Computational Design of MOF-Based Electronic Noses for Dilute Gas Species Detection: Application to Kidney Disease Detection
Computational Design of MOF-Based Electronic Noses for Dilute Gas Species Detection: Application to Kidney Disease Detection
复制标题
用于稀气体种类检测的基于 MOF 的电子鼻的计算设计：在肾脏疾病检测中的应用
DOI：
10.1021/acssensors.1c01808
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发表时间：
2021
期刊：
影响因子：
8.9
通讯作者：
Wilmer, Christopher E.
中科院分区：
文献类型：
--
作者：
Day, Brian A.;Wilmer, Christopher E.
呼气的各种化学成分包含大量关于身体健康的信息，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，通常被称为电子鼻，它被优化为从呼气中检测肾脏疾病，氨是已知的生物标记物。我们定义了与亨利定律系数密切相关的组合线性吸附系数(CLACs)，用于计算呼吸中常见气体(即二氧化碳、Ar和氨)在金属-有机骨架(MOF)中的气体吸附。使用经典原子分子模拟技术通过计算确定这些CLA，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，用于在给定一组传感器输出和一个CLA库的情况下确定呼吸样本的组成。在确定了五个MOF的最佳阵列后，我们筛选了一组100个简化的计算机生成的、无水的肾脏疾病呼吸样本，并能够成功地将所有样本中的氨量量化在将其归类为健康或疾病所需的容差内，展示了此类设备在疾病检测应用中的前景。
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.