Sirvi Märksõna "gas sensor" järgi

Nüüd näidatakse 1 - 2 2

listelement.badge.access-status Embargo ,
Functionalization of CVD graphene by fs-laser treatment for gas sensing applications
(Tartu Ülikool, 2023) Soosaar, Anna; Berholts, Artjom, juhendaja; Jaaniso, Raivo, juhendaja
Graphene and related 2D materials can be used to construct very sensitive gas sensors. In order to achieve high selectivity for detecting different gases, it is necessary to functionalize these materials. In this work, the functionalization is carried out by femtosecond laser treatment. A new fs-laser treatment set-up at the Institute of Physics was tested, and different laser-processed chemiresistive gas sensor chips were studied. The analysis by Raman spectroscopy revealed that the laser-induced defects were created by a two-photon mechanism. The sensitivity of laser-treated sensor chips made on Si/SiO2 substrates increased up to 10 times to 150 ppb of NO2 gas exposure in the air.
listelement.badge.access-status Avatud juurdepääs ,
Semiquantitative Classification of Two Oxidizing Gases with Graphene-Based Gas Sensors
(MDPI, 2022) Lind, Martin; Kiisk, Valter; Kodu, Margus; Kahro, Tauno; Renge, Indrek; Avarmaa, Tea; Makaram, Prashanth; Zurutuza, Amaia; Jaaniso, Raivo
Miniature and low-power gas sensing elements are urgently needed for a portable electronic nose, especially for outdoor pollution monitoring. Hereby we prepared chemiresistive sensors based on wide-area graphene (grown by chemical vapor deposition) placed on Si/Si3N4 substrates with interdigitated electrodes and built-in microheaters. Graphene of each sensor was individually functionalized with ultrathin oxide coating (CuO-MnO2, In2O3 or Sc2O3) by pulsed laser deposition. Over the course of 72 h, the heated sensors were exposed to randomly generated concentration cycles of 30 ppb NO2, 30 ppb O3, 60 ppb NO2, 60 ppb O3 and 30 ppb NO2 + 30 ppb O3 in synthetic air (21% O2, 50% relative humidity). While O3 completely dominated the response of sensors with CuO-MnO2 coating, the other sensors had comparable sensitivity to NO2 as well. Various response features (amplitude, response rate, and recovery rate) were considered as machine learning inputs. Using just the response amplitudes of two complementary sensors allowed us to distinguish these five gas environments with an accuracy of ~ 85%. Misclassification was mostly due to an overlap in the case of the 30 ppb O3, and 30 ppb O3 + 30 ppb NO2 responses, and was largely caused by the temporal drift of these responses. The addition of recovery rates to machine learning input variables enabled us to very clearly distinguish different gases and increase the overall accuracy to ~94%.