Imagine a tiny, pluggable chip that acts like a high-speed biological scanner. Instead of sending blood samples to a distant lab and waiting days, this device uses light and specialized sensors to spot infection markers instantly. It even has a built-in 'brain' that analyzes the results on the spot to tell doctors exactly what is wrong.
Sepsis diagnosis is currently too slow because it relies on centralized labs, and survival rates drop by 7.6% for every hour of delay. Existing point-of-care tools lack the ability to detect multiple biomarkers and provide automated decision support.
A System-in-Package prototype featuring a pluggable 8-channel plasmo-photonic sensor and a zero-power photonic neural network for rapid sepsis classification.
If you are a diagnostic developer dealing with the slow turnaround of centralized lab tests — this project developed a disposable sensing chip that identifies 7 biomarkers within a few minutes. This allows for bedside diagnosis, reducing the time-critical window where survival drops by 7.6% each hour.
If you are a chip maker dealing with high production costs for integrated optics — this project developed a micro-transfer printing (μTP) method for lasers and photodiodes. This technique drastically decreases costs and enables the creation of cheap, disposable pluggable modules.
If you are a hospital dealing with high sepsis mortality rates of around 35% — this project developed a Point of Care Unit that provides real-time disease stage classification. This enables rapid and precise decision making for therapy at the bedside.
The project uses micro-transfer printing (μTP) for on-chip lasers and photodiodes, which drastically decreases costs and makes the sensor arrays disposable.
Yes, the project utilizes a CMOS compatible toolkit and high-throughput micro-transfer technology to ensure the sensors are cheap and manufacturable.
Based on available project data, the IP centers on the integration of aluminum plasmonics with silicon nitride photonics and the use of photonic neural networks for zero-power classification.
The platform provides quantification of multiple biomarkers and bacteria within a few minutes, avoiding the delays caused by specimen transfers to centralized laboratories.
The unit features an embedded Si3N4 photonic neural network that processes and classifies data from at least 7 biomarkers with zero-power consumption.
The consortium is well-balanced for commercialization, featuring 13 partners across 8 countries. With an industry ratio of 46% (including 6 industrial partners and 5 SMEs), there is strong alignment between the academic research (4 universities, 3 research centers) and the market-ready application of the technology.
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