In industrial chemical synthesis and biomedical applications, accurately mapping chemical concentrations in flowing systems are challenging. The performance of standard sensors remains limited when operational requirements include measuring fluid compositions inside lengthy or hard-to-access tubing without disrupting flow. The University of Michigan research team developed time-aware particulate sensors (TAPS) as miniature fluid-moving devices that detect particular chemicals during their journey. Find the study published in the AIChE Journal here:
Manion, M. L., Doctor, J., & Liu, A. T. (2025). Temporally resolved concentration profiling via computationally limited, distributed sensor nodes. AIChE Journal, 71(2). https://doi.org/10.1002/aic.18691
The operational principle of TAPS differs from standard stationary sensors because these devices operate by moving through the system. Micro-scale sensors can record chemicals during their movement through pipes and biological systems. The information produced from analysis provides complete chemical concentration results after data collection.
Simulations demonstrate that deploying thousands of these sensors can collectively map entire concentration profiles that were previously unattainable.
“We show here that even with basic functions for each sensor, with strength in numbers, they achieve something together that is otherwise very hard to do,”
stated Albert Liu, an assistant professor of chemical engineering, macromolecular science and engineering and materials science and engineering at U-M and corresponding author of the study.
Each TAPS device is embedded with an array of memristors, which are electronic components capable of storing information via electrical resistance. These memristors function as an analog clock, sequentially switching off at a predetermined rate. When a sensor encounters a target chemical, the process halts, preserving a timestamp. After retrieval, the number of inactive memristors reveals how much time has passed since the sensor detected the chemical, translating temporal data into spatial chemical mapping.
This approach provides several advantages over conventional sensors:
- Minimal Flow Disruption: Due to their size, TAPS do not significantly alter fluid dynamics.
- Scalability: Millions of these sensors can be fabricated on a single silicon wafer, reducing costs.
- Adaptability: The design can incorporate alternative time-sensitive detection methods beyond memristors.
The study emphasises that optimal sensor performance depends on factors such as chemical type, concentration levels, and system dimensions.
“Our simulation clearly reflected that sensors need to be tailored to their environment. There’s a kind of fingerprint to each system that changes the optimal number of sensors and the sensing material used to detect the target chemical,”
said Matthew Manion, a doctoral student of chemical engineering at U-M and first author of the study.
To address these variations, the researchers developed an optimisation framework that enables engineers to fine-tune sensor characteristics for specific applications. The present system identifies fluid movement along one dimension through pipes and upcoming innovations focus on developing three-dimensional concentration detection methods.
The sensors currently operate without exchanging information with other sensors. Enabling sensors to communicate with each other would result in scaling advantages which would enhance the distributed sensing systems.
“In this study, individual particles do not yet communicate with each other. A lot of emergent behaviour relies upon particle to particle communication. When you have that, nonlinear scaling of such distributed particle-based systems truly comes alive,”
