ArticleSmall methods2026
Gate-Assisted Programmable Molecular Doping of Epitaxial Graphene Devices.
Article in Small methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Gate-Assisted Programmable Molecular Doping of Epitaxial Graphene Devices.Small methods · 2026Article
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Since the discovery of graphene, control of its carrier density via doping or functionalization has been a crucial need. Despite significant progress, precise control of the carrier density for epitaxial graphene on SiC remains a challenge. Multiple cycles of doping and characterization are often required before achieving a desired carrier density. In this work, a new approach is demonstrated to precisely program the doping level in top-gated epitaxial graphene devices that are exposed to nitric acid vapor before the gate deposition. With the help of an applied gate voltage, the modification of carrier concentration introduced by the nitric acid can be reversibly controlled, while the corresponding carrier density at zero gate voltage can be accurately tuned by more than 4 × 10
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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.