CuO nanostructure-decorated InGaN nanorods for selective H2S gas detection.

Establishing a heterostructure is one of the adequate strategies for enhancing device performance and has been explored in sensing, and energy applications. In this study, we constructed a heterostructure through a two-step process involving hydrothermal synthesis of CuO nanostructures and subsequent spin coating on MBE-grown InGaN NRs. We found that the CuO content on the InGaN NRs has a great impact on carrier injection at the heterojunction and thus the H2S gas sensing performance. Popcorn CuO/InGaN NR shows excellent gas sensing performance towards different concentrations of H2S at room temperature. The highest response is up to 35.54% to a H2S concentration of 100 ppm. Even more significantly, this response is further enhanced significantly (123.70%) under 365 nm UV light. In contrast, this composite structure exhibits negligibly low responses to 100 ppm of NO2, H2, CO, and NH3. The heterostructure band model associated with a surface reaction model is manifested to elucidate the sensing mechanism.

Automated Gas Chromatography Peak Alignment: A Deep Learning Approach using Greedy Optimization and Simulation.

The clinical significance of volatile organic compounds (VOC) in detecting diseases has been established over the past decades. Gas chromatography (GC) devices enable the measurement of these VOCs. Chromatographic peak alignment is one of the important yet challenging steps in analyzing chromatogram signals. Traditional semi-automated alignment algorithms require manual intervention by an operator which is slow, expensive and inconsistent. A pipeline is proposed to train a deep-learning model from artificial chromatograms simulated from a small, annotated dataset, and a postprocessing step based on greedy optimization to align the signals.Clinical Relevance- Breath VOCs have been shown to have a significant diagnostic power for various diseases including asthma, acute respiratory distress syndrome and COVID-19. Automatic analysis of chromatograms can lead to improvements in the diagnosis and management of such diseases.

WASP: Wearable Analytical Skin Probe for Dynamic Monitoring of Transepidermal Water Loss.

Early diagnosis of skin barrier dysfunction helps provide timely preventive care against diseases such as atopic dermatitis, psoriasis, food allergies, and other atopic skin disorders. Skin barrier function is commonly evaluated by measuring the transepidermal water loss (TEWL) through stratum corneum due to its noninvasive characteristics. However, existing commercial TEWL devices are significantly affected by many factors, such as ambient temperature, humidity, air flow, water accumulation, initial water contents on the skin surface, bulky sizes, high costs, and requirements for well-controlled environments. Here, we developed a wearable closed-chamber hygrometer-based TEWL device (Wearable Analytical Skin Probe, WASP) and the related algorithm for accurate and continuous monitoring of skin water vapor flux. The WASP uses short dry air purges to dry the skin surface and chamber before each water vapor flux measurement. Its design ensures a highly controlled local environment, such as consistent initial dry conditions for the skin surface and the chamber. We further applied WASP to measure the water vapor flux from six different locations of a small group of human participants. It is found that the WASP can not only measure and distinguish between insensible sweating (i.e., TEWL) and sensible sweating (i.e., thermal sweating) but also track skin dehydration-rehydration cycles. Comparisons with a commercial TEWL device, AquaFlux, show that the results obtained by both devices agree well. The WASP will be broadly applicable to clinical, cosmetic, and biomedical research.

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.

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.