scholarly journals Erratum to : Mechanism of Core-level-excitation-induced Ion Desorption from the Transition-metal-oxide Surface

Shinku ◽  
2003 ◽  
Vol 46 (4) ◽  
pp. 375-375
2020 ◽  
Vol 124 (39) ◽  
pp. 21407-21417
Author(s):  
Daniel J. Aschaffenburg ◽  
Seiji Kawasaki ◽  
Chaitanya Das Pemmaraju ◽  
Tanja Cuk

1985 ◽  
Vol 31 (8) ◽  
pp. 5580-5583 ◽  
Author(s):  
Erminald Bertel ◽  
Roger Stockbauer ◽  
Richard L. Kurtz ◽  
David E. Ramaker ◽  
Theodore E. Madey

1990 ◽  
Vol 208 ◽  
Author(s):  
Anna Swan ◽  
W. Franzen ◽  
M. El-Batanouny ◽  
K. M. Martini

ABSTRACTWe suggest a new application for elastic scattering of a metastable spin-polarized atomic helium beam at thermal energy. We demonstrate how angle-resolved measurements of the Bragg peaks of scattered surviving metastable atoms can give information about the spin-ordering of an antiferromagnetic (AF) transition metal oxide surface. In this paper, we discuss the feasibility of such measurements for NiO(100) and MnO(100), based on available information about their electronic structure and the properties of spin-polarized metastable helium. On impact with a surface, the survival probability of metastables is generally very low (<10-2). There are two possible decay mechanisms for metastables, a resonance ionization followed by auger neutralization, or an auger de-excitation process. For AF surfaces which fulfill certain requirements on their electronic structure, spin-selection rules will inhibit the decay of the metastable atoms from a favourably aligned magnetic sublattice. The survival probability will then be dramatically enhanced from the chosen sublattice, and the coherently scattered surviving metastables will reflect the periodicity of that magnetic sublattice. In contrast to other methods currently applied to magnetic systems, this method does not rely on difference spectra. Consequently, reversal of spinorientation is not necessary for the observation of magnetic ordering.


Nanoscale ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 3853-3859
Author(s):  
Ryosuke Mizuguchi ◽  
Yasuhiko Igarashi ◽  
Hiroaki Imai ◽  
Yuya Oaki

Lateral sizes of the exfoliated transition-metal–oxide nanosheets were predicted and controlled by the assistance of machine learning. 


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