Battery-free, tuning circuit-inspired wireless sensor systems for detection of multiple biomarkers in bodily fluids

Abstract

Tracking the concentration of biomarkers in biofluids can provide crucial information about health status. However, the complexity and nonideal form factors of conventional digital wireless schemes impose challenges in realizing biointegrated, lightweight, and miniaturized sensors. Inspired by the working principle of tuning circuits in radio frequency electronics, this study reports a class of battery-free wireless biochemical sensors: In a resonance circuit, the coupling between a sensing interface and an inductor-capacitor oscillator through a pair of varactor diodes converts a change in electric potential into a modulation in capacitance, resulting in a quantifiable shift of the resonance circuit. Proper design of sensing interfaces with biorecognition elements enables the detection of various biomarkers, including ions, neurotransmitters, and metabolites. Demonstrations of "smart accessories" and miniaturized probes suggest the broad utility of this circuit model. The design concepts and sensing strategies provide a realistic pathway to building biointegrated electronics for wireless biochemical sensing.

Fig. 1.. Design and working principle of the tuning circuit–inspired wireless biochemical sensors.

(A) Schematic illustration of the stretchable, battery-free sensor and envisioned applications of the sensor system in detecting various biomarkers in bodily fluids. ISF, Interstitial fluid. (B) Photographs of a wireless sensor. (C) Equivalent circuit and flowchart for the signal conversion and transmission process of the sensor system. (D and E) Measured and simulated shift of resonance curves of a sensor with a DC reverse bias (from 200 to 400 mV; step: 50 mV). (F) Measured and simulated results of f_s as a function of the reverse bias (inset: zoom-in view).

Fig. 2.. Working range and mechanical properties of the biochemical sensors.

(A) Schematic illustration of the relative position of the sensing platform and the readout coil. (B and C) Resonance curves with varied lateral (B) and vertical (C) distance between the electromagnetic coupling unit and the readout coil. (D and E) Signal strength (D) and measurement precision (E) as a function of the lateral distance from the origin (z = 5 mm). (F) f_s as a function of the vertical distance (x = y = 0). (G) Photographs of a stretchable sensor with a tensile strain (from 0 to 28%) applied to the serpentine wires. (H) Measured results of f_s as a function of the damping resistance and applied strain. (I) SPICE simulation results of f_s as a function of the parasitic inductance (L_p) and damping resistance (inset: equivalent circuit used for simulation).

Fig. 3.. Sensing performance of ISM-functionalized wireless potentiometric sensors.

(A) Schematic illustrations of the DC part of ISM-functionalized sensors and the interaction between ISM and ions at the solution-sensor interface. (B) Photograph of a pair of thin-film electrodes as SE and RE for potentiometric sensing. (C) OCP (versus a Ag/AgCl RE) of a K⁺-functionalized Au electrode in response to varying concentration of K⁺. (D) Normalized values of measured S₁₁ of a wireless K⁺ sensor. (E to H) Calibration plots for K⁺, Ca²⁺, Na⁺, and H⁺ wireless sensors in response to corresponding analytes.

Fig. 4.. Design and performance characterization of aptamer- and enzyme-functionalized sensor systems.

(A) Schematic illustration of the interaction between serotonin and an antiserotonin aptamer-functionalized Au surface. (B) EIS characterization of an aptamer-functionalized electrode in 1× PBS solutions with varying concentration of serotonin. (C) Charge transfer resistance extracted from EIS results in (B). (D) OCP (versus Ag/AgCl RE) of the sensing interface as a function of serotonin concentration in 1× PBS solution. (E) Resonance curves of a wireless sensor system integrated with antiserotonin sensing interface in response to serotonin in 1× PBS solutions. (F) f_s of the integrated sensor extracted from (E). (G) Schematic illustration of the biofuel cell–inspired biochemical interface for glucose sensing. (H) Measured voltage drop across the resistor connecting the cathode and anode of the sensing interface. (I) Resonance curves of a wireless sensor system integrated with biofuel cell–inspired sensing interface in response to glucose solutions. (J) f_s of the integrated sensor as a function of concentration of glucose. (K) Real-time data acquired with varying concentration of glucose measured simultaneously using an electrochemical station and a wireless sensor. The data during the three sensing cycles are collected separately with a pause between each cycle and plotted together for comparison.

Fig. 5.. Multiplexed sensor system based on the LC circuit and sensing performance in mixed ion solutions.

(A) Photograph of a multiplexed ion sensor system consisting of three LC circuits with varied resonance frequency. The four electrodes (from left to right) correspond to H⁺, Na⁺, and K⁺ sensors and the RE. (B) Resonance curves of the multiplexed sensor array with a reverse DC bias applied to one LC circuit. (C) Response to input DC voltage of the three sensors in the multiplexed sensing platform. (D to F) Resonance curves of the multiplexed sensing system in response to concentration change in H⁺, K⁺, and Na⁺ in the mixed ion solution. (G to I) Extracted cross-sensitivity of the multiplexed sensing system based on results in (D) to (F). (J) Calibration methodology used for the multiplexed sensor arrays and calculated ion concentrations in a mixed ion test solution obtained without and with calibration.

Fig. 6.. Demonstrations of biointegrated chemical sensors customized for different application scenarios.

(A) Design and schematic illustration of a smart necklace based on the tuning circuit–inspired sensor prototype, including a pendant, a clasp, and a chain. (B) Photographs of a participant wearing the smart necklace for sweat analysis during cycling. (C) Calibration plot of the smart necklace used for field testing. (D) Real-time wirelessly acquired signals and the corresponding data obtained using a commercial glucose assay kit in two participants during cycling, showing changes in glucose concentration in sweat. (E) Comparison of glucose concentrations measured by the smart necklace and the commercial assay kit. The data are from 20 sweat samples shown in (D), excluding the first point of each study section used for baseline correction. (F) Schematic illustration of a miniaturized sensor probe based on the resonance circuit for potential applications as bioimplants. (G) Comparison between the miniaturized sensor probe and competing state-of-the-art wireless sensing technologies. (H and I) Photographs of a miniaturized coupling unit mounted on a fingertip and integrated with a piece of meat, respectively. (J and K) Electrical performance of a miniaturized probe with an input reverse bias simulating biochemical signals.