BaFe2As2 Surface Domains and Domain Walls: Mirroring the Bulk Spin Structure

High-resolution scanning tunneling microscopy (STM) measurements on BaFe2As2-one of the parent compounds of the iron-based superconductors-reveals a (1x1) As-terminated unit cell on the (001) surface. However, there are significant differences of the surface unit cell compared to the bulk: only one of the two As atoms in the unit cell is imaged and domain walls between different (1x1) regions display a C2 symmetry at the surface. It should have been C2v if the STM image reflected the geometric structure of the surface or the orthorhombic bulk. The inequivalent As atoms and the bias dependence of the domain walls indicate that the origin of the STM image is primarily electronic not geometric. We argue that the surface electronic topography mirrors the bulk spin structure of BaFe2As2, via strong orbital-spin coupling

In situ reservoir biogeochemical evaluation and its indicative significance for coalbed methane extraction: Taking the Shizhuangnan Block in the southern Qinshui Basin as an example

Coal reservoirs are habitats for microorganisms, with some widespread communities linked to carbon cycling. The geographic distribution and metabolic expression of these microorganisms remain largely decoupled from the reservoir environment. Although microbial metabolism under natural conditions is insufficient for gas accumulation, the community influence from organic matter supply and environmental conditions can stimulate a positive response in the in-situ reservoir environment and coalbed methane storage. The southern Qinshui Basin is rich in coalbed methane resources and is one of the first areas in China to realize a commercial exploitation. Taking the Shizhuangnan block in the southern Qinshui Basin as an example, the geochemical characteristics of reservoir water and the biogeochemical sequencing of microbial genes are assessed by assessing ion concentrations, dissolved inorganic carbon isotopes, sulfate isotopes, and microbial abundance and diversity. The results show that the reservoir geochemistry is influenced by hydration, ion exchange, and microbial metabolism. High sodium and bicarbonate ion content indicate relatively reduced or stagnant conditions in the reservoir. Sulfate reducing bacteria and methanogens display synergy during coal degradation. However, sulfate reducing bacteria can outcompete methanogens for substrate when sulfate is sufficient for their metabolism, inhibiting methanogenesis. The sulfate isotopes, dissolved inorganic carbon isotopes, and microbial abundance and diversity may reflect the symbiotic relationship between these microorganisms and the reservoir environment. Regions with active sulfate reduction but weak methanogenesis generally do not provide suitable conditions for effective coalbed methane storage. Relatively reduced or stagnant reservoir environments favor methanogenesis and are beneficial for coalbed methane storage. This research enriches our methods for evaluating reservoir biogeochemistry, guides us in selecting favorable areas for coalbed methane exploration and development, and provides a theoretical basis and guidance for the practical implementation of coalbed methane bioengineering

Surface Geometric and Electronic Structure of BaFe2As2(001)

BaFe2As2 exhibits properties characteristic of the parent compounds of the newly discovered iron (Fe)-based high-TC superconductors. By combining the real space imaging of scanning tunneling microscopy/spectroscopy (STM/S) with momentum space quantitative Low Energy Electron Diffraction (LEED) we have identified the surface plane of cleaved BaFe2As2 crystals as the As terminated Fe-As layer - the plane where superconductivity occurs. LEED and STM/S data on the BaFe2As2(001) surface indicate an ordered arsenic (As) - terminated metallic surface without reconstruction or lattice distortion. It is surprising that the STM images the different Fe-As orbitals associated with the orthorhombic structure, not the As atoms in the surface plane.Comment: 12 pages, 4 figure

Anthropogenic influences on extremely persistent seasonal precipitation in Southern China during May–June 2022

Precipitation in southern China during April–June 2022 was the highest since 1961. Anthropogenic forcing has reduced the probability of 2022-like Rx30day precipitation by about 45% based on CMIP6 simulations

Total Organic Carbon Enrichment and Its Impact on Pore Characteristics: A Case Study from the Niutitang Formation Shales in Northern Guizhou

This study analyzes samples from the Lower Cambrian Niutitang Formation in northern Guizhou Province to enable a better understanding of total organic carbon (TOC) enrichment and its impact on the pore characteristics of over-mature marine shale. Organic geochemical analysis, X-ray diffraction, scanning electron microscopy, helium porosity, and low-temperature nitrogen adsorption experiments were conducted on shale samples. Their original TOC (TOCo) content and organic porosity were estimated by theoretical calculation, and fractal dimension D was computed with the fractal Frenkel–Halsey–Hill model. The results were then used to consider which factors control TOC enrichment and pore characteristics. The samples are shown to be dominated by type-I kerogen with a TOC content of 0.29–9.36% and an equivalent vitrinite reflectance value of 1.72–2.72%. The TOCo content varies between 0.64% and 18.17%, and the overall recovery coefficient for the Niutitang Formation was 2.16. Total porosity of the samples ranged between 0.36% and 6.93%. TOC content directly controls porosity when TOC content lies in the range 1.0% to 6.0%. For samples with TOC < 1.0% and TOC > 6.0%, inorganic pores are the main contributors to porosity. Additionally, pore structure parameters show no obvious trends with TOC, quartz, and clay mineral content. The fractal dimension D1 is between 2.619 and 2.716, and D2 is between 2.680 and 2.854, illustrating significant pore surface roughness and structural heterogeneity. No single constituent had a dominant effect on the fractal characteristics

Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution