Evidence map›Paper›PMID 36030295›Full record

ArticleNature communications2022

Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing.

Mantian Xue, Charles Mackin, Wei-Hung Weng, Jiadi Zhu, Yiyue Luo, Shao-Xiong Lennon Luo, Ang-Yu Lu, Marek Hempel, Elaine McVay, Jing Kong and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed.

  1. Review
  2. From Sensing Mechanisms to System Co-Design: Graphene-Based Sensors and Readout Circuits.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Engineering two-dimensional electronic biosensors.Nature reviews electrical engineering · 2026
    Article
  4. Article
  5. Review
  6. Review
  7. Electrochemical in-biosensing computing.National science review · 2026
    Article
  8. AI Methods in Sensor Calibration.Sensors (Basel, Switzerland) · 2026
    Review
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Neuromorphic chips for biomedical engineering.Mechanobiology in medicine · 2025
    Review
  16. Article
  17. Article
  18. Article
  19. Recent Advances in Xenes Based FET for Biosensing Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  20. Advances in 2D materials for wearable biomonitoring.Materials science & engineering. R, Reports : a review journal · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Mantian Xue *Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. mxue@mit.edu.ORCID 0000-0001-5396-2674
Charles Mackin *IBM Research-Almaden, 650 Harry Road, San Jose, CA, USA.ORCID 0000-0001-8413-5583
Wei-Hung WengDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jiadi ZhuDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Yiyue LuoDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Shao-Xiong Lennon LuoDepartment of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-5308-4576
Ang-Yu LuDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-4031-0156
Marek HempelDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Elaine McVayDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jing KongDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0003-0551-1208
Tomás PalaciosDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. tpalacios@mit.edu.ORCID 0000-0002-2190-563X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform  composed of  more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system's functionality and accuracy in ion classification.

Indexed as

Biosensing TechniquesGraphiteElectrolytesElectronicsIonsElectrolytesGraphiteIons

Identifiers

PMID36030295
PMCPMC9420106

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.