Hardware- and software-based selectivity enhancement of gas nanosensors


Trends in Nanotechnology International Conference (TNT2022) | event contribution
Oct. 3, 2022 | Tirana, Albania

Gas and volatile organic compound detection in exhaled breath is an emerging diagnostic technique.
Breath can contain thousands of such compounds, usually at ultralow levels, making it necessary to
develop highly sensitive and selective detection tools. Carbon nanomaterial-based chemiresistors
outperform the traditional metal-oxide sensors in terms of sensitivity at room-temperature, [1] which
is convenient toward the miniaturization in point-of-care diagnostic applications. However, selectivity
is a known issue still to be addressed and in the focus of current research efforts.[2] Two approaches
are promising and compatible with each other to contribute substantially in the improvement of the
selectivity. The first of them consists on the surface engineering of the sensing nanomaterial by
including elements of known chemical affinity toward the target gas. We describe how the
functionalization of carbon nanotubes with gold nanoparticles (Figure 1a) results in improved affinity
toward hydrogen sulfide, a known marker of small intestinal bacteria overgrowth. A self-validation
device results from the integration of multiple of such sensors in a single chip.[3] The second
approach is based on the implementation of machine learning algorithms to maximize the
information obtained from a single sensor. We report how its use with a graphene-based device helps
to distinguish ammonia and phosphine, gases that interact similarly with graphene, by deeply
analyzing their interaction kinetics (Figure 1b). [4] We predict that combining these two approaches
can be a key step in the realization of novel, smart gas sensing platforms of high analytical efficiency.

References
[1] Pallvi Dariyal, Sushant Sharma, Gaurav Singh Chauhan, Bhanu Pratap Singh, Sanjay R. Dhakate,
Nanoscale Adv., 3 (2021), 6514-6544
[2] Indah Raya, Hamzah H. Kzar, Zaid Hameed Mahmoud, Alim Al Ayub Ahmed, Aygul Z. Ibatova, Ehsan
Kianfar, Carbon Lett. 32 (2022), 339–364
[3] Luis Antonio Panes-Ruiz, Leif Riemenschneider, Mohamad Moner Al Chawa, Markus Löffler, Bernd
Rellinghaus, Ronald Tetzlaff, Viktor Bezugly, Bergoi Ibarlucea, Gianaurelio Cuniberti, Nano Research 15
(2022), 2512–2521
[4] Shirong Huang, Alexander Croy, Luis Antonio Panes-Ruiz, Vyacheslav Khavrus, Viktor Bezugly, Bergoi
Ibarlucea, Gianaurelio Cuniberti, Adv. Intell. Syst. 4 (2022), 2200016


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Hardware- and software-based selectivity enhancement of gas nanosensors


Trends in Nanotechnology International Conference (TNT2022) | event contribution
Oct. 3, 2022 | Tirana, Albania

Gas and volatile organic compound detection in exhaled breath is an emerging diagnostic technique.
Breath can contain thousands of such compounds, usually at ultralow levels, making it necessary to
develop highly sensitive and selective detection tools. Carbon nanomaterial-based chemiresistors
outperform the traditional metal-oxide sensors in terms of sensitivity at room-temperature, [1] which
is convenient toward the miniaturization in point-of-care diagnostic applications. However, selectivity
is a known issue still to be addressed and in the focus of current research efforts.[2] Two approaches
are promising and compatible with each other to contribute substantially in the improvement of the
selectivity. The first of them consists on the surface engineering of the sensing nanomaterial by
including elements of known chemical affinity toward the target gas. We describe how the
functionalization of carbon nanotubes with gold nanoparticles (Figure 1a) results in improved affinity
toward hydrogen sulfide, a known marker of small intestinal bacteria overgrowth. A self-validation
device results from the integration of multiple of such sensors in a single chip.[3] The second
approach is based on the implementation of machine learning algorithms to maximize the
information obtained from a single sensor. We report how its use with a graphene-based device helps
to distinguish ammonia and phosphine, gases that interact similarly with graphene, by deeply
analyzing their interaction kinetics (Figure 1b). [4] We predict that combining these two approaches
can be a key step in the realization of novel, smart gas sensing platforms of high analytical efficiency.

References
[1] Pallvi Dariyal, Sushant Sharma, Gaurav Singh Chauhan, Bhanu Pratap Singh, Sanjay R. Dhakate,
Nanoscale Adv., 3 (2021), 6514-6544
[2] Indah Raya, Hamzah H. Kzar, Zaid Hameed Mahmoud, Alim Al Ayub Ahmed, Aygul Z. Ibatova, Ehsan
Kianfar, Carbon Lett. 32 (2022), 339–364
[3] Luis Antonio Panes-Ruiz, Leif Riemenschneider, Mohamad Moner Al Chawa, Markus Löffler, Bernd
Rellinghaus, Ronald Tetzlaff, Viktor Bezugly, Bergoi Ibarlucea, Gianaurelio Cuniberti, Nano Research 15
(2022), 2512–2521
[4] Shirong Huang, Alexander Croy, Luis Antonio Panes-Ruiz, Vyacheslav Khavrus, Viktor Bezugly, Bergoi
Ibarlucea, Gianaurelio Cuniberti, Adv. Intell. Syst. 4 (2022), 2200016


Related groups

Related projects

Related publications