WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 9Citation - Scopus: 11Size, Material Type, and Concentration Estimation for Micro-Particles in Liquid Samples(Elsevier Science SA, 2024-05) Genc, Sinan; Erdem, Talha; Icoz, KutayThe on -site examination and characterization of microparticles are becoming crucial due to the significant rise in plastic pollution in natural resources. Hence, identifying the specific microplastic composition and quantity would enable the implementation of preventive measures. This paper presents a cost-effective setup that utilizes the Random Forest algorithm to detect the size and refractive index of micro particles, hence facilitating the identification of the material type. The system utilizes the scattering patterns of laser light from the dispersion of microparticles, namely within the concentration range of 0.05 fM to 3.00 fM. The refractive indices and particle sizes of melamine (Me8) spheres with a size of 8 mu m, as well as polystyrene (PS8) spheres with a size of 8 mu m and (PS10) 10 mu m, were estimated using the Random Forest algorithm and recorded scattering patterns. The proposed method may deliver findings with an average deviation of 0.23 mu m for particle size and 0.015 for particle refractive index. The statistical analysis indicated that there was no notable disparity between the experimental findings and the predictions derived from the machine learning system. The existing configuration can be readily converted into a point -of -use system that can be employed on -site for the purpose of monitoring and identifying microplastic contamination.Article Citation - WoS: 18Citation - Scopus: 20Respiration Monitoring Using a Paper-Based Wearable Humidity Sensor, a Step Forward to Clinical Tests(Elsevier Science SA, 2023-06) Solak, Irfan; Gencer, Serife; Yildirim, Beyza; Oznur, Emine; Hah, Dooyoung; Icoz, KutayMonitoring respiratory variables can provide valuable information for clinical applications and sport activities. Paper-based wearable respiration monitoring systems have great advantages and potential, they are low-cost, easily disposable, non-invasive and can provide real-time, reliable data. Despite some examples presented for exhaled breath analysis using paper-based sensors exist, none of them have been validated yet in a study involving many patients. In this work, we present a novel paper-based platform for exhaled breath sensors and validate it on 101 subjects including 41 patients to demonstrate its clinical applicability. By using the paperbased wearable capacitive sensors, we collected respiration data from different groups of people, namely, smokers, non-smokers and patients diagnosed with pneumonia, or chronic obstructive pulmonary disease (COPD). The change in humidity during inhale and exhale was converted to capacitance change and thus an electrical signal was obtained. The electrical signal was transmitted to a nearby computer and capacitance versus time data was post-processed. Four ratio parameters were defined on the recorded data; area, rate, maximum amplitude, and average maximum-minimum difference, all of which were compared between deep breathing and normal breathing. The collected data was statistically analyzed, and the humidity changes were compared among different groups. The results show that the developed sensor and the proposed analysis method can be used to detect the humidity changes in breathing, and to differentiate between smokers and non-smokers, and between non-smokers and patients with pulmonary disease.