Department of Chemical and Petroleum Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15261, USA.
Department of Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15261, USA.
Sensors (Basel). 2020 Feb 10;20(3):924. doi: 10.3390/s20030924.
Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through over 1.125 × 10 unique array combinations to identify optimal arrays for the detection of CO in air. We found that despite individual MOFs having lower selectivity for O or N relative to CO, intelligently selecting the right combinations of MOFs enables accurate prediction of the concentrations of all components in the mixture (i.e., CO, O, N). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we found that an array whose MOFs have diversity in their volumetric surface areas has improved sensing. Consistent with this observation, we found that the best arrays consistently had greater structural diversity (e.g., pore sizes, void fractions, and surface areas) than the worst arrays.
气体传感器阵列，也被称为电子鼻，利用多种材料来识别复杂气体混合物的成分。金属-有机骨架（MOFs）由于其高比表面积以及化学和结构可调变性，已成为电子鼻有前途的材料。使用我们最近报道的遗传算法设计方法，我们研究了一组 50 个 MOFs，并搜索了超过 1.125×10^5 个独特的阵列组合，以确定用于空气中 CO 检测的最佳阵列。我们发现，尽管单个 MOFs 对 O 或 N 的选择性相对 CO 较低，但智能选择正确的 MOF 组合可以准确预测混合物中所有成分（即 CO、O、N）的浓度。我们还分析了阵列中元素的物理性质，以开发对提高阵列设计的直觉。值得注意的是，我们发现，其比体积表面积具有多样性的 MOFs 的阵列具有改进的传感性能。与这一观察结果一致，我们发现，最佳的阵列始终比最差的阵列具有更大的结构多样性（例如孔径、空隙率和表面积）。
