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Zaytsev V, Ermatov TI, Fedorov FS, Balabin N, Kapralov PO, Bondareva JV, Ignatyeva DO, Khlebtsov BN, Kosolobov SS, Belotelov VI, Nasibulin AG, Gorin DA. Design of an Artificial Opal/Photonic Crystal Interface for Alcohol Intoxication Assessment: Capillary Condensation in Pores and Photonic Materials Work Together. Anal Chem 2022; 94:12305-12313. [PMID: 36027051 DOI: 10.1021/acs.analchem.2c00573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Alcohol intoxication has a dangerous effect on human health and is often associated with a risk of catastrophic injuries and alcohol-related crimes. A demand to address this problem adheres to the design of new sensor systems for the real-time monitoring of exhaled breath. We introduce a new sensor system based on a porous hydrophilic layer of submicron silica particles (SiO2 SMPs) placed on a one-dimensional photonic crystal made of Ta2O5/SiO2 dielectric layers whose operation relies on detecting changes in the position of surface wave resonance during capillary condensation in pores. To make the active layer of SiO2 SMPs, we examine the influence of electrostatic interactions of media, particles, and the surface of the crystal influenced by buoyancy, gravity force, and Stokes drag force in the frame of the dip-coating preparation method. We evaluate the sensing performance toward biomarkers such as acetone, ammonia, ethanol, and isopropanol and test sensor system capabilities for alcohol intoxication assessment. We have found this sensor to respond to all tested analytes in a broad range of concentrations. By processing the sensor signals by principal component analysis, we selectively determined the analytes. We demonstrated the excellent performance of our device for alcohol intoxication assessment in real-time.
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Affiliation(s)
- Valeriy Zaytsev
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
| | - Timur I Ermatov
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
| | - Fedor S Fedorov
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
| | - Nikita Balabin
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
| | - Pavel O Kapralov
- Russian Quantum Centre, 30 bld. 1 Bolshoy Boulevard, Moscow 121205, Russia
| | - Julia V Bondareva
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
| | - Daria O Ignatyeva
- Russian Quantum Centre, 30 bld. 1 Bolshoy Boulevard, Moscow 121205, Russia.,Lomonosov Moscow State University, Faculty of Physics, Leninskie Gory, Moscow 119991, Russia
| | - Boris N Khlebtsov
- Institute of Biochemistry and Physiology of Plants and Microorganisms, 13 Prospekt Entuziastov, Saratov 410049, Russia.,Saratov State University, 83 Astrakhanskaya Street, Saratov 410012, Russia
| | - Sergey S Kosolobov
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
| | - Vladimir I Belotelov
- Russian Quantum Centre, 30 bld. 1 Bolshoy Boulevard, Moscow 121205, Russia.,Lomonosov Moscow State University, Faculty of Physics, Leninskie Gory, Moscow 119991, Russia
| | - Albert G Nasibulin
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia.,Aalto University, Kemistintie 1, P.O. Box 16100, Aalto 00076, Finland
| | - Dmitry A Gorin
- Skolkovo Institute of Science and Technology, 3 Nobel Street, Moscow 121205, Russia
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Konki SK, Khambampati AK, Sharma SK, Kim KY. A deep neural network for estimating the bladder boundary using electrical impedance tomography. Physiol Meas 2020; 41:115003. [PMID: 32726770 DOI: 10.1088/1361-6579/abaa56] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Accurate bladder size estimation is an important clinical parameter that assists physicians, enabling them to provide better treatment for patients who are suffering from urinary incontinence. Electrical impedance tomography (EIT) is a non-invasive medical imaging method that estimates organ boundaries assuming that the electrical conductivity values of the background, bladder, and adjacent tissues inside the pelvic domain are known a priori. However, the performance of a traditional EIT inverse algorithm such as the modified Newton-Raphson (mNR) for shape estimation exhibits severe convergence problems as it heavily depends on the initial guess and often fails to estimate complex boundaries that require greater numbers of Fourier coefficients to approximate the boundary shape. Therefore, in this study a deep neural network (DNN) is introduced to estimate the urinary bladder boundary inside the pelvic domain. APPROACH We designed a five-layer DNN which was trained with a dataset of 15 subjects that had different pelvic boundaries, bladder shapes, and conductivity. The boundary voltage measurements of the pelvic domain are defined as input and the corresponding Fourier coefficients that describe the bladder boundary as output data. To evaluate the DNN, we tested with three different sizes of urinary bladder. MAIN RESULTS Numerical simulations and phantom experiments were performed to validate the performance of the proposed DNN model. The proposed DNN algorithm is compared with the radial basis function (RBF) and mNR method for bladder shape estimation. The results show that the DNN has a low root mean square error for estimated boundary coefficients and better estimation of bladder size when compared to the mNR and RBF. SIGNIFICANCE We apply the first DNN algorithm to estimate the complex boundaries such as the urinary bladder using EIT. Our work provides a novel efficient EIT inverse solver to estimate the bladder boundary and size accurately. The proposed DNN algorithm has advantages in that it is simple to implement, and has better accuracy and fast estimation.
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Affiliation(s)
- S K Konki
- Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
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