Electronic Properties of Graphene Nanoribbon on Si(001) Substrate

We show by first-principles calculations that the electronic properties of zigzag graphene nanoribbons (Z-GNRs) adsorbed on Si(001) substrate strongly depend on ribbon width and adsorption orientation. Only narrow Z-GNRs with even rows of zigzag chains across their width adsorbed perpendicularly to the Si dimer rows possess an energy gap, while wider Z-GNRs are metallic due to width-dependent interface hybridization. The Z-GNRs can be metastably adsorbed parallel to the Si dimer rows, but show uniform metallic nature independent of ribbon width due to adsorption induced dangling-bond states on the Si surface.Comment: 13 pages, 3 figure

The rheological properties of shear thickening fluid reinforced with SiC nanowires

The rheological properties of shear thickening fluid (STF) reinforced with SiC nanowires were investigated in this paper. Pure STF consists of 56 vol% silica nano-particles and polyethylene glycol 400 (PEG 400) solvent was fabricated; and a specific amount of SiC nanowires were dispersed into this pure STF, and then the volume fraction of PEG400 was adjusted to maintain the volume fraction of solid phase in the STF at a constant of 56%. The results showed there was almost 30% increase in the initial and shear thickening viscosity of the STF reinforced with SiC nanowires compared to the pure STF. Combining with the hydrodynamic cluster theory, the effect of the mechanism of SiC nanowire on the viscosity of STF was discussed, and based on the experimental results, an analytical model of viscosity was used to describe the rheological properties of STF, which agreed with the experimental results

A YOLOv7 incorporating the Adan optimizer based corn pests identification method

Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and neural networks are limited by high model training costs and low recognition accuracy. To address these problems, we proposed a YOLOv7 maize pests identification method incorporating the Adan optimizer. First, we selected three major corn pests, corn borer, armyworm and bollworm as research objects. Then, we collected and constructed a corn pests dataset by using data augmentation to address the problem of scarce corn pests data. Second, we chose the YOLOv7 network as the detection model, and we proposed to replace the original optimizer of YOLOv7 with the Adan optimizer for its high computational cost. The Adan optimizer can efficiently sense the surrounding gradient information in advance, allowing the model to escape sharp local minima. Thus, the robustness and accuracy of the model can be improved while significantly reducing the computing power. Finally, we did ablation experiments and compared the experiments with traditional methods and other common object detection networks. Theoretical analysis and experimental result show that the model incorporating with Adan optimizer only requires 1/2-2/3 of the computing power of the original network to obtain performance beyond that of the original network. The mAP@[.5:.95] (mean Average Precision) of the improved network reaches 96.69% and the precision reaches 99.95%. Meanwhile, the mAP@[.5:.95] was improved by 2.79%-11.83% compared to the original YOLOv7 and 41.98%-60.61% compared to other common object detection models. In complex natural scenes, our proposed method is not only time-efficient and has higher recognition accuracy, reaching the level of SOTA

Metagenomics Reveals Microbial Diversity and Metabolic Potentials of Seawater and Surface Sediment From a Hadal Biosphere at the Yap Trench

Hadal biosphere represents the deepest part of the ocean with water depth >6,000 m. Accumulating evidence suggests the existence of unique microbial communities dominated by heterotrophic processes in this environment. However, investigations of the microbial diversity and their metabolic potentials are limited because of technical constraints for sample collection. Here, we provide a detailed metagenomic analysis of three seawater samples at water depths 5,000–6,000 m below sea level (mbsl) and three surface sediment samples at water depths 4,435–6,578 mbsl at the Yap Trench of the western Pacific. Distinct microbial community compositions were observed with the dominance of Gammaproteobacteria in seawater and Thaumarchaeota in surface sediment. Comparative analysis of the genes involved in carbon, nitrogen and sulfur metabolisms revealed that heterotrophic processes (i.e., degradation of carbohydrates, hydrocarbons, and aromatics) are the most common microbial metabolisms in the seawater, while chemolithoautotrophic metabolisms such as ammonia oxidation with the HP/HB cycle for CO2 fixation probably dominated the surface sediment communities of the Yap Trench. Furthermore, abundant genes involved in stress response and metal resistance were both detected in the seawater and sediments, thus the enrichment of metal resistance genes is further hypothesized to be characteristic of the hadal microbial communities. Overall, this study sheds light on the metabolic versatility of microorganisms in the Yap Trench, their roles in carbon, nitrogen, and sulfur biogeochemical cycles, and how they have adapted to this unique hadal environment