Life Cycle Assessment of Large-scale Compressed Bio-natural Gas Production in China: A Case Study on Manure Co-digestion with Corn Stover

Compressed bio-natural gas (CBG) production from large-scale systems has been recognized as promising because of the abundance of manure and crop residue feedstocks and its environmental friendliness. This study is a life cycle assessment using the local database of an operating large-scale CBG system of manure co-digestion with corn stover in China and eBalance software. The results showed that the system’s Primary Energy Input to Output (PEIO) ratio was 20%. Its anaerobic digestion process was the main contributor to energy consumption, accounting for 76%. Among the six environmental impacts investigated in this study, the global warming potential (GWP) was the major environmental impact, and the digestate effluent management process was the main contributor to the GWP, accounting for 60%. The mitigation potential of the system, compared with reference case for GWP, was 3.19 kg CO2-eq for 1 m3 CBG production. In the future, the GWP mitigation could be 479 × 106 metric tons CO2-eq with 150 × 109 m3 yr−1 CBG production from the entire China. This study provides a reference on large-scale CBG production system for establishing a localized life cycle assessment inventory database in China

V2C MXene-modified g-C3N4 for enhanced visible-light photocatalytic activity

Increasing the efficiency of charge transfer and separation efficiency of photogenerated carriers are still the main challenges in the field of semiconductor-based photocatalysts. Herein, we synthesized g-C3N4@V2C MXene photocatalyst by modifying g-C3N4 using V2C MXene. The prepared photocatalyst exhibited outstanding photocatalytic performance under visible light. The degradation efficiency of methyl orange by g-C3N4@V2C MXene photocatalyst was as high as 94.5%, which is 1.56 times higher than that by g-C3N4. This was attributed to the V2C MXene inhibiting the rapid recombination of photogenerated carriers and facilitating rapid transfer of photogenerated electrons (e) from g-C3N4 to MXene. Moreover, g-C3N4@V2C MXene photocatalyst showed good cycling stability. The photocatalytic performance was higher than 85% after three cycles. Experiments to capture free radicals revealed that superoxide radicals (02) are the main contributors to the photocatalytic activity. Thus, the proposed g-C3N4@V2C MXene photocatalyst is a promising visible-light catalyst.Comment: 20 pages, 9 figure

Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning

DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semilocal approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system is non-trivial and usually requires computationally intensive and cumbersome first-principles calculations. In this Letter, we address this issue by building a machine learning (ML) model to predict material-specific U parameters only from the structural information. An ML model is trained for the Mn-O chemical system by calibrating their DFT+U electronic structures with the hybrid functional results of more than Mn-O 3000 structures. The model allows us to determine a reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no computational cost; yet the obtained U value is as good as that obtained from the conventional first-principles methods. Further analysis reveals that the U value is primarily determined by the local chemical structure, especially the bond lengths, and this property is well captured by the ML model developed in this work. This concept of the ML U model is universally applicable and can considerably ease the usage of the DFT+U method by providing structure-specific, readily accessible U values

Over-expression of eukaryotic translation initiation factor 4 gamma 1 correlates with tumor progression and poor prognosis in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>The aim of the present study was to analyze the expression of eukaryotic translation initiation factor 4 gamma 1 (<it>EIF4G1</it>) in nasopharyngeal carcinoma (NPC) and its correlation with clinicopathologic features, including patients' survival time.</p> <p>Methods</p> <p>Using real-time PCR, we detected the expression of <it>EIF4G1 </it>in normal nasopharyngeal tissues, immortalized nasopharyngeal epithelial cell lines NP69, NPC tissues and cell lines. <it>EIF4G1 </it>protein expression in NPC tissues was examined using immunohistochemistry. Survival analysis was performed using Kaplan-Meier method. The effect of <it>EIF4G1 </it>on cell invasion and tumorigenesis were investigated.</p> <p>Results</p> <p>The expression levels of <it>EIF4G1 </it>mRNA were significantly greater in NPC tissues and cell lines than those in the normal nasopharyngeal tissues and NP69 cells (<it>P </it>< 0.001). Immunohistochemical analysis revealed that the expression of <it>EIF4G1 </it>protein was higher in NPC tissues than that in the nasopharyngeal tissues (<it>P </it>< 0.001). In addition, the levels of <it>EIF4G1 </it>protein in tumors were positively correlated with tumor T classification (<it>P </it>= 0.039), lymph node involvement (N classification, <it>P </it>= 0.008), and the clinical stages (<it>P </it>= 0.003) of NPC patients. Patients with higher <it>EIF4G</it>1 expression had shorter overall survival time (<it>P </it>= 0.019). Multivariate analysis showed that <it>EIF4G1 </it>expression was an independent prognostic indicator for the overall survival of NPC patients. Using shRNA to knock down the expression of <it>EIF4G1 </it>not only markedly inhibited cell cycle progression, proliferation, migration, invasion, and colony formation, but also dramatically suppressed <it>in vivo </it>xenograft tumor growth.</p> <p>Conclusion</p> <p>Our data suggest that <it>EIF4G1 </it>can serve as a biomarker for the prognosis of NPC patients.</p

Dual-constraint coarse-to-fine network for camouflaged object detection

Camouflaged object detection (COD) is an important yet challenging task, with great application values in industrial defect detection, medical care, etc. The challenges mainly come from the high intrinsic similarities between target objects and background. In this paper, inspired by the biological studies that object detection consists of two steps, i.e., search and identification, we propose a novel framework, named DCNet, for accurate COD. DCNet explores candidate objects and extra object-related edges through two constraints (object area and boundary) and detects camouflaged objects in a coarse-to-fine manner. Specifically, we first exploit an area-boundary decoder (ABD) to obtain initial region cues and boundary cues simultaneously by fusing multi-level features of the backbone. Then, an area search module (ASM) is embedded into each level of the backbone to adaptively search coarse regions of objects with the assistance of region cues from the ABD. After the ASM, an area refinement module (ARM) is utilized to identify fine regions of objects by fusing adjacent-level features with the guidance of boundary cues. Through the deep supervision strategy, DCNet can finally localize the camouflaged objects precisely. Extensive experiments on three benchmark COD datasets demonstrate that our DCNet is superior to 12 state-of-the-art COD methods. In addition, DCNet shows promising results on two COD-related tasks, i.e., industrial defect detection and polyp segmentation