Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 669

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Transport Measurements Across a Tunable Potential Barrier in Graphene

Open access
Huard, B., Sulpizio, J. A., Stander, N., Todd, K., Yang, B., Goldhaber-Gordon, D.·2007·Physical Review Letters

Abstract

The peculiar nature of electron scattering in graphene is among many exciting theoretical predictions for the physical properties of this material. To investigate electron scattering properties in a graphene plane, we have created a gate-tunable potential barrier within a single-layer graphene sheet. We report measurements of electrical transport across this structure as the tunable barrier potential is swept through a range of heights. When the barrier is sufficiently strong to form a bipolar junction (n-p-n or p-n-p) within the graphene sheet, the resistance across the barrier sharply increases. We compare these results to predictions for both diffusive and ballistic transport, as the barrier rises on a length scale comparable to the mean free path. Finally, we show how a magnetic field modifies transport across the barrier.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1103/physrevlett.98.236803

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 669

Metadata

Authors
Huard, B., Sulpizio, J. A., Stander, N., Todd, K., Yang, B., & Goldhaber-Gordon, D.
Venue
Physical Review Letters
Published
2007-06-07
Volume
98
Issue
23
Pages
236803-236803
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W1999175619
Cited by
669
References
4
FWCI
34.37
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Transport Measurements Across a Tunable Potential Barrier in Graphene. https://app.cochat.ai/papers/10.1103/physrevlett.98.236803
The paper
Huard, B., Sulpizio, J. A., Stander, N., Todd, K., Yang, B., & Goldhaber-Gordon, D. (2007). Transport Measurements Across a Tunable Potential Barrier in Graphene. Physical Review Letters. https://doi.org/10.1103/physrevlett.98.236803

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.