Department of Chemical & Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
ACS Sens. 2021 Dec 24;6(12):4425-4434. doi: 10.1021/acssensors.1c01808. Epub 2021 Dec 2.
The diverse chemical composition of exhaled human breath contains a vast amount of information about the health of the body, and yet this is seldom taken advantage of for diagnostic purposes due to the lack of appropriate gas-sensing technologies. In this work, we apply computational methods to design mass-based gas sensor arrays, often called electronic noses, that are optimized for detecting kidney disease from breath, for which ammonia is a known biomarker. We define combined linear adsorption coefficients (CLACs), which are closely related to Henry's law coefficients, for calculating gas adsorption in metal-organic frameworks (MOFs) of gases commonly found in breath (i.e., carbon dioxide, argon, and ammonia). These CLACs were determined computationally using classical atomistic molecular simulation techniques and subsequently used to design and evaluate gas sensor arrays. We also describe a novel numerical algorithm for determining the composition of a breath sample given a set of sensor outputs and a library of CLACs. After identifying an optimal array of five MOFs, we screened a set of 100 simplified computer-generated, water-free breath samples for kidney disease and were able to successfully quantify the amount of ammonia in all samples within the tolerances needed to classify them as either healthy or diseased, demonstrating the promise of such devices for disease detection applications.
呼气中的人体呼吸所包含的化学成分多种多样，其中蕴含着大量与身体健康状况相关的信息，但由于缺乏适当的气体传感技术，这些信息很少被用于诊断目的。在这项工作中，我们应用计算方法来设计基于质量的气体传感器阵列，通常称为电子鼻，这些传感器阵列经过优化，可以从呼吸中检测出肾脏疾病，而氨是一种已知的生物标志物。我们定义了组合线性吸附系数 (CLAC)，它与亨利定律系数密切相关，用于计算呼吸中常见气体（即二氧化碳、氩气和氨气）在金属有机骨架 (MOF) 中的气体吸附。这些 CLAC 通过使用经典的原子分子模拟技术进行计算，并随后用于设计和评估气体传感器阵列。我们还描述了一种新的数值算法，用于根据一组传感器输出和一组 CLAC 确定呼吸样本的组成。在确定了由五个 MOF 组成的最佳阵列后，我们对一组 100 个简化的计算机生成的无水呼吸样本进行了筛选，以检测肾脏疾病，并能够成功地定量分析所有样本中的氨含量，这些含量足以在需要将其分类为健康或患病的范围内进行分类，这表明此类设备在疾病检测应用方面具有广阔的应用前景。
