Infrared Multiple Photon Dissociation Spectroscopy of Hydrated Cobalt Anions Doped with Carbon Dioxide CoCO2 (H2 O)n - , n=1-10, in the C-O Stretch Region.
Erik BarwaMilan OnčákTobias F PascherAndreas HerburgerChristian van der LindeMartin K BeyerPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2020)
We investigate anionic [Co,CO2 ,nH2 O]- clusters as model systems for the electrochemical activation of CO2 by infrared multiple photon dissociation (IRMPD) spectroscopy in the range of 1250-2234 cm-1 using an FT-ICR mass spectrometer. We show that both CO2 and H2 O are activated in a significant fraction of the [Co,CO2 ,H2 O]- clusters since it dissociates by CO loss, and the IR spectrum exhibits the characteristic C-O stretching frequency. About 25 % of the ion population can be dissociated by pumping the C-O stretching mode. With the help of quantum chemical calculations, we assign the structure of this ion as Co(CO)(OH)2 - . However, calculations find Co(HCOO)(OH)- as the global minimum, which is stable against IRMPD under the conditions of our experiment. Weak features around 1590-1730 cm-1 are most likely due to higher lying isomers of the composition Co(HOCO)(OH)- . Upon additional hydration, all species [Co,CO2 ,nH2 O]- , n≥2, undergo IRMPD through loss of H2 O molecules as a relatively weakly bound messenger. The main spectral features are the C-O stretching mode of the CO ligand around 1900 cm-1 , the water bending mode mixed with the antisymmetric C-O stretching mode of the HCOO- ligand around 1580-1730 cm-1 , and the symmetric C-O stretching mode of the HCOO- ligand around 1300 cm-1 . A weak feature above 2000 cm-1 is assigned to water combination bands. The spectral assignment clearly indicates the presence of at least two distinct isomers for n ≥2.
Keyphrases
- carbon dioxide
- high resolution
- molecular dynamics
- monte carlo
- optical coherence tomography
- density functional theory
- molecular dynamics simulations
- ionic liquid
- single molecule
- gold nanoparticles
- room temperature
- machine learning
- magnetic resonance imaging
- quantum dots
- computed tomography
- metal organic framework
- electron transfer
- reduced graphene oxide
- highly efficient