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Huang X, Sharma R, Sivakumar AD, Yang S, Fan X. Ultrathin Silica Integration for Enhancing Reliability of Microfluidic Photoionization Detectors. Anal Chem 2023; 95:8496-8504. [PMID: 37278057 DOI: 10.1021/acs.analchem.3c00354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Microfluidic photoionization detectors (μPIDs) based on silicon chips can rapidly and sensitively detect volatile compounds. However, the applications of μPID are limited by the manual assembly process using glue, which may outgas and clog the fluidic channel, and by the short lifetime of the vacuum ultraviolet (VUV) lamps (especially, argon lamps). Here, we developed a gold-gold cold welding-based microfabrication process to integrate ultrathin (10 nm) silica into μPID. The silica coating enables direct bonding of the VUV window to silicon under amicable conditions and works as a moisture and plasma exposure barrier for VUV windows that are susceptible to hygroscopicity and solarization. Detailed characterization of the silica coating was conducted, showing that the 10 nm silica coating allows 40-80% VUV transmission from 8.5 to 11.5 eV. It is further shown that the silica-protected μPID maintained 90% of its original sensitivity after 2200 h of exposure to ambient (dew point = 8.0 ± 1.8 °C), compared to 39% without silica. Furthermore, argon plasma inside an argon VUV lamp was identified as the dominant degradation source for the LiF window with color centers formation in UV-vis and VUV transmission spectra. Ultrathin silica was then also demonstrated effective in protecting the LiF from argon plasma exposure. Lastly, thermal annealing was found to bleach the color centers and restore VUV transmission of degraded LiF windows effectively, which will lead to future development of a new type of VUV lamp and the corresponding μPID (and PID in general) that can be mass produced with a high yield, a longer lifetime, and better regenerability.
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Affiliation(s)
- Xiaheng Huang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ruchi Sharma
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Anjali Devi Sivakumar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Shuo Yang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Xudong Fan
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
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Sharma R, Zang W, Tabartehfarahani A, Lam A, Huang X, Sivakumar AD, Thota C, Yang S, Dickson RP, Sjoding MW, Bisco E, Mahmood CC, Diaz KM, Sautter N, Ansari S, Ward KR, Fan X. Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant. JAMA Netw Open 2023; 6:e230982. [PMID: 36853606 PMCID: PMC9975913 DOI: 10.1001/jamanetworkopen.2023.0982] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/12/2023] [Indexed: 03/01/2023] Open
Abstract
Importance Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown. Objective To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent. Design, Setting, and Participants This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography. Main Outcomes and Measures Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness. Results Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy. Conclusions and Relevance The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.
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Affiliation(s)
- Ruchi Sharma
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Wenzhe Zang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Ali Tabartehfarahani
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Andres Lam
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Xiaheng Huang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Anjali Devi Sivakumar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Chandrakalavathi Thota
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Shuo Yang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Robert P. Dickson
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Internal Medicine, Division of Pulmonary Critical Care Medicine, University of Michigan, Ann Arbor
| | - Michael W. Sjoding
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Internal Medicine, Division of Pulmonary Critical Care Medicine, University of Michigan, Ann Arbor
| | - Erin Bisco
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Carmen Colmenero Mahmood
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Kristen Machado Diaz
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Nicholas Sautter
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Sardar Ansari
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Kevin R. Ward
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Xudong Fan
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
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