课题基金 / 基金详情

AMBROSIA: A Multiplexed Plasmo-Photonic Biosensing Platform For Rapid And Intelligent Sepsis Diagnosis At The Point- Of-Care

AMBROSIA: A Multiplexed Plasmo-Photonic Biosensing Platform For Rapid And Intelligent Sepsis Diagnosis At The Point- Of-Care
AMBROSIA:用于护理点快速智能脓毒症诊断的多重等离子体光子生物传感平台
批准号:
10066297
负责人:
金额:
$72.97万
依托单位:
依托单位国家:
英国
项目类别:
EU-Funded
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
AMBROSIA旨在为多传感、面向未来的脓毒症诊断床提供基础,该床由CMOS兼容工具包提供,并通过片上光子神经网络技术进行增强,以提供准确、快速的诊断。安博亚洲将投资于集成等离子体光子传感器的超小尺寸和高灵敏度,并通过著名的片上慢光效应和Si 3 N4上的微转移印刷激光器和光电二极管以及集成光子神经网络引擎的功能处理和分类产品组合进行增强,以描绘传感器发展的下一个颠覆性趋势,在系统级封装原型组件中定制它们,传感器是廉价的一次性可插拔模块,可以在临床环境中在床边快速准确地诊断败血症。安布罗西亚的目标是展示一个包含以下内容的护理点:i)可切换的传感器区域阵列,其中每个传感器区域促进用于脓毒症诊断的可插拔的8通道无标记等离子体光子传感器,其对于每个干涉传感器具有超过130.000nm/RIU的灵敏度和低于10-8 RIU的检测限,ii)嵌入式Si 3 N4光子神经网络同时处理和分类来自具有零功率的至少7个生物标志物的数据,在最初几分钟内提供对脓毒症的准确和快速诊断,iii)芯片上的微转移印刷激光器和光电探测器,这将大大降低传感和神经网络模块的成本,并使传感器阵列一次性使用。
英文摘要
AMBROSIA aims to provide the foundations for a multi-sensing future-proof Point of Care Unit for sepsis diagnosis offered by a CMOS compatible toolkit and enhanced by on-chip photonic neural network technology to provide an accurate and rapid diagnosis. AMBROSIA will be investing in the established ultra-small-footprint and elevated sensitivity of integrated plasmo-photonic sensors reinforced by the well-known on-chip slow-light effect and micro-transfer printed lasers and photodiodes on Si3N4, as well as the functional processing and classification portfolio of integrated photonic neural network engines, towards painting the landscape of the next-coming disruption in sensor evolution, tailoring them in System-in-Package prototype assemblies, with the sensors being cheap disposable pluggable modules that can rapidly and accurately diagnose sepsis at the bedside in clinical environments. AMBROSIA targets to demonstrate a Point of Care Unit incorporating: i) a switchable sensor area array, with each sensor area facilitating a pluggable, 8-channel label-free plasmo-photonic sensor for sepsis diagnosis with a sensitivity over 130.000nm/RIU and a Limit of Detection below 10-8 RIU for each interferometric sensor, ii) an embedded Si3N4 photonic neural network processing and classifying at the same time the data from at least 7 biomarkers with zero-power providing in the first minutes an accurate and rapid diagnosis for sepsis, iii) Micro transfer printed lasers and photodetectors on chip that will drastically decrease costs of both the sensing and neural network modules, and render the sensor arrays disposable.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
海外基金