Modular Platform for Dynamic Biochemical Sensing of Acute Skin Wounds
Abstract
The purpose of this project was to develop reliable sensors that can report the concentration of key bioactive factors in wound fluid, and which can then be used for diagnosing and treating wounds, either by the physician, or as part of an eventual smart bandage that would use the sensor information to deliver treatments in an automated way. We developed sensor patches that are coated with antibodies for a specific antigen. Specific binding of the antigen to the sensor changes its impedance characteristics proportionally to the antigen concentration. We prepared sensors for inteleukin-6, tumor necrosis factor-, and albumin, and demonstrated their use on skin wounds in a standard mouse wound model. Different markers peaked at different times, thus providing biochemical information on the evolution of the wound healing. Sensor data were validated using standard biochemical assays. Sensors were also connected to a custom designed complementary metal-oxide-semiconductor (CMOS) chip, which enabled incorporation of the impedance analyzer in a bandage form factor, and will eventually allow collection of signals from multiple sensor patches. Time-resolved, on-demand sensor activation was achieved using graphene oxide membrane covers that were opened/ruptured by applying tunable vibration. While still more effort is necessary to understand the design, modeling, fabrication, and reliability of these membrane covers, these new results suggest potential for controllable rupture of membranes based on their diameter and thickness, along with their placement, bonding, and geometrical relationship to the substrate/device on which they reside. Using different membrane cover diameters that resonated at different frequencies provided a means to open covers in a specified sequence, thus allowing liquid samples to reach the desired sensor patches.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 31, 2022
- Accession Number
- AD1186936
Entities
People
- Aaron Mazzeo
- François Berthiaume
- Kaushik Sengupta
- Mehdi Javanmard
Organizations
- Princeton University
- Rutgers University–New Brunswick