Identifying Quantum Interference Effects from Joint Conductance–Thermopower Statistics

Although quantum effects are thought to dominate the heat and charge transport through molecular junctions, large uncertainties in chemical structure, lead-molecule coupling strengths, and energy levels make it difficult to definitively identify these effects from the measured thermopower <i>S</i> and conductance <i>G</i> distributions alone. Here, we develop a simple statistical method to identify destructive quantum interference features (nodes) through the anticorrelation between simultaneously measured <i>G</i> and <i>S</i> values. We find these correlations can be used to unambiguously identify far-detuned nodes, even when <i>G</i> and <i>S</i> distributions alone cannot. As an example, we consider several para- and meta-configured systems, including benzenediamine and diiodo-terphenyl-based junctions, finding that nodes can be identified in ensembles with broad level-alignment and lead-molecule coupling distributions, and with significant anodal transport contributions, including from vacuum tunneling. The efficacy and limitations of this method are analyzed.

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Publication Details

Journal
Nano Letters
Published
2024-11-13
DOI
https://doi.org/10.1021/acs.nanolett.4c04439
Citations
5
Primary Topic
Molecular Junctions and Nanostructures
Type
article
Field-Weighted Citation Impact
1.04

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article

Identifying Quantum Interference Effects from Joint Conductance–Thermopower Statistics

Justin P. Bergfield
5 citations
Nano Letters
Molecular Junctions and Nanostructures
1.04
article

Identifying Quantum Interference Effects from Joint Conductance–Thermopower Statistics

Justin P. Bergfield
article en
5 citations

Abstract

Although quantum effects are thought to dominate the heat and charge transport through molecular junctions, large uncertainties in chemical structure, lead-molecule coupling strengths, and energy levels make it difficult to definitively identify these effects from the measured thermopower <i>S</i> and conductance <i>G</i> distributions alone. Here, we develop a simple statistical method to identify destructive quantum interference features (nodes) through the anticorrelation between simultaneously measured <i>G</i> and <i>S</i> values. We find these correlations can be used to unambiguously identify far-detuned nodes, even when <i>G</i> and <i>S</i> distributions alone cannot. As an example, we consider several para- and meta-configured systems, including benzenediamine and diiodo-terphenyl-based junctions, finding that nodes can be identified in ensembles with broad level-alignment and lead-molecule coupling distributions, and with significant anodal transport contributions, including from vacuum tunneling. The efficacy and limitations of this method are analyzed.

Nano LettersVol. 24(47)
Illinois State University (US)
Division of Materials Research
Climate action
Openalex Percentile: Top 22%
Molecular Junctions and Nanostructures
1.04
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Identifying Quantum Interference Effects from Joint Conductance–Thermopower Statistics — Justin P. Bergfield · Nano Letters (2024) | TGRS Research Map | TGRS