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SUMMARY:Characterizing the electrical double layer at oxide-electrolyte in
 terfaces using  machine learning potential simulations - Prof. Annabella S
 elloni\, Princeton University
DTSTART:20241104T143000Z
DTEND:20241104T150000Z
UID:TALK224101@talks.cam.ac.uk
CONTACT:Dr Fabian Berger
DESCRIPTION:The electrical double layer (EDL) at metal oxide-electrolyte i
 nterfaces critically affects fundamental processes in water splitting\, ba
 tteries\, and corrosion.  However\, limitations in the microscopic-level u
 nderstanding of the EDL have been a major bottleneck in controlling these 
 interfacial processes. We used ab initio-based machine learning potential 
 simulations incorporating long-range electrostatics to investigate the str
 ucture and chemistry of the EDL at the prototypical anatase TiO2-electroly
 te interface under various pH conditions. Our simulations show that the la
 rger capacitance of the EDL under basic relative to acidic conditions orig
 inates primarily from the higher affinity of the cations for the oxide sur
 face and gives rise to distinct charging mechanisms on negative and positi
 ve surfaces.  These results are validated by the agreement between the com
 puted EDL capacitance and experimental data.
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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