Design theory-based nanostructured leaf-vein networks for selective VOC sensing

室内空气质量监测对保护英国儿童和脆弱成年人健康的重要性怎么强调都不为过。室内空气污染的主要来源是日常生活用品和材料。许多排放有害的非甲烷挥发性有机化合物（VOCs），如甲醛、甲苯和邻苯二甲酸盐。即使浓度极低，这些特定的化合物在长时间低水平暴露后也会诱发各种呼吸、神经和内分泌紊乱。然而，目前的环境传感器，包括那些由主要的半导体集成设备制造商和专业气体传感器制造商商业化的传感器。Bosch Sensortech, Sensirion， AMS等)无法在可接受的浓度水平下专门检测这些不同的有毒气体，也无法提供任何有用的预防指导。当前一代低成本VOC传感器面临的挑战来自于针对常见传感器架构的经验优化的传感膜。这种方法有很强的缺点，因为它没有对气体或分析物通过传感材料的最佳渗透进行总体设计，以获得最大的响应。至关重要的是，这些传感器是非特异性的，只能检测挥发性有机化合物（TVOCs）的总浓度，即空气中存在的一组空气中挥发性有机化合物的总浓度，作为室内空气质量的总体衡量标准。然而，不同的TVOC测量方法取决于VOCs的混合物，可能产生很大不同的TVOC估计浓度。值得注意的是，不同挥发性有机化合物的毒性阈值存在数量级差异；因此，总浓度不能提供任何有用的总毒性量度。我们将设计材料构建模块，为目标气体分子提供最大和选择性的响应，以应对这一挑战。然后，在一个雄心勃勃的步骤中，通过溶液阶段的增材制造技术，我们将创建这些构建块的大规模自组装，以获得纳米和微观水平的结构，模拟植物木质部和叶脉的长度尺度等级。通过多层相互连接的通道，这种普遍的结构已经进化了数百万年，通过保持体积流速，以最小的能量消耗来确保质量运输（即流体渗透）。因此，我们的方法将允许高度优化的气体通流到工程构建模块，对这些有毒气体提供快速，高度敏感和选择性的响应。具有自组装块的增材制造传感薄膜的高度可重复性将实现前所未有的设备到设备的均匀性。我们将利用这一点来创建新一代的训练算法，以显着减少传统传感器的训练时间和成本。我们设想基于自然规律的材料设计和制造途径将提供最先进的有毒VOC传感器性能的10到100倍，使室内空气质量监测负担得起且可靠。

The importance of indoor air quality monitoring to safeguard the health of children and vulnerable adults in the UK cannot be overstated. A primary source of indoor air pollution is everyday household products and materials. Many emit harmful non-methane volatile organic compounds (VOCs), such as formaldehyde, toluene and phthalates. Even in minute concentrations, these specific compounds can induce a variety of respiratory, neurological, endocrine disorders over prolonged low-level exposures. However, current environmental sensors, including those commercialised by major semiconductor integrated device manufacturers and by specialised gas sensor manufacturers (e.g . Bosch Sensortech, Sensirion, AMS, and others), cannot specifically detect these different toxic gases at an acceptable concentration level and are unable to provide any helpful preventive guidance.The challenges faced by current-generation low-cost VOC sensors arise from empirically optimised sensing films for common sensor architectures. This approach has strong drawbacks as it does not have an overarching design consideration for the optimum permeation of gases or analytes through the sensing material for a maximised response. Crucially, these sensors are non-specific and can only detect the total concentration of VOCs (TVOCs), i.e. the total concentration of a subset of airborne VOCs present in the air, as an overall measure of indoor air quality. However, different TVOC measurement methods depend on VOCs' mixture and can yield substantially different estimated TVOC concentrations. Notably, the toxicity thresholds of the individual VOCs differ by orders of magnitude; the total concentration, therefore, does not provide any useful measure of total toxicity. We will design material building blocks engineered to offer a maximum and selective response to target gas molecules to address this challenge. Then, in an ambitious step, through solution-phase additive manufacturing techniques, we will create large-scale self-assembly of these building blocks to obtain a nano- and micro-level structure mimicking the hierarchy of length scales found in xylems and leaf veins in plants. With multiple levels of interconnected channels, this universal structure has evolved over many million years to ensure mass transport (i.e. fluid permeation) with minimum energy expenditure through the preservation of volumetric flow rate. Our approach will therefore allow highly optimum through-flow of gases to the engineered building blocks, providing a fast, highly sensitive and selective response to these toxic gases. The highly repeatable nature of our additively manufactured sensing thin-film with self-assembled blocks will enable unprecedented device-to-device uniformity. We will exploit this to create a new generation of training algorithms to significantly reduce the traditional sensor training time and cost. We envisage that our materials design and manufacturing pathway based on natural laws will offer x10 to x100 times the state-of-the-art toxic VOC sensors' performance, making indoor air quality monitoring affordable and reliable.