Unexpected Reconstruction of the alpha-Boron (111) Surface

We report on a novel reconstruction of the alpha-boron (111) surface, discovered using an ab initio evolution structure search, and reveal that it has an unexpected neat structure and much lower surface energy than the recently proposed (111)-I_R,(a) surface. For this reconstruction, every single interstitial boron atom forms bridges with the unique polar-covalent bonds between neighboring B_12 icosahedra, which perfectly meet the electron counting rule and are responsible for the reconstruction-induced metal-semiconductor transition. The peculiar charge transfer between the interstitial atoms and the icosahedra plays an important role in stabilizing the surface.Comment: [email protected];[email protected]

How do chemical properties of the atoms change under pressure

Abundant evidence has shown the emergence of dramatic new chemical phenomena under pressure, including the formation of unexpected crystal structures and completely new counterintuitive compounds. In many cases, there is no convincing explanation for these phenomena and there are virtually no chemical rules or models capable of predicting or even rationalizing these phenomena. Here we consider two central chemical properties of atoms, electronegativity and chemical hardness, and determine them as a function of pressure up to 500 GPa. For elements without orbital transfer at high pressure, electronegativity first increases and then decreases, while chemical hardness monotonically decreases as pressure increases. For some active metals, the chemical hardness has a further increase at pressures of the order of tens-hundreds of gigapascals. Furthermore, we discover that orbital transfer, in particular s-d transfer, makes Ni a "pseudo-noble-gas", Fe and Co strong electron acceptors, while Cu and Zn become active metals. We show the explicative and predictive power of our electronegativity and chemical hardness scales under pressure

Magnetic Borophenes from an Evolutionary Search

A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson\u27s ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously

Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results with high-quality maps and high efficiency, we propose a novel Memory-aided Contrastive Consensus Learning (MCCL) framework, which is capable of effectively detecting co-salient objects in real time (~150 fps). To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories. Finally, to improve the quality and integrity of the predicted maps, we develop an Adversarial Integrity Learning (AIL) strategy to make the segmented regions more likely composed of complete objects with less surrounding noise. Extensive experiments on all the latest CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models, achieving the new state of the art (~5.9% and ~6.2% improvement in S-measure on CoSOD3k and CoSal2015, respectively). Our source codes, saliency maps, and online demos are publicly available at https://github.com/ZhengPeng7/MCCL.Comment: AAAI 202

The Distribution of Transcutaneous CO2 Emission and Correlation With the Points Along the Pericardium Meridian

AbstractThis study aimed to understand energy metabolism distribution along the pericardium meridian and verify the correlation between the body surface (points), and classic meridian theory. A highly sensitive CO2 instrument was used to measure the transcutaneous CO2 emission at 13 points along the pericardium meridian line (12 points on the line and one point beyond the line) and 13 control points beside them. Results showed that the distribution of transcutaneous CO2 emission is highly related to the position on the body. Transcutaneous CO2 emission is significantly higher at P7 and P3, than the control points beside them. The points along the meridian and the points beside them were clustered with relative distance by SAS statistics software. Two distance matrixes were then obtained. The correlation coefficients between the points along the line and between the control points were calculated. The results showed that the 13th point beyond the line was far from the 12 points on the line (distance, 0.24), while acupoints on the line clustered earlier when compared with the non-acupoints. The average correlation coefficients among the acu-points was 0.65 which was significantly higher than 0.56, among the non-acupoints. No such characteristics were found among the control points. It was concluded that there is a strong correlativity of energy metabolism activity between the body surfaces along the meridian, and an even stronger correlativity between the acupoints on the meridian