Modulation of aluminum species in mordenite zeolite for enhanced dimethyl ether carbonylation

Dimethyl ether (DME) carbonylation is an important intermediate step in the synthesis of methyl acetate (MA) and ethanol. H-form mordenite (MOR) can efficiently catalyze the reaction, in which Brønsted acid sites (BASs) associated with framework Al function as active sites. But the role of other Al species such as exteraframework Al (EFAl) and framework-associated Al still remains unknown. In this study, we have proposed two convenient approaches for controlling the two Al species and investigating their influence on the DME carbonylation reaction. NH3-TPD and Py-IR analyses revealed that the number of BASs increased after the removal of EFAl and the inhibition the formation of framework-associated Al. The reactivity results showed that the elimination of EFAl promoted the DME conversion from 28% to 46%. Additionally, through the implementation of in-situ calcination to impede the presence of framework-associated Al, the DME conversion increased from 28% to 50%. With the understanding that both EFAl and framework-associated Al have a detrimental effect on the reaction, the highest conversion is achieved with these two treatments, leading to 73% DME conversion with 99% selectivity to MA. Our findings provide a systematical strategy to effectively regulate the presence of Al species in zeolite, offering insights of rational design to optimize zeolite catalysts for important industrialized process

Real-time phase-contrast flow cardiovascular magnetic resonance with low-rank modeling and parallel imaging

Background: Conventional phase-contrast cardiovascular magnetic resonance (PC-CMR) employs cine-based acquisitions to assess blood flow condition, in which electro-cardiogram (ECG) gating and respiration control are generally required. This often results in lower acquisition efficiency, and limited utility in the presence of cardiovascular pathology (e.g., cardiac arrhythmia). Real-time PC-CMR, without ECG gating and respiration control, is a promising alternative that could overcome limitations of the conventional approach. But real-time PC-CMR involves image reconstruction from highly undersampled (k, t)-space data, which is very challenging. In this study, we present a novel model-based imaging method to enable high-resolution real-time PC-CMR with sparse sampling. Methods: The proposed method captures spatiotemporal correlation among flow-compensated and flow-encoded image sequences with a novel low-rank model. The image reconstruction problem is then formulated as a low-rank matrix recovery problem. With proper temporal subspace modeling, it results in a convex optimization formulation. We further integrate this formulation with the SENSE-based parallel imaging model to handle multichannel acquisitions. The performance of the proposed method was systematically evaluated in 2D real-time PC-CMR with flow phantom experiments and in vivo experiments (with healthy subjects). Additionally, we performed a feasibility study of the proposed method on patients with cardiac arrhythmia. Results: The proposed method achieves a spatial resolution of 1.8 mm and a temporal resolution of 18 ms for 2D real-time PC-CMR with one directional flow encoding. For the flow phantom experiments, both regular and irregular flow patterns were accurately captured. For the in vivo experiments with healthy subjects, flow dynamics obtained from the proposed method correlated well with those from the cine-based acquisitions. For the experiments with the arrhythmic patients, the proposed method demonstrated excellent capability of resolving the beat-by-beat flow variations, which cannot be obtained from the conventional cine-based method. Conclusion: The proposed method enables high-resolution real-time PC-CMR at 2D without ECG gating and respiration control. It accurately resolves beat-by-beat flow variations, which holds great promise for studying patients with irregular heartbeats. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0330-1) contains supplementary material, which is available to authorized users

Breaking Trade‐Off between Selectivity and Activity of Nickel‐Based Hydrogenation Catalysts by Tuning Both Steric Effect and d‐Band Center

Abstract For selective hydrogenation of chemicals the high selectivity is always at the expense of activity and improving both selectivity and activity is challenging. Here, by chelating with p‐fluorothiophenol (SPhF)‐arrays, both steric and electronic effects are created to boost the performance of cheap nickel‐based catalysts. Compared with dinickel phosphide, the SPhF‐chelated one exhibits nearly 12 times higher activity and especially its selectivity is increased from 38.1% and 21.3% to nearly 100% in hydrogenations of 3‐nitrostyrene and cinnamaldehyde. Commercial catalysts like Raney Ni chelating with SPhF‐array also exhibits an enhanced selectivity from 20.5% and 23.4% to ≈100% along with doubled activity. Both experimental and density functional theory (DFT) calculation prove that the superior performance is attributed to the confined flat adsorption by ordered SPhF‐arrays and downshifted d‐band center of catalysts, leading to prohibited hydrogenation of the vinyl group and accelerative H2 activation. Such a surface modification can provide an easily‐realized and low‐cost way to design catalysts for the selective hydrogenation

Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence

Perovskites are promising materials applied in new energy devices, from solar cells to battery electrodes. Under traditional experimental conditions in laboratories, the performance improvement of new energy devices is slow and limited. Artificial intelligence (AI) has recently drawn much attention in material properties prediction and new functional materials exploration. With the advent of the AI era, the methods of studying perovskites have been upgraded, thereby benefiting the energy industry. In this review, we summarize the application of AI in perovskite discovery and synthesis and its positive influence on new energy research. First, we list the advantages of AI in perovskite research and the steps of AI application in perovskite discovery, including data availability, the selection of training algorithms, and the interpretation of results. Second, we introduce a new synthesis method with high efficiency in cloud labs and explain how this platform can assist perovskite discovery. We review the use of perovskites in energy applications and illustrate that the efficiency of energy production in these fields can be significantly boosted due to the use of AI in the development process. This review aims to provide the future application prospects of AI in perovskite research and new energy generation

Identification of Subpathway Signatures For Ovarian Cancer Prognosis by Integrated Analyses of High-Throughput miRNA and mRNA Expression

Background/Aims: Ovarian cancer (OC) causes more death and serious conditions than any other female reproductive cancers, and many expression signatures have been identified for OC prognoses. However, no significant overlap is found among signatures from different studies, indicating the necessity of signature identifications at the functional level. Methods: We performed an integrated analyses of miRNA and gene expressions to identify OC prognostic subpathways (pathway regions). Using The Cancer Genome Atlas data set, we identified core prognostic subpathways, and calculated subpathway risk scores using both miRNA and gene components. Finally, we performed global risk impact analyses to optimize core subpathways using the random walk algorithm. Results: Subpathway-level analyses displayed more robust results than the gene- and miRNA-level analyses. Moreover, we verified the advantage of core subpathways over the entire pathway-based results and their prognostic performance in two independent validation data sets. Based on the global impact score, 13 subpathway signatures were selected and a combined subpathway-based risk score was further calculated for OC patient prognoses. Conclusions: Overall, it was possible to systematically perform integrated analyses of the expression levels of miRNAs and genes to identify prognostic subpathways and infer subpathway risk scores for use in OC clinical applications