Detecting Ammonia Using Graphene Sensors

Using their extensive knowledge of graphene and its properties, a partnership of the Massachusetts Institute of Technology (MIT) and Graphenea has developed a sensor array used for sensitive and selective detection of ammonia. The array is comprised of 160 graphene pixels that provide large statistics and thus improved sensing performance. The sensors have been extensively tested under conditions that mimic real-life, which is a step forward to practical use.

Components of Graphene Sensor Array

The sensing aspect of the array consists of two components: a graphene surface and porphyrins. Porphyrins are a class of organic molecules that are highly sensitive, and are also able to be attached to the graphene surface. They also produce minimal deviation to graphene’s outstanding electrical properties. The sensor works through ammonia molecules attaching to the porphyrins, which causes the compound to become a strong dipole that changes electrical properties of the graphene. This change in property is detected as a sign of the presence of ammonia.

Reprinted with permission from ACS Appl. Mater. Interfaces, 2018, 10 (18), pp 16169–16176. Copyright 2018 American Chemical Society.

The graphene sensor array is built as an insertable chip to be used alongside a custom readout system, which is connected to a computer via USB. This system works in tandem with specialized data acquisition software, to perform rapid high-quality readout of data from hundreds of sensors, which is a significant improvement over previous prototype graphene sensor devices. By monitoring such a large number of sensors, performance variations and reproducibility can also be monitored which leads to improvement of sensor array performance.

Reprinted with permission from ACS Appl. Mater. Interfaces, 2018, 10 (18), pp 16169–16176. Copyright 2018 American Chemical Society.

Detection of Ammonia

Crucially, the graphene sensors are shown to be selective – through not reacting with other compounds they can distinguish between ammonia and other molecules such as water, hexane, ethanol, chloroform, or acetonitrile. For ammonia concentrations of 160 ppm the electrical properties of the sensors change by as much as 8%, but even concentrations as small as 20 ppm were successfully detected. These sensors have 4 times the sensitivity of the previously reported pristine graphene sensors.

Although quantification measurements were taken in a dry nitrogen atmosphere, when put in an air environment the specificity and sensitivity to ammonia were shown to remain. This enables robust performance in real use conditions. Individual sensor pixels showed little deviation in performance, allowing reproducibility and mass production.

The findings were published in the journal ACS Applied Materials & Interfaces. The work opens up new avenues for further quality engineering and advancement of this sensing technology.

This information has been sourced, reviewed and adapted from materials provided by Graphenea.

For more information on this source, please visit Graphenea.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Graphenea. (2018, October 01). Detecting Ammonia Using Graphene Sensors. AZoSensors. Retrieved on November 21, 2024 from https://www.azosensors.com/article.aspx?ArticleID=1335.

  • MLA

    Graphenea. "Detecting Ammonia Using Graphene Sensors". AZoSensors. 21 November 2024. <https://www.azosensors.com/article.aspx?ArticleID=1335>.

  • Chicago

    Graphenea. "Detecting Ammonia Using Graphene Sensors". AZoSensors. https://www.azosensors.com/article.aspx?ArticleID=1335. (accessed November 21, 2024).

  • Harvard

    Graphenea. 2018. Detecting Ammonia Using Graphene Sensors. AZoSensors, viewed 21 November 2024, https://www.azosensors.com/article.aspx?ArticleID=1335.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.