化工学报 ›› 2021, Vol. 72 ›› Issue (1): 27-41.doi: 10.11949/0438-1157.20201037

• 综述与专论 • 上一篇    下一篇

遗传算法在催化体系的全局结构优化中的应用

石向成1,2,3(),赵志坚1,2(),巩金龙1,2,3   

  1. 1.天津大学化工学院,绿色合成与转化教育部重点实验室,天津 300072
    2.天津化学化工协同创新中心,天津 300072
    3.天津大学-新加坡国立大学福州联合学院,福建 福州 350207
  • 收稿日期:2020-07-25 修回日期:2020-10-05 出版日期:2021-01-05 发布日期:2021-01-05
  • 通讯作者: 赵志坚 E-mail:e0444250@u.nus.edu;zjzhao@tju.edu.cn
  • 作者简介:石向成(1996—),男,博士研究生,e0444250@u.nus.edu
  • 基金资助:
    国家自然科学基金项目(21761132023)

Application of genetic algorithm in the global structure optimization of catalytic system

SHI Xiangcheng1,2,3(),ZHAO Zhijian1,2(),GONG Jinlong1,2,3   

  1. 1.Key Laboratory for Green Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
    2.Collaborative Innovation Center of Chemical Science and Engineering(Tianjin), Tianjin 300072, China
    3.Joint School of National University of Singapore and Tianjin University, Fuzhou 350207, Fujian, China
  • Received:2020-07-25 Revised:2020-10-05 Published:2021-01-05 Online:2021-01-05
  • Contact: ZHAO Zhijian E-mail:e0444250@u.nus.edu;zjzhao@tju.edu.cn

摘要:

对催化体系进行全局结构优化,搜寻基态结构对预测催化剂结构、分析反应物的吸附特性、研究多相催化反应机理、构建实际反应路径等方面至关重要。遗传算法通过交叉、变异和选择等操作,模拟了自然淘汰进化过程,来搜索势能面上的基态结构。作为一种无偏优化算法,遗传算法的优化过程不依赖于输入结构,具有很强的全局搜索能力。对遗传算法在催化体系的全局结构优化问题中的应用进行了综述,介绍了遗传算法在实空间上进行全局结构优化的基本程序框架以及近年来结合并行计算、机器学习等技术发展的改进框架,并讨论了它们在团簇优化、负载型催化剂的结构优化问题上的相关应用,为遗传算法的进一步改进以及更广泛的应用提供理论指导。

关键词: 遗传算法, 全局优化, 催化, 势能面, 纳米结构, 分子模拟, 机器学习

Abstract:

Genetic algorithm is widely used to search for the global minimum structure that is important for analyzing the catalyst structure, the mechanism of heterogeneous catalytic reaction, and actual reaction pathway. By performing crossover, mutation and selection, genetic algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction to explore the potential energy surface. As an unbiased optimization algorithm, the optimization process of genetic algorithm does not depend on the input structure and has strong global search capabilities. This review summarizes the recent progress of the design and application of genetic algorithm, as a global structure optimizer, in the catalytic system. Starting with introducing the standard genetic algorithm framework for global structure optimization, this review also includes the advanced framework developed by introducing parallel computing and machine learning technique. Finally, some examples about the reported application of genetic algorithm in catalytic structure optimization are presented, such as the optimization of metallic clusters, supported catalysts, etc. This review might provide a significant insight into the further improvement of genetic algorithm and the wider application in catalytic system.

Key words: genetic algorithm, global optimization, catalysis, potential energy surface, nanostructure, molecular simulation, machine learning

中图分类号: 

  • O 643

图1

包含和不包含对称性时随机产生的LJ38(a)和LJ100(b)团簇的能量分布(能量值是相对基态结构的相对值)[29]"

图2

父代通过几何剪贴法交叉形成新子代的过程"

图3

基于池的遗传算法框架及其性能测试[21]"

图4

结合主动学习进行加速的遗传算法框架及性能表现"

图5

不同尺寸和电荷下的Au团簇的基态结构"

图6

不同尺寸硫醇配体保护的Au团簇及其成核、成长过程[4]"

图7

遗传算法所得到的各类负载型催化剂的基态结构"

图8

Pt7团簇在α-Al2O3表面上的演化过程[9]"

图9

α-Al2O3(0001)吸附的Pt8H4团簇(a),γ-Al2O3(100)吸附的Pt8H5团簇(b),α-Al2O3(0001)吸附的Pt8H24团簇(c)和γ-Al2O3(100)吸附的Pt8H24团簇(d)的基态结构[91]"

图10

O2在MgO(100)负载的Au12团簇的吸附过程[92]"

1 Johnston R L. Evolving better nanoparticles: genetic algorithms for optimising cluster geometries[J]. Dalton Transactions, 2003, (22): 4193-4207.
2 Mcleod A S, Gladden L F. Heterogeneous catalyst design using stochastic optimization algorithms[J]. Journal of Chemical Information and Computer Sciences, 2000, 40(4): 981-987.
3 Davis J B A, Horswell S L, Johnston R L. Application of a parallel genetic algorithm to the global optimization of gas-phase and supported gold–iridium sub-nanoalloys[J]. The Journal of Physical Chemistry C, 2016, 120(7): 3759-3765.
4 Liu C, Pei Y, Sun H, et al. The nucleation and growth mechanism of thiolate-protected Au nanoclusters[J]. Journal of the American Chemical Society, 2015, 137(50): 15809-15816.
5 Reichenbach T, Walter M, Moseler M, et al. Effects of gas-phase conditions and particle size on the properties of Cu(111)-supported ZnyOx particles revealed by global optimization and ab initio thermodynamics[J]. The Journal of Physical Chemistry C, 2019, 123(51): 30903-30916.
6 Fang Y, Gong X. Genetic algorithm aided density functional theory simulations unravel the kinetic nature of Au(100) in catalytic CO oxidation[J]. Chinese Chemical Letters, 2019, 30(6): 1346-1350.
7 Ding X L, Liao H L, Zhang Y, et al. Geometric and electronic properties of gold clusters doped with a single oxygen atom[J]. Physical Chemistry Chemical Physics, 2016, 18(41): 28960-28972.
8 Zandkarimi B, Alexandrova A N. Dynamics of subnanometer Pt clusters can break the scaling relationships in catalysis[J]. The Journal of Physical Chemistry Letters, 2019, 10(3): 460-467.
9 Zhai H, Alexandrova A N. Local fluxionality of surface-deposited cluster catalysts: the case of Pt7 on Al2O3[J]. The Journal of Physical Chemistry Letters, 2018, 9(7): 1696-1702.
10 Jäger M, Schäfer R, Johnston R L. First principles global optimization of metal clusters and nanoalloys[J]. Advances in Physics: X, 2018, 3(1): 1516514.
11 Stillinger F H. Exponential multiplicity of inherent structures[J]. Physical Review E, 1999, 59(1): 48-51.
12 Sierka M. Synergy between theory and experiment in structure resolution of low-dimensional oxides[J]. Progress in Surface Science, 2010, 85(9/10/11/12): 398-434.
13 Hartke B. Global geometry optimization of clusters using genetic algorithms[J]. The Journal of Physical Chemistry, 1993, 97(39): 9973-9976.
14 Deaven D M, Ho K M. Molecular geometry optimization with a genetic algorithm[J]. Physical Review Letters, 1995, 75(2): 288-291.
15 Froemming N S, Henkelman G. Optimizing core-shell nanoparticle catalysts with a genetic algorithm[J]. The Journal of Chemical Physics, 2009, 131(23): 234103.
16 Vilhelmsen L B, Hammer B. A genetic algorithm for first principles global structure optimization of supported nano structures[J]. The Journal of Chemical Physics, 2014, 141(4): 044711.
17 Yang H, Wong M W. Automatic conformational search of transition states for catalytic reactions using genetic algorithm[J]. The Journal of Physical Chemistry A, 2019, 123(47): 10303-10314.
18 Dittner M, Hartke B. Globally optimal catalytic fields – inverse design of abstract embeddings for maximum reaction rate acceleration[J]. Journal of Chemical Theory and Computation, 2018, 14(7): 3547-3564.
19 Hartke B. Global cluster geometry optimization by a phenotype algorithm with niches: location of elusive minima, and low-order scaling with cluster size[J]. Journal of Computational Chemistry, 1999, 20(16): 1752-1759.
20 Oganov A R, Glass C W. Crystal structure prediction using ab initio evolutionary techniques: principles and applications[J]. The Journal of Chemical Physics, 2006, 124(24): 244704.
21 Shayeghi A, Götz D, Davis J B A, et al. Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters[J]. Physical Chemistry Chemical Physics, 2015, 17(3): 2104-2112.
22 Zhang J, Hu P, Wang H. Amorphous catalysis: machine learning driven high-throughput screening of superior active site for hydrogen evolution reaction[J]. The Journal of Physical Chemistry C, 2020, 124(19): 10483-10494.
23 Zhong M, Tran K, Min Y, et al. Accelerated discovery of CO2 electrocatalysts using active machine learning[J]. Nature, 2020, 581(7807): 178-183.
24 Bisbo M K, Hammer B. Efficient global structure optimization with a machine-learned surrogate model[J]. Physical Review Letters, 2020, 124(8): 086102.
25 Le T C, Winkler D A. Discovery and optimization of materials using evolutionary approaches[J]. Chemical Reviews, 2016, 116(10): 6107-6132.
26 Gobin O C, Schüth F. On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms[J]. Journal of Combinatorial Chemistry, 2008, 10(6): 835-846.
27 Tipton W W, Hennig R G. A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials[J]. Journal of Physics: Condensed Matter, 2013, 25(49): 495401.
28 Rodemerck U, Baerns M, Holena M, et al. Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials[J]. Applied Surface Science, 2004, 223(1): 168-174.
29 Lv J, Wang Y, Zhu L, et al. Particle-swarm structure prediction on clusters[J]. The Journal of Chemical Physics, 2012, 137(8): 084104.
30 Lyakhov A O, Oganov A R, Stokes H T, et al. New developments in evolutionary structure prediction algorithm USPEX[J]. Computer Physics Communications, 2013, 184(4): 1172-1182.
31 Wang Y, Lv J, Zhu L, et al. CALYPSO: a method for crystal structure prediction[J]. Computer Physics Communications, 2012, 183(10): 2063-2070.
32 Zhai H, Alexandrova A N. Ensemble-average representation of Pt clusters in conditions of catalysis accessed through GPU accelerated deep neural network fitting global optimization[J]. Journal of Chemical Theory and Computation, 2016, 12(12): 6213-6226.
33 Hajinazar S, Sandoval E D, Cullo A J, et al. Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models[J]. Physical Chemistry Chemical Physics, 2019, 21(17): 8729-8742.
34 Jennings P C, Lysgaard S, Hummelshøj J S, et al. Genetic algorithms for computational materials discovery accelerated by machine learning[J]. npj Computational Materials, 2019, 5(1): 46.
35 Curtis F, Li X, Rose T, et al. GAtor: a first-principles genetic algorithm for molecular crystal structure prediction[J]. Journal of Chemical Theory and Computation, 2018, 14(4): 2246-2264.
36 Maldonis J J, Xu Z, Song Z, et al. StructOpt: a modular materials structure optimization suite incorporating experimental data and simulated energies[J]. Computational Materials Science, 2019, 160: 1-8.
37 Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
38 Pereira S R M, Clerc F, Farrusseng D, et al. Effect of the genetic algorithm parameters on the optimisation of heterogeneous catalysts[J]. QSAR & Combinatorial Science, 2005, 24(1): 45-57.
39 Yee L, Yong G, Zong-Ben X. Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis[J]. IEEE Transactions on Neural Networks, 1997, 8(5): 1165-1176.
40 Rata I, Shvartsburg A A, Horoi M, et al. Single-parent evolution algorithm and the optimization of Si clusters[J]. Physical Review Letters, 2000, 85(3): 546-549.
41 Jäger M, Schäfer R, Johnston R L. GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands[J]. Nanoscale, 2019, 11(18): 9042-9052.
42 Jørgensen M S, Groves M N, Hammer B. Combining evolutionary algorithms with clustering toward rational global structure optimization at the atomic scale[J]. Journal of Chemical Theory and Computation, 2017, 13(3): 1486-1493.
43 Supady A, Blum V, Baldauf C. First-principles molecular structure search with a genetic algorithm[J]. Journal of Chemical Information and Modeling, 2015, 55(11): 2338-2348.
44 Davis J B A, Shayeghi A, Horswell S L, et al. The Birmingham parallel genetic algorithm and its application to the direct DFT global optimisation of IrN(N = 10–20) clusters[J]. Nanoscale, 2015, 7(33): 14032-14038.
45 Hussein H A, Demiroglu I, Johnston R L. Application of a parallel genetic algorithm to the global optimization of medium-sized Au–Pd sub-nanometre clusters[J]. The European Physical Journal B, 2018, 91(2): 34.
46 Demiroglu I, Yao K, Hussein H A, et al. DFT global optimization of gas-phase subnanometer Ru–Pt clusters[J]. The Journal of Physical Chemistry C, 2017, 121(20): 10773-10780.
47 Hussein H A, Davis J B A, Johnston R L. DFT global optimisation of gas-phase and MgO-supported sub-nanometre AuPd clusters[J]. Physical Chemistry Chemical Physics, 2016, 18(37): 26133-26143.
48 Habershon S, Harris K D M, Johnston R L. Development of a multipopulation parallel genetic algorithm for structure solution from powder diffraction data[J]. Journal of Computational Chemistry, 2003, 24(14): 1766-1774.
49 Fan T E, Shao G F, Ji Q S, et al. A multi-populations multi-strategies differential evolution algorithm for structural optimization of metal nanoclusters[J]. Computer Physics Communications, 2016, 208: 64-72.
50 Jiang B, Li J, Guo H. Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach[J]. International Reviews in Physical Chemistry, 2016, 35(3): 479-506.
51 Li H, Zhang Z, Liu Z. Application of artificial neural networks for catalysis: a review[J]. Catalysts, 2017, 7(10): 306.
52 Le T, Epa V C, Burden F R, et al. Quantitative structure-property relationship modeling of diverse materials properties[J]. Chemical Reviews, 2012, 112(5): 2889-2919.
53 Kamath A, Vargas-Hernández R A, Krems R V, et al. Neural networks vs Gaussian process regression for representing potential energy surfaces: a comparative study of fit quality and vibrational spectrum accuracy[J]. The Journal of Chemical Physics, 2018, 148(24): 241702.
54 Chmiela S, Tkatchenko A, Sauceda H E, et al. Machine learning of accurate energy-conserving molecular force fields[J]. Science Advances, 2017, 3(5): e1603015.
55 Chen X, Jørgensen M S, Li J, et al. Atomic energies from a convolutional neural network[J]. Journal of Chemical Theory and Computation, 2018, 14(7): 3933-3942.
56 Rupp M, Tkatchenko A, Müller K R, et al. Fast and accurate modeling of molecular atomization energies with machine learning[J]. Physical Review Letters, 2012, 108(5): 058301.
57 Ramakrishnan R, Dral P O, Rupp M, et al. Quantum chemistry structures and properties of 134 kilo molecules[J]. Scientific Data, 2014, 1(1): 140022.
58 Behler J. Constructing high-dimensional neural network potentials: a tutorial review[J]. International Journal of Quantum Chemistry, 2015, 115(16): 1032-1050.
59 Zhang L, Lin D Y, Wang H, et al. Active learning of uniformly accurate interatomic potentials for materials simulation[J]. Physical Review Materials, 2019, 3(2): 023804.
60 Kolsbjerg E L, Peterson A A, Hammer B. Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles[J]. Physical Review B, 2018, 97(19): 195424.
61 Sørensen K H, Jørgensen M S, Bruix A, et al. Accelerating atomic structure search with cluster regularization[J]. The Journal of Chemical Physics, 2018, 148(24): 241734.
62 Jørgensen M S, Larsen U F, Jacobsen K W, et al. Exploration versus exploitation in global atomistic structure optimization[J]. The Journal of Physical Chemistry A, 2018, 122(5): 1504-1509.
63 Grosan C, Abraham A. Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews [M]. Berlin, Heidelberg: Springer-Verlag, 2007: 1-17.
64 Chen P H, Shahandashti S M. Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints[J]. Automation in Construction, 2009, 18(4): 434-443.
65 Zacharias C R, Lemes M R, Dal Pino A. Combining genetic algorithm and simulated annealing: a molecular geometry optimization study[J]. Journal of Molecular Structure: THEOCHEM, 1998, 430: 29-39.
66 Cai W, Jiang H, Shao X. Global optimization of lennard-jones clusters by a parallel fast annealing evolutionary algorithm[J]. Journal of Chemical Information and Computer Sciences, 2002, 42(5): 1099-1103.
67 Zeiri Y. Study of the lowest energy structure of atomic clusters using a genetic algorithm[J]. Computer Physics Communications, 1997, 103(1): 28-42.
68 Alexandrova A N, Boldyrev A I. Search for the Lin0/+1/-1 (n = 5-7) lowest-energy structures using the ab initio gradient embedded genetic algorithm (GEGA). Elucidation of the chemical bonding in the lithium clusters[J]. Journal of Chemical Theory and Computation, 2005, 1(4): 566-580.
69 Dieterich J M, Hartke B. OGOLEM: global cluster structure optimisation for arbitrary mixtures of flexible molecules. A multiscaling, object-oriented approach[J]. Molecular Physics, 2010, 108(3/4): 279-291.
70 Vargas J A, Buendía F, Beltrán M R. New AuN (N = 27–30) lowest energy clusters obtained by means of an improved DFT–genetic algorithm methodology[J]. The Journal of Physical Chemistry C, 2017, 121(20): 10982-10991.
71 Yamazoe S, Koyasu K, Tsukuda T. Nonscalable oxidation catalysis of gold clusters[J]. Accounts of Chemical Research, 2013, 47(3): 816-824.
72 Assadollahzadeh B, Schwerdtfeger P. A systematic search for minimum structures of small gold clusters Aun (n=2–20) and their electronic properties[J]. The Journal of Chemical Physics, 2009, 131(6): 064306.
73 Li T X, Yin S Y, Ji Y L, et al. A genetic algorithm study on the most stable disordered and ordered configurations of Au38–55[J]. Physics Letters A, 2000, 267(5/6): 403-407.
74 Shayeghi A, Johnston R L, Schäfer R. Evaluation of photodissociation spectroscopy as a structure elucidation tool for isolated clusters: a case study of Ag4+ and Au4+[J]. Physical Chemistry Chemical Physics, 2013, 15(45): 19715-19723.
75 Gilb S, Weis P, Furche F, et al. Structures of small gold cluster cations (Aun+, n<14): ion mobility measurements versus density functional calculations[J]. The Journal of Chemical Physics, 2002, 116(10): 4094-4101.
76 Hong L, Wang H, Cheng J, et al. Atomic structures and electronic properties of small Au–Ag binary clusters: effects of size and composition[J]. Computational and Theoretical Chemistry, 2012, 993: 36-44.
77 Kang X, Li Y, Zhu M, et al. Atomically precise alloy nanoclusters: syntheses, structures, and properties[J]. Chemical Society Reviews, 2020, 49: 6443-6514.
78 Xie S, Tsunoyama H, Kurashige W, et al. Enhancement in aerobic alcohol oxidation catalysis of Au25 clusters by single Pd atom doping[J]. ACS Catalysis, 2012, 2(7): 1519-1523.
79 Liu C, Ren X, Lin F, et al. Structure of the Au23-xAgx(S-Adm)15 nanocluster and its application for photocatalytic degradation of organic pollutants[J]. Angewandte Chemie International Edition, 2019, 58(33): 11335-11339.
80 Heiles S, Logsdail A J, Schäfer R, et al. Dopant-induced 2D–3D transition in small Au-containing clusters: DFT-global optimisation of 8-atom Au–Ag nanoalloys[J]. Nanoscale, 2012, 4(4): 1109-1115.
81 Davis J B A, Horswell S L, Johnston R L. Global optimization of 8–10 atom palladium–iridium nanoalloys at the DFT level[J]. The Journal of Physical Chemistry A, 2013, 118(1): 208-214.
82 Heard C J, Johnston R L. A density functional global optimisation study of neutral 8-atom Cu-Ag and Cu-Au clusters[J]. The European Physical Journal D, 2013, 67(2): 34.
83 Luo Z, Nachammai V, Zhang B, et al. Toward understanding the growth mechanism: tracing all stable intermediate species from reduction of Au(I)–thiolate complexes to evolution of Au25 nanoclusters[J]. Journal of the American Chemical Society, 2014, 136(30): 10577-10580.
84 Hussein H A, Demiroglu I, Johnston R L. Application of a parallel genetic algorithm to the global optimization of medium-sized Au–Pd sub-nanometre clusters[J]. The European Physical Journal B, 2018, 91(2): 34.
85 Bailey M S, Wilson N T, Roberts C, et al. Structures, stabilities and ordering in Ni-Al nanoalloy clusters[J]. The European Physical Journal D - Atomic, Molecular, Optical and Plasma Physics, 2003, 25(1): 41-55.
86 Zhang J, Glezakou V A, Rousseau R, et al. NWPEsSe: an adaptive-learning global optimization algorithm for nanosized cluster systems[J]. Journal of Chemical Theory and Computation, 2020, 16(6): 3947-3958.
87 Sierka M, Todorova T K, Kaya S, et al. Interplay between theory and experiment in the quest for silica with reduced dimensionality grown on a Mo(112) surface[J]. Chemical Physics Letters, 2006, 424(1/2/3): 115-119.
88 Hjorth L A, Jørgen M J, Blomqvist J, et al. The atomic simulation environment—a Python library for working with atoms[J]. Journal of Physics: Condensed Matter, 2017, 29(27): 273002.
89 Vilhelmsen L B, Walton K S, Sholl D S. Structure and mobility of metal clusters in MOFs: Au, Pd, and AuPd clusters in MOF-74[J]. Journal of the American Chemical Society, 2012, 134(30): 12807-12816.
90 He Y, Liu J C, Luo L, et al. Size-dependent dynamic structures of supported gold nanoparticles in CO oxidation reaction condition[J]. Proceedings of the National Academy of Sciences, 2018, 115(30): 7700-7705.
91 Sun G, Alexandrova A N, Sautet P. Pt8 cluster on alumina under a pressure of hydrogen: support-dependent reconstruction from first-principles global optimization[J]. The Journal of Chemical Physics, 2019, 151(19): 194703.
92 Vilhelmsen L B, Hammer B. Systematic study of Au6 to Au12 gold clusters on MgO(100) F-centers using density-functional theory[J]. Physical Review Letters, 2012, 108(12): 126101.
[1] 戴晓业, 安青松, 许云婷, 史琳. 废弃制冷剂降解方法研究现状及思考[J]. 化工学报, 2021, 72(S1): 1-6.
[2] 李腾飞, 缪赟, 杨柳, 王龙耀, 朱铧丞. 微波强化Y型分子筛离子交换技术[J]. 化工学报, 2021, 72(S1): 406-412.
[3] 陈晨, 王明明, 王志刚, 谭小耀. 镍基非对称中空纤维膜用于乙醇自热重整制氢[J]. 化工学报, 2021, 72(S1): 482-493.
[4] 张牧星, 张小松, 丁烨, 宋翼. 氧化石墨烯膜间距对电渗析空气除湿特性影响的分子动力学研究[J]. 化工学报, 2021, 72(S1): 63-69.
[5] 李闯, 张扬, 刘小娟, 王学重. 添加剂作用下阿司匹林结晶模拟和实验研究[J]. 化工学报, 2021, 72(9): 4796-4807.
[6] 王欢, 符方宝, 李琼, 席跃宾, 杨东杰. 木质素碳纳米材料制备及在催化中的应用研究进展[J]. 化工学报, 2021, 72(9): 4445-4457.
[7] 陈旭杰, 吕喜蕾, 史欢欢, 郑丽萍, 魏茜文, 田鹏辉, 蒋雨希, 吕秀阳. HBr-MgBr2催化己糖二酸脱水环合制备2,5-呋喃二甲酸的研究[J]. 化工学报, 2021, 72(9): 4658-4664.
[8] 贺兴处,陈德珍,梅振飞,阿迪力·巴吐尔null,安青. CaO催化PE热解及H2O对催化过程影响的ReaxFF MD研究与机理分析[J]. 化工学报, 2021, 72(9): 4665-4674.
[9] 谢晶, 舒歌平, 杨葛灵, 高山松, 王洪学, 卢晗锋, 陈银飞. Mo修饰的钼铁复合催化剂及其煤直接液化催化性能[J]. 化工学报, 2021, 72(9): 4675-4684.
[10] 杨丽, 孙赟冬, 焦勇, 杨烨, 陈建标, 廖传华. 灰分在印染污泥热解气化中的协同催化机理[J]. 化工学报, 2021, 72(9): 4718-4729.
[11] 单良, 尹荣强, 王慧, 费传军, 周清清, 徐杰, 王志强, 徐涛, 陈建军, 李俊华. VMoTi/玻纤复合催化滤布制备及其除尘协同脱硝性能研究[J]. 化工学报, 2021, 72(9): 4892-4899.
[12] 朱轶林, 张新敬, 徐玉杰, 丁捷, 郭欢, 陈海生. 基于遗传算法-综合计算法的生物质热解气化优化分析[J]. 化工学报, 2021, 72(9): 4910-4920.
[13] 从少领, 赵捷, 杨玉飞, 吴长清, 贺凡, 袁华, 汪晓芹, 熊善新, 吴燕, 周安宁. 煤基聚苯胺制掺N碳微纳米管的实验研究[J]. 化工学报, 2021, 72(9): 4950-4960.
[14] 王伟, 钱伟鑫, 马宏方, 应卫勇, 张海涛. 吡啶修饰H-MOR上二甲醚羰基化吸附-扩散理论研究[J]. 化工学报, 2021, 72(9): 4786-4795.
[15] 梁家豪, 张国强, 高源, 尹娇, 郑华艳, 李忠. 介孔构建对CuY甲醇氧化羰基化反应活性的影响[J]. 化工学报, 2021, 72(9): 4685-4697.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] PatrickPERRE. 硬木中流体移动的双尺度多孔机理的依据(Ⅱ)描述实验结果的双尺度计算模型[J]. CIESC Journal, 2004, 12(6): 783 -791 .
[2] 刘植昌, 孟祥海, 徐春明, 高金森. 重油催化裂解汽柴油二次裂解性能研究[J]. CIESC Journal, 2007, 15(3): 309 -314 .
[3] 刘永健, 袁希钢, 罗祎青. 基于浓度间隔分析的用水系统集成(II)不连续过程[J]. CIESC Journal, 2007, 15(3): 369 -375 .
[4] 王斐, 汪文川, 黄世萍, 滕加伟, 谢在库. 正丁烷及丁烯-1在不同硅铝比ZSM-5分子筛上吸附的实验与模型[J]. CIESC Journal, 2007, 15(3): 376 -386 .
[5] 孙国刚, 时铭显. 喷嘴进料对催化裂化提升管流动行为的影响[J]. CIESC Journal, 2003, 11(6): 638 -642 .
[6] 王宏智, 姚素薇, 张卫国. 电沉积Ni-W梯度镀层及其结构表征[J]. CIESC Journal, 2003, 11(3): 348 -351 .
[7] 蔡振云, 卢祖国, 李小波. 用管式反应技术制备乙二醇乙醚乙酸酯[J]. CIESC Journal, 2003, 11(3): 338 -340 .
[8] 杨卫国, 王金福, 金涌. 高温高压浆态鼓泡床反应器中的气-液传质[J]. CIESC Journal, 2001, 9(3): 253 -257 .
[9] 虞启明, pairat Kaewsarn, 马卫东, Jose T. Matheickal, 尹平河. 用海藻类生物吸附剂去除废水中的重金属离子——一种经济型新技术[J]. CIESC Journal, 2001, 9(2): 133 -136 .
[10] Tahir Imran Qureshi, Dong-Ik Song, Young-Woong Jeon, Young-Sup Lee . 十六烷基三甲基季铵阳离子改良二氧化硅对酸性染料的吸着[J]. CIESC Journal, 2002, 10(1): 102 -108 .