Molecular Switching by Proton-Coupled Electron Transport Drives Giant Negative Differential Resistance
To develop new types of dynamic molecular devices with atomic-scale control over electronic function, new types of molecular switches are needed with time-dependent switching probabilities. We report such a molecular switch based on proton-coupled electron transfer (PCET) reaction with giant hysteric negative differential resistance (NDR) with peak-to-valley ratios of 120 ± 6.6 and memory on/off ratios of (2.4 ± 0.6) * 103. The switching dynamics probabilities are modulated by bias voltage sweep rate and can also be controlled by pH and relative humidity, confirmed by kinetic isotope effect measurements. The demonstrated dynamical and environment-specific modulation of giant NDR and memory effects provide new opportunities for bioelectronics and artificial neural networks..
Medienart: |
Preprint |
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Erscheinungsjahr: |
2024 |
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Erschienen: |
2024 |
Enthalten in: |
chemRxiv.org - (2024) vom: 26. März Zur Gesamtaufnahme - year:2024 |
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Sprache: |
Englisch |
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Beteiligte Personen: |
Zhang, Qian [VerfasserIn] |
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Links: |
Volltext [kostenfrei] |
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Themen: |
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doi: |
10.26434/chemrxiv-2024-3m9ff |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
XCH042924928 |
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520 | |a To develop new types of dynamic molecular devices with atomic-scale control over electronic function, new types of molecular switches are needed with time-dependent switching probabilities. We report such a molecular switch based on proton-coupled electron transfer (PCET) reaction with giant hysteric negative differential resistance (NDR) with peak-to-valley ratios of 120 ± 6.6 and memory on/off ratios of (2.4 ± 0.6) * 103. The switching dynamics probabilities are modulated by bias voltage sweep rate and can also be controlled by pH and relative humidity, confirmed by kinetic isotope effect measurements. The demonstrated dynamical and environment-specific modulation of giant NDR and memory effects provide new opportunities for bioelectronics and artificial neural networks. | ||
650 | 4 | |a Chemistry |7 (dpeaa)DE-84 | |
650 | 4 | |a 540 |7 (dpeaa)DE-84 | |
700 | 1 | |a Wang, Yulong |0 (orcid)0000-0001-9441-0497 |4 aut | |
700 | 1 | |a Nickle, Cameron |4 aut | |
700 | 1 | |a Zhang, Ziyu |4 aut | |
700 | 1 | |a Leoncini, Andrea |0 (orcid)0000-0002-8127-1085 |4 aut | |
700 | 1 | |a Qi, Dong-Chen |4 aut | |
700 | 1 | |a Sotthewes, Kai |4 aut | |
700 | 1 | |a Borrini, Alessandro |0 (orcid)0000-0001-5468-0038 |4 aut | |
700 | 1 | |a J. W. Zandvliet, Harold |0 (orcid)0000-0001-6809-139X |4 aut | |
700 | 1 | |a del Barco, Enrique |4 aut | |
700 | 1 | |a Thompson, Damien |4 aut | |
700 | 1 | |a A. Nijhuis, Christian |4 aut | |
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