Convergence Accelerator Workshop - Chemical sensing with an olfaction analogue: high-dimensional, bio-inspired sensing and computation

融合加速器研讨会 - 具有嗅觉模拟的化学传感：高维、仿生传感和计算

• 批准号: 2231512
• 负责人: Ricardo Gutierrez-Osuna
• 金额: $ 10万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231512&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Workshop; Chemical; sensing

Biological olfactory systems are the most exquisite chemical detectors on the planet. Yet, unlike other sensory systems (e.g., vision, hearing), whose artificial analogues have revolutionized society (e.g., image and speech recognition), mimicking olfaction remains a grand challenge. This is in part due to the fact that, to recognize odors, biological noses use a large number of sensors –an estimated 400 sensors in humans, each sensor detecting different properties of odor molecules. Building an odor-detection instrument with such high number and diversity of sensors has not been possible to date. But this is changing. New sensing technologies have emerged over the past decade that make it possible to develop large and diverse arrays of chemical sensors. Promising technologies include those that use the same sensors as biological noses, miniaturization techniques such as nano-photonic and micro-electromechanical systems, and sensors that can detect multiple properties of an odor molecule. In parallel, there have been significant advances in algorithms to mimic odor processing in the biological olfactory system, and in neuro-morphic hardware to efficiently process large amounts of data. In concert, these advances offer tremendous potential for new breakthroughs in artificial olfaction and chemical sensing that until recently were technically implausible. However, this requires bringing together a diverse group of experts who currently work largely independently across disciplines (e.g., biochemistry, perception, bioengineering, machine learning, healthcare, manufacturing). To address this issue, a cross-disciplinary team of researchers will organize an interactive workshop to (1) discuss opportunities from emerging sensing and computing technologies that are ready to be turned into products and applications, and (2) identify application areas in which they can have the greatest societal impact, such as environmental monitoring, healthcare, quality control of food and beverages, precision agriculture, and homeland security. The goal of this workshop is to create a comprehensive 3-year roadmap for translational research in artificial chemo-sensory systems that may lead to new products and applications. The research team will collaborate with a facilitator company to design a 4-day workshop with a variety of convergent and divergent activities, to be performed in small groups and individually. Through these activities, participants will be prompted to define specifications for artificial chemosensory systems, identify the most promising sensing technologies that are ready for translation, and applications and end-user communities to engage. Throughout the workshop, participants will be divided into small breakouts for group exercises. These groups may be formed randomly or engineered based on some factor of importance (e.g., diversity of expertise, career stage, sector). Depending on the activity, it may be beneficial to place participants in breakout based on commonality vs diversity. The research team and the facilitation team will work together to determine, for each activity, the best way of organizing participants. The workshop will be structured into a series of steps to “define” the need, “identify the solution” and then “target applications.” In Step 1, participants will develop key requirements and specifications for the chemical sensors and sensor systems of the future. In Step 2, participants will identify potential approaches in five areas: sample delivery, biological sensors, micro-analytical instruments, multiparameter sensors, and computational analysis. Finally, in State 3, participants will identify application areas where this technology could have societal impact in 2-, 5- and 10-year timeframes, as well as identify those in the end-user communities who should be engaged in the short, medium and long term. Participants will be recruited from four major stakeholders: industry, non-profit organizations, government organizations, and academia, to cover a broad range of disciplines, including basic science (materials science, biochemistry, sensory perception, neuroscience, analytical chemistry), engineering (bio- and neuromorphic engineering, instrumentation, machine learning), applications (healthcare, environment, quality control, agriculture, military, security), as well as product design and manufacturing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

然而，与其他感官系统（例如视觉、听觉）不同，生物嗅觉系统的人工模拟已经彻底改变了社会（例如图像和语音识别），模仿嗅觉仍然是一个巨大的挑战。部分原因在于，为了识别气味，生物鼻子使用了大量传感器——估计人类有 400 个传感器，每个传感器检测气味分子的不同特性。迄今为止，还不可能拥有如此大量和多样化的传感器的气味检测仪器，但这种情况正在改变，过去十年中出现了新的传感技术，使得开发大量多样化的化学传感器成为可能。与此同时，模拟气味处理的算法也取得了重大进展。生物学嗅觉系统，以及有效处理大量数据的神经形态，这些进步为人工嗅觉和化学传感方面的新突破提供了巨大的潜力，而这在技术上是不可能实现的，然而，这需要将不同的硬件组合在一起。目前基本上独立工作的跨学科专家（例如生物化学、感知、生物工程、机器学习、医疗保健、制造）为了解决这个问题，一个跨学科的研究人员团队将组织一个互动研讨会，以 (1) 讨论机会。新兴传感和计算技术已准备好转化为产品和应用，并且 (2) 确定它们可以产生最大社会影响的应用领域，例如环境监测、医疗保健、食品和饮料质量控制、精准农业该研讨会的目标是为人工化学传感系统的转化研究制定一个全面的 3 年路线图，这可能会带来新的产品和应用。研究团队将与一家促进公司合作设计 4 个项目。为期一天的研讨会，内容丰富聚合和发散的活动，以小组和单独的形式进行，通过这些活动，将促使参与者定义人工化学传感系统的规范，确定最有前途的传感技术，以供翻译、应用和最终用户社区使用。在整个研讨会中，参与者将被分成小组练习，这些小组可能是随机组成的，也可能是根据某些重要因素（例如，专业知识的多样性、职业阶段、部门）而设计的。可能有利于放置在基于共性与多样性的突破 研究团队和促进团队将共同努力，为每项活动确定组织参与者的最佳方式，研讨会将分为一系列步骤来“定义”需求、“识别”。在步骤 1 中，参与者将制定未来化学传感器和传感器系统的关键要求和规范。在步骤 2 中，参与者将确定五个领域的潜在方法：样品输送、生物传感器。 , 微量分析仪器, 多参数最后，在状态 3 中，参与者将确定该技术可能在 2 年、5 年和 10 年时间范围内产生社会影响的应用领域，并确定最终用户社区中哪些人应该受到影响。参与者将来自四个主要利益相关者：工业界、非营利组织、政府组织和学术界，涵盖广泛的学科，包括基础科学（材料科学、生物化学、感官）。 ,该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。