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Developing of economic and efficient catalysts is critical for the application of electroreduction of carbon dioxide to highly valuable chemicals. Herein, we present a facile method to synthesize N-doped hieratically porous carbon through pyrolysis of petroleum pitch followed by ammonia etching. We found mesopores are favored formation by removing of asphaltene from petroleum pitch during the carbonation process. Simultaneously, ammonia etching can not only increase the pyridinic-N content, but also upgrade the ratio of meso- to micro- pores of carbon materials. Using the N-doped hieratically porous carbon as catalyst for carbon dioxide electroreduction, the Faradaic efficiency of carbon monoxide reaches 83% at −0.9 V vs. the reversible hydrogen electrode (RHE) in 0.1 M KHCO3. This superior performance is attributed to the synergistic effects of highly pyridinic-N content in conjunction with the hieratically porous architecture, rendering abundant exposed and accessible active sites for electroreduction of CO2. Our work provides a new strategy for the large-scale preparation of high-performance, low-cost catalysts for CO2 electroreduction.

The rising demand for high-performance energy storage devices calls for cost-effective electrode materials with large working potential windows, high specific capacities, and rapid rate capability. Herein, we propose a facile molten-salt-mediated production of ultrathin carbon nanosheets with tunable nitrogen doping using low-cost precursors including asphalt and melamine. The as-obtained nitrogen doped slim carbon nanosheets (NSCN) constructed through this easy and effective in-situ method offer both high specific capacity and excellent cycling stability. Specifically, a specific capacity of 848 mAh g−1 after 800 cycles at 1 A g−1 and 214 mAh g−1 after 1500 cycles at 1 A g−1 were achieved for lithium and potassium storage, respectively. The excellent electrochemical performances benefit from the synergistic effect of structural merits including ultra-thin nanosheet, nitrogen doping, high disorder degree and large specific surface area. Meanwhile, it also provides an avenue for realizing the value-added utilization of heavy oil in an alternative field.

As an effective waste heat recovery technique, the hydrocarbon-CO2 mixture based Rankine cycle is always operating on a specific temperature, which has a noticeable influence on flammability limits. Thus it is critical to research flammability limit of hydrocarbon-CO2 at various initial temperatures. Focusing on the hydrocarbon-CO2 mixture, this paper first measured its flammability limits at different initial temperatures using an experimental setup based on the ASTM E681-09. Results have shown that lower flammability limit decreases with the elevating initial temperatures while upper flammability limit increases, therefore the flammable zone expands leading to higher potential dangers. Moreover the expansion values of flammable zone from 30 °C to 70 °C have decreased by nearly 0.3 vol% with the dilute concentration changing from 0 to 0.9. A linear relationship between temperature and flammability limits of hydrocarbon-CO2 mixture has been proposed based on thermal theory and verified with experimental values. At the same time, a comprehensive method based on calculated adiabatic flame temperature theory was presented to predict the flammability limit at different temperatures with higher accuracy. The mean relative deviation between the predicted results and observed ones is 2.76% at upper flammability limit and 1.71% at lower flammability limit in the temperature range examined.

A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach is proposed to allocate the defense resource in road dangerous goods transportation network. Firstly, the weighted physical network without direction and its weighted service network with direction are established. The Self-Contained Girvan-Newman Algorithm is applied to separate the whole weighted physical network into several communities. Next, based on the service network, the covariance matrix of each separated community is established, the Mean Variance Model is used to allocate the defense resource for each community, which focuses on selecting the option with the lowest probability of loss caused by the dangerous goods transportation accident/risk. The case study is conducted by using the road network and the dangerous goods transportation volume of Dalian, China as the background. When the whole network is separated into 6 communities, the defense resource capability in the whole network is the best. The Power Function Allocation Model (PFAM) is applied as the comparison approach, the overall rescue capability of the network is defined and applied to evaluate the defense resource allocation schemes. The results show that, the approach proposed in this paper has better effectiveness than PFAM, especially when the whole network is separated into 6 communities.

Aims N6-methyladenosine (m6A) is the most prevalent internal chemical RNA modification in mammal mRNAs. Accumulating evidence has shown the critical role of m6A in cardiovascular diseases including cardiac hypertrophy, heart failure, ischemic heart disease, vascular calcification, restenosis, and aortic aneurysm. However, whether m6A participates in the occurrence and development of hypoxic pulmonary hypertension (HPH) remains largely unknown. The present study aims to explore the role of key transferase METTL3, in the development of HPH. Materials and methods Pulmonary artery smooth muscle cells (PASMCs) and hypoxic rat models were used to research the METTL3-mediated m6A in HPH. EdU, transwell and TUNEL were performed to evaluate the proliferation, migration and apoptosis rates. m6A RNA Methylation Quantification Kit and m6A-qPCR were utilized to measure the total m6A level and m6A level of PTEN mRNA. RNA immunoprecipitation was used to detect the interaction between METTL3 and PTEN mRNA. Key findings Both METTL3 mRNA and protein were found abnormally upregulated in vivo and in vitro. Furthermore, downregulation of METTL3 attenuated PASMCs proliferation and migration. In addition, m6A binding protein YTHDF2 was found significantly increased in PASMCs under hypoxia. YTHDF2 recognized METTL3 mediated m6A modified PTEN mRNA and promoted the degradation of PTEN. Decreased PTEN led to over-proliferation of PASMCs through activation of PI3K/Akt signaling pathway. Significance METTL3/YTHDF2/PTEN axis exerts a significant role in hypoxia induced PASMCs proliferation, providing a novel therapeutic target for HPH.

Vocal folds are soft laryngeal connective tissues with distinct layered structures and complex multicomponent matrix compositions that endow phonatory and respiratory functions. This delicate tissue is easily damaged by various environmental factors and pathological conditions, altering vocal biomechanics and causing debilitating vocal disorders that detrimentally affect the daily lives of suffering individuals. Modern techniques and advanced knowledge of regenerative medicine have led to a deeper understanding of the microstructure, microphysiology, and micropathophysiology of vocal fold tissues. State-of-the-art materials ranging from extracecullar-matrix (ECM)-derived biomaterials to synthetic polymer scaffolds have been proposed for the prevention and treatment of voice disorders including vocal fold scarring and fibrosis. This review intends to provide a thorough overview of current achievements in the field of vocal fold tissue engineering, including the fabrication of injectable biomaterials to mimic in vitro cell microenvironments, novel designs of bioreactors that capture in vivo tissue biomechanics, and establishment of various animal models to characterize the in vivo biocompatibility of these materials. The combination of polymeric scaffolds, cell transplantation, biomechanical stimulation, and delivery of antifibrotic growth factors will lead to successful restoration of functional vocal folds and improved vocal recovery in animal models, facilitating the application of these materials and related methodologies in clinical practice.

In this paper, an Interpretive Structural Modeling (ISM) and Bayesian Network (BN) combining approach is applied to analyze the relationships and interaction strengths among the risk factors or accident causes of railway dangerous goods transportation system (RDGTS), quantitatively. According to the statistical data, 17 sub-indicators are concluded and applied as the nodes in BN. Based on the 17 sub-indicators and with the help of experts, the combined ISM and BN approach is used to analyze the system risk with seven steps: establish the relation matrix, calculate the reachability matrix, divide the reachability matrix into different levels, form the directed graph, form the Bayesian Network, establish the prior marginal and conditional probabilities of the sub-indicators in the BN, causal reasoning for the BN and obtain the final probabilities of occurrence for all sub-indicators. The conditional probability tables (CPTs) are used to express the relationships and interaction strengths among the nodes and the parent node variables. The final analysis results show that the sub-indicator, staffs are lack of technical and knowledge during transportation processes, has the highest impact to the RDGTS with probability 0.74, and the sudden natural disaster has the weakest impact to the RDGTS with probability 0.05.

Co-containing zeolitic imidazolate framework material (Co-ZIF) was prepared and its catalytic performance in the aerobic epoxidation of olefins using isobutyraldehyde as reductant under mild conditions was first studied. Co-ZIF was characterized by XRD, FT-IR and X-ray single-crystal diffraction. It showed good performance in the epoxidation of olefins, with 100% conversion, 98.5% selectivity and 638.3 turnover frequency for the epoxidation of cyclooctene. Co-ZIF could be reused for 5 times without loss of its catalytic activity and the structure of the recovered catalyst was almost unchanged compared to that of the fresh one.

MRI has considerable potential as a non-destructive probe of porous media, permitting rapid quantification of local fluid content and the possibility of local flow visualization and quantification. In this work we explore a general approach to flow velocity measurement in porous media by combining Cotts pulsed field gradient flow encoding with SPRITE MRI. This technique permits facile and accurate flow and dispersion coefficient mapping of fluids in porous media. This new approach has proven to be robust in characterizing fluid behavior. This method is illustrated through measurements of flow in pipes, flow in sand packs and flow in porous reservoir rocks. Spatially resolved flow maps and local fluid velocity distribution were acquired.