ArticleRSC advances2024
Nanoarchitectonics of molecular self assembled monolayers by transition metal ion intercalation for enhancement of molecular junction conductivity.
Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research delves into the role of metal ions in enhancing the electronic properties of 5,5'-bis(mercaptomethyl)-2,2'-bipyridine (BPD) self-assembled monolayers (SAMs). It combines experimental techniques and numerical simulations to understand the impact of these ions on the structural, electronic, and transport properties of BPD SAMs. Key findings include the varied bonding preferences of metal ions and their significant role in modifying the electronic structure of BPD molecules, leading to enhanced electron delocalization and migration. The study highlights the potential of metal ions in advancing molecular electronics, particularly in the development of high-performance electronic and energy devices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.