Molecular cluster analysis of O2 adsorption and dissociation on Pt(111)

1992 ◽  
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pp. 167-172 ◽  
Author(s):  
Ru-Hong Zhou ◽  
Pei-Lin Cao
2010 ◽  
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pp. 10723 ◽  
Author(s):  
Alberto Roldán ◽  
Josep Manel Ricart ◽  
Francesc Illas ◽  
Gianfranco Pacchioni

Langmuir ◽  
2017 ◽  
Vol 33 (42) ◽  
pp. 11156-11163 ◽  
Author(s):  
Lu Tan ◽  
Liangliang Huang ◽  
Qi Wang ◽  
Yingchun Liu

2017 ◽  
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pp. 6079-6089 ◽  
Author(s):  
Raúl García-Cruz ◽  
Enrique Poulain ◽  
Isaías Hernández-Pérez ◽  
Juan A. Reyes-Nava ◽  
Julio C. González-Torres ◽  
...  

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Author(s):  
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Qin Liu ◽  
Yongping Zheng ◽  
Zhu Wang ◽  
Chunxu Pan ◽  
...  

2007 ◽  
Vol 79 (5) ◽  
pp. 488-494 ◽  
Author(s):  
Grace Tjon ◽  
Maria Xiridou ◽  
Roel Coutinho ◽  
Sylvia Bruisten

2016 ◽  
Vol 18 (6) ◽  
pp. 4569-4576 ◽  
Author(s):  
Liangliang Liu ◽  
Qin Liu ◽  
Wei Xiao ◽  
Chunxu Pan ◽  
Zhu Wang

Two active oxygen adatoms and two strongly bonded oxygen adatoms are generated after the dissociation of two O2 molecules near a subsurface Ti interstitial.


Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.


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