# Computational Models for Smart CO2 Monitoring > **NIH NIH R21** · MICHIGAN STATE UNIVERSITY · 2024 · $374,378 ## Abstract Project Summary Among the vital signs of human health, respiratory parameters are key indicators of the physiological status of the human body. Particularly important to clinicians is the ability to quantify the real-time dynamics and physio- logical distribution of blood gas measurements of oxygen (O2) and carbon dioxide (CO2), which provide them with an understanding of the mechanisms associated with both pathological and normal physiological conditions. The accurate diagnosis of respiratory diseases requires a measure of the partial pressure of arterial oxygen (PaO2) and the arterial partial pressure of carbon dioxide (PaCO2), called blood gases. The determination of blood gases requires an arterial blood sample, an invasive and painful process. This procedure, however, pro- vides only a discrete measurement of respiratory efficacy during a rapidly changing situation. Transcutaneous monitoring is a noninvasive method of continuously measuring the transcutaneous partial pressures of O2 and CO2 (PtcO2 and PtcCO2) diffused through the skin, and any changes they undergo correlate closely with changes in PaO2 and PaCO2. The contemporary methodology for measuring PtcO2 and PtcCO2 requires a heated sensor and a costly non-portable, bulky, corded sensing unit. The goal of the proposed research is to develop a computational modeling framework for a miniaturized, noninvasive, wireless, luminescence- based carbon dioxide sensing wearable device with embedded computational models that can accurately translate the PtcCO2 to PaO2, a vital clinical parameter. PIs’ groups recently pioneered a new technique that uses CO2-sensitive luminescence film to monitor PtcCO2. It requires neither heating nor direct line-of-sight overcoming all the drawbacks of the electrochemical- and IR- based techniques. Although very promising, PtcCO2’s usage is limited in clinical practice since the previous studies show unreliable correlation between PaCO2 and PtcCO2 measurements. Our approach will differ from current practice by predicting the effects of the factors such as temperature, sensor location, age, sex in order to estimate PaCO2 more accurately. In this research effort, nanoscale bio-electronic components are tightly integrated with computational models of biological processes for more accurate and personalized interpretation of measurements in the noisy environment of the human body. In Specific Aim 1, we will develop computational models of carbon dioxide transport from capillaries through skin tissue layers using finite element analysis and develop a physics-based estimation algorithm that can be implemented in a wearable. In Specific Aim 2, we will develop the transcutaneous CO2 wearable with an embed- ded lightweight estimation algorithm based on computational models developed in Specific Aim 1 to facilitate the real-time operation of the device. In Specific Aim 3, we will conduct a human pilot study in a lab setting to validate our proposed monitoring system agai... ## Key facts - **NIH application ID:** 10952464 - **Project number:** 1R21EB036329-01 - **Recipient organization:** MICHIGAN STATE UNIVERSITY - **Principal Investigator:** Ulkuhan Guler - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $374,378 - **Award type:** 1 - **Project period:** 2024-09-01 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10952464 ## Citation > US National Institutes of Health, RePORTER application 10952464, Computational Models for Smart CO2 Monitoring (1R21EB036329-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10952464. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*