Mono-exponential Current Attenuation with Distance across 16 nm Thick Bacteriorhodopsin Multilayers
Domenikos Chryssikos, Jerry A. Fereiro, Jonathan Rojas, Sudipta Bera, Defne T\"uz\"un, Evanthia Kounoupioti, Rui N. Pereira, Christian Pfeiffer, Ali Khoshouei, Hendrik Dietz, Mordechai Sheves, David Cahen, and Marc Tornow

TL;DR
This study demonstrates a mono-exponential decay of electrical conductance with distance in bacteriorhodopsin multilayers up to 16 nm thick, revealing efficient long-range electron transport that challenges existing theories.
Contribution
It provides the first detailed measurement of exponential current attenuation in thick bacteriorhodopsin layers and suggests the need for new theoretical models for protein-based charge transport.
Findings
Conductance decays mono-exponentially with a small attenuation coefficient (~0.8 nm^{-1})
Effective energy barriers of about 100 meV are observed from temperature-dependent measurements
Transport is consistent with tunneling through protein-protein and protein-electrode interfaces
Abstract
The remarkable ability of natural proteins to conduct electricity in the dry state over long distances remains largely inexplicable despite intensive research. In some cases, a (weakly) exponential length-attenuation, as in off-resonant tunneling transport, extends to thicknesses even beyond 10 nm. This report deals with such charge transport characteristics observed in self-assembled multilayers of the protein bacteriorhodopsin (bR). About 7.5 nm to 15.5 nm thick bR layers were prepared on conductive titanium nitride (TiN) substrates using aminohexylphosphonic acid and poly-diallyl-dimethylammonium electrostatic linkers. Using conical EGaIn top contacts, an intriguing, mono-exponential conductance attenuation as a function of the bR layer thickness with a small attenuation coefficient is measured at zero bias. Variable-temperature measurements…
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Taxonomy
TopicsPhotoreceptor and optogenetics research · Molecular Communication and Nanonetworks · Neural dynamics and brain function
