Surface-Functionalized Multichannel Nanosensors and Machine Learning Analysis for Improved Sensitivity and Selectivity in Gas Sensing Applications

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


Breath analysis is an emerging technique in the field of diagnostics. The presence of thousands of gases and volatile organic compounds (VOCs), many of them at part per billion (ppb) concentration levels, require the development of ultrasensitive and selective detection approaches, which pose challenges still trying to be addressed by the scientific community. Here, we describe two approaches that provide a substantial contribution to the development of gas sensors. The first one is based on modifications of the used sensing material, namely a specific surface functionalization based on gold nanoparticles of carbon nanotubes to achieve selectivity toward hydrogen sulfide, together with the implementation of multiple sensors for self-validation. The second one focuses on the analysis method, implementing machine learning algorithms to maximize the information obtained from each single sensor to distinguish gases based on their interaction kinetics with the sensor. The combination of both approaches is foreseen as a powerful tool for the development of new smart sensing platforms with high potential in terms of analytical efficiency.


Original languageEnglish
Title of host publicationAdvances in System-Integrated Intelligence - Proceedings of the 6th International Conference on System-Integrated Intelligence SysInt 2022, Genova, Italy
EditorsMaurizio Valle, Dirk Lehmhus, Christian Gianoglio, Edoardo Ragusa, Lucia Seminara, Stefan Bosse, Ali Ibrahim, Klaus-Dieter Thoben
PublisherSpringer Science and Business Media B.V.
Number of pages8
ISBN (print)9783031162800
Publication statusPublished - 2023

Publication series

SeriesLecture Notes in Networks and Systems
Volume546 LNNS


Title6th International Conference on System-Integrated Intelligence, SysInt 2022
Duration7 - 9 September 2022

External IDs

WOS 000871881800066
unpaywall 10.1007/978-3-031-16281-7_66
ORCID /0000-0002-4349-793X/work/142245515
ORCID /0000-0002-3007-8840/work/142247144
ORCID /0000-0002-9899-1409/work/142249209



  • Breath diagnostics, Gas nanosensors, Machine learning