Hydrodeoxygenation of bio-oil model compounds on supported noble metal catalysts

This thesis focuses on understanding acidic effects on the mechanisms of Pt or Pd-catalyzed bio-oil model ketone or aldehyde hydrodeoxygenation (HDO) and application of nanocatalysts - supported Pt and Pd with different surface acidity in the hydrodeoxygenation of acetophenone and benzaldehyde. The first part of the thesis addressed the understanding of bio-oil model ketone compound - acetophenone hydrodeoxygenation mechanism over alumina and silica-alumina supported Pt and Pd catalysts by in-situ attenuated total reflection infrared spectroscopy (ATR-IR) in combination with modulation excitation spectroscopy (MES) and phase sensitive detection (PSD). Experimental results indicated acidic supports promoted the hydrodeoxygenation of acetophenone (AP) to produce ethylbenzene (EB). Specially, on alumina supported Pt, AP was predominantly adsorbed on Pt via its η1 (O) configuration and this species was hydrogenated with high chemoselectivity to 1-phenylethanol (PE). On silica-alumina supported Pd, hydrodeoxygenation of AP to EB involves transformation of a carbonyl group to PE via η1 (O) configuration, followed by a dehydration producing styrene on acidic sites of supports, the styrene was further hydrogenated to EB on Pd. The second part focused on the application of acidic supports supported catalysts Pt/Al-MCM-41 and Pt/SiO2-Al2O3 on hydrodeoxygenation of acetophenone and benzaldehyde. Results indicated that Pt/Al-MCM-41 catalysts serve as bifunctional catalysts in the hydrogenation of AP. The overall activity over the noble metal catalysts on acidic supports MCM-41 increased with the increase of surface acidity up to support Si/Al=20, further increase the surface acidity leads to the decrease of catalytic activity. The increase of surface acidity up to Si/Al=20 also promotes the hydrogenation of aromatic ring to produce CMK and CE. For hydrodeoxygenation of benzaldehyde, products toward hydrogenation of both carbonyl and aromatic ring can be produced on a reference Pt/Al2O3 catalyst at 80°C whereas when temperature was increased to 200°C, only toluene and benzene can be detected. SiO2-doped Pt/SiO2-Al2O3 catalysts showed 10%-20% higher catalytic activity than reference catalyst of Pt/Al2O3 under similar reaction condition. Acidity did also influence catalytic selectivity of benzaldehyde hydrodeoxygenation, toluene prefers to form on relative low acidic catalysts whereas methylcyclohexane was more easily formed on high acidic catalysts

Characterizations and perturbation analysis of a class of matrices related to core-EP inverses

[EN] Let A, B is an element of C-nxn with ind(A) = k and ind(B) = s and let L-B = (BB)-B-2(sic). A new condition (C-s,C-*): R(A(k)) boolean AND N((B-s)*) = {0} and R(B-s) boolean AND N((A(k))*) = {0}, is defined. Some new characterizations related to core-EP inverses are obtained when B satisfies condition (C-s,C-*). Explicit expressions of B(sic) and BB(sic) are also given. In addition, equivalent conditions, which guarantee that B satisfies condition (C-s,C-*), are investigated. We proved that B satisfies condition (C-s,C-*) if and only if L-B has a fixed matrix form. As an application, upper bounds for the errors parallel to B(sic) - A(sic)parallel to/parallel to A(sic)parallel to and parallel to BB(sic) - AA(sic)parallel to are studied. (c) 2021 Elsevier B.V. All rights reserved.The authors thank the Editor and Reviewers sincerely for their constructive comments and suggestions which have improved the quality of the paper. This research is supported by the National Natural Science Foundation of China (Nos. 11771076, 11871145), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX18 -0053), the China Scholarship Council (File No. 201906090122). The third author is partially supported by Ministerio de Economia y Competitividad of Spain (grant Red de Excelencia MTM2017-90682-REDT) and partially supported by Universidad de Buenos Aires, Argentina. EXP-UBA: 13.019/2017, 20020170100350BAZhou, M.; Chen, J.; Thome, N. (2021). Characterizations and perturbation analysis of a class of matrices related to core-EP inverses. Journal of Computational and Applied Mathematics. 393:1-11. https://doi.org/10.1016/j.cam.2021.113496S11139

The W-weighted Drazin-star matrix and its dual

[EN] After decades studying extensively two generalized inverses, namely Moore--Penrose inverse and Drazin inverse, currently, we found immersed in a new generation of generalized inverses (core inverse, DMP inverse, etc.). The main aim of this paper is to introduce and investigate a matrix related to these new generalized inverses defined for rectangular matrices. We apply our results to the solution of linear systems.The authors wish to thank the editor and reviewers sincerely for their constructive comments and suggestions that have improved the quality of the paper. This research is supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX18_0053), the China Scholarship Council (File No. 201906090122), the National Natural Science Foundation of China (No.11771076, 11871145). The third author is partially supported by Ministerio de Economía y Competitividad of Spain (grant Red de Excelencia MTM2017-90682-REDT) and Universitat Nacional de La Pampa, Facultad de Ingeniería (Grant Resol. No. 135/19)Zhou, M.; Chen, J.; Thome, N. (2021). The W-weighted Drazin-star matrix and its dual. The Electronic Journal of Linear Algebra. 37:72-87. https://doi.org/10.13001/ela.2021.5389S72873

MST-Based Semi-Supervised Clustering Using M-Labeled Objects

Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse density and arbitrary shape. Based on Minimum Spanning Tree's algorithm variant, K-SSMST can automatically find natural clusters in a dataset by using K labeled data objects where K is the number of clusters. M-SSMST can detect new clusters with insufficient semi-supervised information. Our algorithms have been tested on various artificial and UCI datasets. The results demonstrate that the algorithm's accuracy is better than other supervised and semi-supervised approaches