. We find that the datasets and analyses put forward in support of zoonosis are biased, and lack sufficient verifiable data to support this hypothesis. The earliest COVID-19 case at the HSM was not at or near a wildlife stall, the distribution of cases at the HSM is consistent with a Poisson point process model (randomly distributed) and the distribution of wildlife stalls and COVID-19 cases are consistent with independent Poisson point processes. No statistical correlation is found between cases and wildlife stall locations. The random distribution of cases and several isolated clusters is consistent with human-to-human transmission in shared areas such as eating areas, toilets and social gathering areas. Sampling bias is evident in specimen collection at the market, with over-sampling evident in the SW corner of the market relative to the rest of the market. Notwithstanding this bias, environmental positive PCR samples are more consistent with contamination by infected COVID-19 cases and aerosol spread from the HSM toilets, as compared with from wildlife stalls. Although proposed as the intermediate spillover host, raccoon dogs were unlikely to be linked with the outbreak, as they were sold in Wuhan in small numbers, and there is no epidemiological evidence indicating any infection of a raccoon dog, or any other wild or domestic animal, before or during the early pandemic, at any market elsewhere in Wuhan, or even in the rest of China. These considerations indicate that HSM was instead likely a superspreader location, with only tenuous evidence to support a zoonotic spillover there. Consequently, we conclude there is sufficient evidence to indicate the HSM as the source of the pandemic

Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection

Millimeter-wave (MMW) imaging is emerging as a promising technique for safe security inspection. It achieves a delicate balance between imaging resolution, penetrability and human safety, resulting in higher resolution compared to low-frequency microwave, stronger penetrability compared to visible light, and stronger safety compared to X ray. Despite of recent advance in the last decades, the high cost of requisite large-scale antenna array hinders widespread adoption of MMW imaging in practice. To tackle this challenge, we report a large-scale single-shot MMW imaging framework using sparse antenna array, achieving low-cost but high-fidelity security inspection under an interpretable learning scheme. We first collected extensive full-sampled MMW echoes to study the statistical ranking of each element in the large-scale array. These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, which realizes robust and accurate image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at the same sample sampling ratio. The performance of the reported technique presents higher than 50% superiority over the existing MMW imaging schemes on various metrics including precision, recall, and mAP50. With such strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging, and could advocate its further practical applications