The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
SARS-CoV-2 RapidPlex: A Graphene-Based Multiplexed Telemedicine Platform for Rapid and Low-Cost COVID-19 Diagnosis and Monitoring
Open accessAbstract
The COVID-19 pandemic is an ongoing global challenge for public health systems. Ultrasensitive and early identification of infection is critical in preventing widespread COVID-19 infection by presymptomatic and asymptomatic individuals, especially in the community and in-home settings. We demonstrate a multiplexed, portable, wireless electrochemical platform for ultra-rapid detection of COVID-19: the SARS-CoV-2 RapidPlex. It detects viral antigen nucleocapsid protein, IgM and IgG antibodies, as well as the inflammatory biomarker C-reactive protein, based on our mass-producible laser-engraved graphene electrodes. We demonstrate ultrasensitive, highly selective, and rapid electrochemical detection in the physiologically relevant ranges. We successfully evaluated the applicability of our SARS-CoV-2 RapidPlex platform with COVID-19-positive and COVID-19-negative blood and saliva samples. Based on this pilot study, our multiplexed immunosensor platform may allow for high-frequency at-home testing for COVID-19 telemedicine diagnosis and monitoring.
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.
Integrity signals
Registry
Cites
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.
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
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Self-citationWu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., Huang, H., Zhang, L., Zhou, X., Du, C., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.0994 · Cited by 8,249
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,685
How will country-based mitigation measures influence the course of the COVID-19 epidemic?
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30567-5 · Cited by 3,648
Wearable biosensors for healthcare monitoring
Kim, J., Campbell, A. S., de Ávila, B. E.-F., & Wang, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0045-y · Cited by 3,373
Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019
Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., Wang, X., Yuan, J., Li, T., Li, J., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa344 · Cited by 2,607
Mizumoto, K., Kagaya, K., Zarebski, A., & Chowell, G. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.10.2000180 · Cited by 2,276
Seo, G., Lee, G., Kim, M. J., Baek, S.-H., Choi, M., Ku, K. B., Lee, C.-S., Jun, S., Park, D., Kim, H. G., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c02823 · Cited by 1,837
Li, Z., Yi, Y., Luo, X., Xiong, N., Liu, Y., Li, S., Sun, R., Wang, Y., Hu, B., Chen, W., et al. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25727 · Cited by 1,771
SHERLOCK: nucleic acid detection with CRISPR nucleases
Kellner, M. J., Koob, J. G., Gootenberg, J. S., Abudayyeh, O. O., & Zhang, F. (2019) · Nature Protocols · doi:10.1038/s41596-019-0210-2 · Cited by 1,631
Cited by
Wearable chemical sensors for biomarker discovery in the omics era
Sempionatto, J. R., Lasalde-Ramírez, J. A., Mahato, K., Wang, J., & Gao, W. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00439-w · Cited by 613
A wearable aptamer nanobiosensor for non-invasive female hormone monitoring
Ye, C., Wang, M., Min, J., Tay, R. Y., Lukas, H., Sempionatto, J. R., Li, J., Xu, C., & Gao, W. (2024) · Nature Nanotechnology · doi:10.1038/s41565-023-01513-0 · Cited by 466
Self-citationA wireless patch for the monitoring of C-reactive protein in sweat
Tu, J., Min, J., Song, Y., Xu, C., Li, J., Moore, J., Hanson, J., Hu, E., Parimon, T., Wang, T.-Y., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01059-5 · Cited by 395
Self-citationAll-printed soft human-machine interface for robotic physicochemical sensing
Yu, Y., Li, J., Solomon, S. A., Min, J., Tu, J., Guo, W., Xu, C., Song, Y., & Gao, W. (2022) · Science Robotics · doi:10.1126/scirobotics.abn0495 · Cited by 365
Self-citationBrümmer, L. E., Katzenschlager, S., Gaeddert, M., Erdmann, C., Schmitz, S., Bota, M., Grilli, M., Larmann, J., Weigand, M. A., Pollock, N. R., et al. (2021) · PLOS Medicine · doi:10.1371/journal.pmed.1003735 · Cited by 298
Metadata
- Authors
- Torrente-Rodríguez, R. M., Lukas, H., Tu, J., Min, J., Yang, Y., Xu, C., Rossiter, H. B., & Gao, W.
- Venue
- Matter
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 3
- Issue
- 6
- Pages
- 1981-1998
- ISSN
- 2590-2385 (ISSN-L)
- OpenAlex
- W3091281260
- Cited by
- 490
- References
- 58
- FWCI
- 11.50
- Type
- journal article
- Language
- en
- Open access
- bronze
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: SARS-CoV-2 RapidPlex: A Graphene-Based Multiplexed Telemedicine Platform for Rapid and Low-Cost COVID-19 Diagnosis and Monitoring. https://app.cochat.ai/papers/10.1016/j.matt.2020.09.027
- The paper
- Torrente-Rodríguez, R. M., Lukas, H., Tu, J., Min, J., Yang, Y., Xu, C., Rossiter, H. B., & Gao, W. (2020). SARS-CoV-2 RapidPlex: A Graphene-Based Multiplexed Telemedicine Platform for Rapid and Low-Cost COVID-19 Diagnosis and Monitoring. Matter. https://doi.org/10.1016/j.matt.2020.09.027
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.
