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This paper presents sensor nanotechnologies that can be used for the skin-based gas “smelling” of disease. Skin testing may provide rapid and reliable results, using specific “fingerprints” or unique patterns for a variety of diseases and conditions. These can include metabolic diseases, such as diabetes and cholesterol-induced heart disease; neurological diseases, such as Alzheimer’s and Parkinson’s; quality of life conditions, such as obesity and sleep apnea; pulmonary diseases, such as cystic fibrosis, asthma, and chronic obstructive pulmonary disease; gastrointestinal tract diseases, such as irritable bowel syndrome and colitis; cancers, such as breast, lung, pancreatic, and colon cancers; infectious diseases, such as the flu and COVID-19; as well as diseases commonly found in ICU patients, such as urinary tract infections, pneumonia, and infections of the blood stream. Focusing on the most common gaseous biomarkers in breath and skin, which is nitric oxide and carbon monoxide, and certain abundant volatile organic compounds (acetone, isoprene, ammonia, alcohols, sulfides), it is argued here that effective discrimination between the diseases mentioned above is possible, by capturing the relative sensor output signals from the detection of each of these biomarkers and identifying the distinct breath print for each disease.
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AI‐Assisted Multimodal Breath Sensing System with Semiconductive Polymers for Accurate Monitoring of Ammonia Biomarkers
Abstract Breath ammonia is an essential biomarker for patients with many chronic illnesses, such as chronic kidney disease (CKD), chronic liver disease (CLD), urea cycle disorders (UCD), and hepatic encephalopathy. However, existing breath ammonia sensors fail to compensate for the impact of breath humidity and complex breathing motions associated with a human breath sample. Here, a multimodal breath sensing system is presented that integrates an ammonia sensor based on a thermally cleaved conjugated polymer, a humidity sensor based on reduced graphene oxide (rGO), and a breath dynamics sensor based on a 3D folded strain‐responsive mesostructure. The miniaturized construction and module‐based configuration offer flexible integration with a broad range of masks. Experimental results present the capabilities of the system in continuously detecting diagnostic ranges of breath ammonia under real, humid breath conditions with sufficient sensing accuracy and selectivity over 3 weeks. A machine‐learning algorithm based on K‐means clustering decodes multimodal signals collected from the breath sensor to differentiate between healthy and diseased breath concentrations of ammonia. The on‐body test highlights the operational simplicity and practicality of the system for noninvasively tracing ammonia biomarkers.
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- Award ID(s):
- 2139659
- PAR ID:
- 10641384
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Technologies
- Volume:
- 9
- Issue:
- 9
- ISSN:
- 2365-709X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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