CO_(2) hydrogenation to methanol over the copper promoted In_(2)O_(3) catalyst

The metal promoted In_(2)O_(3) catalysts for CO_(2) hydrogenation to methanol have attracted wide attention because of their high activity with high methanol selectivity.However,there was still no experimental confirmation if copper could be a good promoter for In_(2)O_(3).Herein,the Cu promoted In_(2)O_(3) catalyst was prepared using a deposition-precipitation method.Such prepared Cu/In_(2)O_(3) catalyst shows significantly higher CO_(2) conversion and space time yield(STY)of methanol,compared to the un-promoted In_(2)O_(3) catalyst.The loading of Cu facilitates the activation of both H_(2) and CO_(2) with the interface between the Cu cluster and defective In_(2)O_(3) as the active site.The Cu/In_(2)O_(3) catalyst takes the CO hydrogenation pathway for methanol synthesis from CO_(2) hydrogenation.It exhibits a unique size effect on the CO adsorption.At temperatures below 250℃,CO adsorption on Cu/In_(2)O_(3) is stronger than that on In_(2)O_(3),causing higher methanol selectivity.With increasing temperatu res,the Cu catalyst aggregates,which leads to the formation of weak CO adsorption site and causes a decrease in the methanol selectivity.Compared with other metal promoted In_(2)O_(3) catalysts,it can be concluded that the catalyst with stronger CO adsorption possesses higher methanol selectivity.

Deep learning assisted variational Hilbert quantitative phase imaging

We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.

Synthesis of tetrahedrally coordinated CoO for higher alcohol synthesis directly from syngas

Higher alcohol synthesis directly from syngas is highly desirable as one of the efficient non-petroleum energy conversion routes.Co^(0)–CoO catalysts showed great potential for this reaction,but the alcohol selectivity still needs to be improved and the crystal structure effect of Co^(0)on catalytic behaviors lacks investigation.Here,a series of tetrahedrally coordinated Co^(0)polymorphs were prepared by a thermal decomposition method,which consisted of wurtzite CoO and zinc blende CoO with varied contents.After diluting with SiO_(2),the catalyst showed excellent performance for higher alcohol synthesis with ROH selectivity of 45.8%and higher alcohol distribution of 84.1 wt%under the CO conversion of 38.0%.With increasing the content of wurtzite CoO,the Co^(0)/Co^(2+)ratio gradually increased in the spent catalysts,while the proportion of highly active hexagonal close packed cobalt in Co^(0)decreased,leading to first decreased then increased CO conversion.Moreover,the higher content of zinc blende CoO in fresh catalyst facilitated the retention of more Co^(2+)sites in spent catalysts,promoting the ROH selectivity but slightly decreasing the distribution of higher alcohols.The catalyst with 40%wurtzite CoO obtained the optimal performance with a space time yield toward higher alcohols of 7.9 mmol·gcat^(-1)·h^(-1).

Carbon layer-coated ordered mesoporous silica supported Co-based catalysts for higher alcohol synthesis:The role of carbon source

Surface chemical properties of supports have an important influence on active sites and their catalytic behavio r.Here,we fabricated a series of cobalt-based catalysts supported by carbon layer-coated ordered mesoporous silica(OMS) composites for higher alcohol synthesis(HAS).The carbon layers were derived from different sources and uniformly coated on the porous surface of OMS.Combined with the characterization results of carbonized catalysts,it is demonstrated that the carbon layer-coated supports significantly enhanced the metal dispersion and increased the ratio of Co2+ to Co0 sites,which further increased the CO conversion and alcohols selectivity.Moreover,it is found that the catalytic activity changed in line with the amount of defects and surface oxygenic groups of carbon layers,which re sulted from the different carbon sources.The highest space time yield of C2+OH was 27.5 mmol gcat-1h-1)obtained by the catalyst coated with glucose-derived carbon layer.But the carbon source is not the key factor influencing the distribution of Co-Co2+ dual sites and shows little effect on selectivity in HAS.These results may guide for further design of carbon supported catalysts.