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Updated: Mar 31, 2026
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Photoelectron Imaging of Anions Illustrated by 310 Nm Detachment of F−
Published on: July 27, 2018
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Anion Photoelectron Spectroscopy and CASSCF/CASPT2/RASSI Study of Lan(-) (n = 1, 3-7).
A S Ivanov1, X Zhang2, H Wang2
1Department of Chemistry and Biochemistry, Utah State University , 0300 Old Main Hill, Logan, Utah 84322-0300, United States.
The Journal of Physical Chemistry. A
|October 28, 2015
Summary
Researchers studied small lanthanum clusters using photoelectron spectroscopy and advanced computational methods. They determined electron affinities and identified a triangular structure for La3(-), revealing novel d-orbital aromaticity.
Area of Science:
- Quantum chemistry
- Atomic and molecular physics
- Materials science
Background:
- Lanthanum clusters are of interest due to their unique electronic properties.
- Understanding their structure and bonding is crucial for potential applications.
Purpose of the Study:
- To experimentally and theoretically investigate small lanthanum clusters (Lan-, n=1, 3-7).
- To determine electron affinities and electronic structures of these clusters.
- To elucidate the bonding and aromaticity in the lanthanum trimer anion (La3(-)).
Main Methods:
- Negative ion photoelectron spectroscopy for experimental data acquisition.
- High-level multiconfigurational quantum chemical calculations (CASSCF/CASPT2/RASSI/ANO-RCC-L) with spin-orbit corrections for theoretical analysis.
- Chemical bonding analysis to understand electronic structure.
Main Results:
- Electron affinities for Lan- clusters were measured, ranging from 0.49 eV (La-) to 1.5 eV (La7-).
- The most stable structure for La3(-) was determined to be an equilateral triangle with (1)A1' symmetry.
- Experimental accuracy was achieved using advanced computational methods, including spin-orbit corrections.
Conclusions:
- The study provides a comprehensive understanding of small lanthanum cluster properties.
- La3(-) exhibits the first experimentally observed d-orbital double sigma and pi aromaticity.
- This work highlights the importance of high-level computational methods for accurate prediction of cluster properties.
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