Chemical programmability in food-safety nanobiosensing: a pathway-centric framework for autonomous analytical systems

Area of Science:

• Analytical Chemistry
• Biosensor Technology
• Food Science

Background:

• Food-safety sensors exhibit high analytical sensitivity in lab settings but degrade in complex food matrices.
• Performance decline is attributed to matrix components interfering with signal-transduction pathways, causing misidentification.
• Existing sensor designs often overlook the impact of matrix effects on recognition, transduction, and output stages.

Purpose of the Study:

• To introduce the concept of chemical programmability for controlling sensing pathways in food-safety sensors.
• To establish a framework for designing robust, adaptive, and autonomous food-safety sensing systems.
• To shift focus from sensitivity optimization to pathway controllability for improved real-world performance.

Main Methods:

• Defined chemical programmability as rational control of signal generation, transduction, amplification, and readout via chemical instructions.
• Developed a 3x2 programmability matrix based on programming node (recognition, transduction, output) and timing (design-time, run-time).
• Discussed strategies in molecular recognition engineering, material-layer energy/electron-transfer control, and system-layer signal conversion.

Main Results:

• Demonstrated that matrix interference disrupts signal pathways, leading to systematic errors despite high sensitivity.
• Showcased diverse strategies unified by energy landscape engineering to favor target pathways by modulating energy barriers.
• Proposed chemical programmability as a method to overcome matrix effects and enhance sensor reliability.

Conclusions:

• Chemical programmability provides a unified framework for developing next-generation food-safety sensors.
• Controlling signal pathways, rather than solely optimizing sensitivity, is key to robust performance in real food.
• This approach enables the design of adaptive and autonomous sensing systems for improved food safety.