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

基于理论的纳米结构叶脉网络用于选择性 VOC 传感

• 批准号: EP/W024284/1
• 负责人: Tawfique Hasan
• 金额: $ 53.56万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW024284%2F1
• 关键词: Design; theory; based; nanostructured; leaf

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.

室内空气质量监测对保障英国儿童和脆弱成年人的健康的重要性怎么强调都不为过。室内空气污染的一个主要来源是日常家居产品和材料。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。即使在极小的浓度下，这些特定的化合物在长期的低水平暴露中也会导致各种呼吸、神经和内分泌紊乱。然而，目前的环境传感器，包括由主要半导体集成器件制造商和专业气体传感器制造商商业化的传感器(例如，博世Sensortech、Sensirion、AMS和其他公司)无法在可接受的浓度水平专门检测到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传感器所面临的挑战源于对常见传感器架构的传感膜进行了经验优化。这种方法有很大的缺点，因为它没有对气体或分析物通过传感材料的最佳渗透性进行总体设计考虑，以实现最大限度的响应。关键是，这些传感器是非特异性的，只能检测VOCs的总浓度(TVOCs)，即空气中存在的一部分空气中VOCs的总浓度，作为室内空气质量的整体衡量标准。然而，不同的TVOC测量方法取决于VOCs的混合物，并可能产生显著不同的TVOC估计浓度。值得注意的是，各个VOCs的毒性阈值相差几个数量级；因此，总浓度不能提供任何有用的总毒性衡量标准。我们将设计材料构建块，以提供对目标气体分子的最大和选择性响应，以应对这一挑战。然后，在雄心勃勃的一步中，通过溶液阶段的添加制造技术，我们将创建这些构建块的大规模自组装，以获得模仿植物木质部和叶脉中发现的长度鳞片层次结构的纳米和微观级结构。由于有多层相互连接的通道，这种通用结构经过数百万年的演变，通过保持体积流量来确保质量传输(即流体渗透)，并将能量消耗降至最低。因此，我们的方法将允许高度优化的气体通流到工程构建块，提供对这些有毒气体的快速、高度敏感和选择性的反应。我们附加制造的带有自组装模块的传感薄膜具有高度可重复性的特性，将实现前所未有的设备到设备的一致性。我们将利用这一点来创建新一代训练算法，以显著减少传统传感器的训练时间和成本。我们设想，我们基于自然规律的材料设计和制造路线将提供最先进的有毒VOC传感器性能的X10到X100倍，使室内空气质量监测变得负担得起和可靠。

项目成果

Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments

• DOI: 10.1126/sciadv.adk6856
• 发表时间: 2023-09
• 期刊: Science Advances
• 影响因子: 13.6
• 作者: Zhuo Chen;Binghang Zhou;Mingfei Xiao;T. Bhowmick;Padmanathan Karthick Kannan;L. Occhipinti;J. Gardner-J.-Gard

Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments.

使用气凝胶长丝在室温下进行实时、噪声和漂移弹性甲醛传感。

• DOI: 10.17863/cam.105388
• 发表时间: 2024
• 作者: Chen Z

EMG-Based Human Motion Analysis: A Novel Approach Using Towel Electrodes and Transfer Learning

• DOI: 10.1109/jsen.2024.3354307
• 发表时间: 2024-03
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Chenyu Tang;Wentian Yi;Sanjeev Kumar;Gurvinder S. Virk;L. Occhipinti

Universal Murray's law for optimised fluid transport in synthetic structures

用于优化合成结构中流体传输的通用默里定律

• DOI: 10.48550/arxiv.2309.16567
• 发表时间: 2023
• 作者: Zhou B