| 1. |
- Domènech-Gil, Guillem, Mr. Doctor, et al.
(författare)
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Efficient Methane Monitoring with Low-Cost Chemical Sensorsand Machine Learning
- 2024
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Konferensbidrag (refereegranskat)abstract
- We present a method to monitor methane at atmospheric concentrations with errors inthe order of tens of parts per billion. We use machine learning techniques and periodic calibrationswith reference equipment to quantify methane from the readings of an electronic nose. The resultsobtained demonstrate versatile and robust solution that outputs adequate concentrations in a varietyof different cases studied, including indoor and outdoor environments with emissions arising fromnatural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-costsensor system networks for greenhouse gas monitoring.
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| 2. |
- Domènech-Gil, Guillem, Mr. Doctor, et al.
(författare)
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Electronic Nose for Improved Environmental Methane Monitoring
- 2024
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Ingår i: Environmental Science and Technology. - : AMER CHEMICAL SOC. - 0013-936X .- 1520-5851. ; 58, s. 352-361
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Tidskriftsartikel (refereegranskat)abstract
- Reducing emissions of the key greenhouse gas methane (CH4) is increasingly highlighted as being important to mitigate climate change. Effective emission reductions require cost-effective ways to measure CH4 to detect sources and verify that mitigation efforts work. We present here a novel approach to measure methane at atmospheric concentrations by means of a low-cost electronic nose strategy where the readings of a few sensors are combined, leading to errors down to 33 ppb and coefficients of determination, R-2, up to 0.91 for in situ measurements. Data from methane, temperature, humidity, and atmospheric pressure sensors were used in customized machine learning models to account for environmental cross-effects and quantify methane in the ppm-ppb range both in indoor and outdoor conditions. The electronic nose strategy was confirmed to be versatile with improved accuracy when more reference data were supplied to the quantification model. Our results pave the way toward the use of networks of low-cost sensor systems for the monitoring of greenhouse gases.
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| 3. |
- Casalinuovo, Silvia, et al.
(författare)
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Questioning Breath: A Digital Dive into CO2 Levels
- 2024
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Konferensbidrag (refereegranskat)abstract
- This work presents a smart mask for real-time monitoring of carbon dioxide (CO2) levels asa reference tool for diagnosis, sports training and mental health status. A printed circuit board wasprojected and fabricated to gain data with real-time visualization and storage on a database, enablingremote monitoring as a needed skill for telemedicine purposes. The electronics were inserted in awearable device—shaped like a mask—and 3D-printed with biocompatible materials. The wholedevice was used for analyzing CO2 on a breath volunteer in three kinds of measurement.
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| 4. |
- Domènech-Gil, Guillem, Mr. Doctor, et al.
(författare)
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Machine Learning for Enhanced Operation of UnderperformingSensors in Humid Conditions
- 2024
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Konferensbidrag (refereegranskat)abstract
- Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaininggood results with underperforming sensors that, at first glance, would be discarded. For this aim, wecharacterized chemical gas sensors with low repeatability and random drift towards both dangerousand innocuous volatile organic compounds (VOCs) under different levels of relative humidity. Ourresults show classification accuracies higher than 90% when differentiating harmful from harmlessVOCs and coefficients of determination, R2, higher than 80% when determining their concentrationin the parts per billion to parts per million range.
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| 5. |
- Eriksson, Jens, 1979-, et al.
(författare)
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Electronic Nose for Early Diagnosis of Ovarian Cancer
- 2024
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Konferensbidrag (refereegranskat)abstract
- We present an electronic nose that detects ovarian cancer based on gas emissions from blood plasma. There is currently no test available for screening or diagnostic testing of this disease, whichis therefore often detected at aa late stage, resulting in a poor prognosis. Our approach correctly detected 85 out of 87 ovarian cancers, ranging from borderline to stage IV.
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