A quantitative sensing system based on a 3D-printed ion-selective electrode for rapid and sensitive detection of bacteria in biological fluid

Bacterial infections, such as urinary tract infections, are crucial health problems. Here, we report a new potentiometric sensor to detect bacteria sensitively, accurately, and quickly. First, a customizable, 3D printed Ag+ selective electrode was fabricated as the probe. Our 3D printed electrode showed sensitive, linear, and selective responses to Ag+. Compared to commercial Ag+ selective electrodes, ours required less sample volume, shorter responding time, and lower costs. Next, a novel potentiometer was developed with Arduino to couple the electrode for data transducing and transferring, which was programmed to transfer results to cell phones wirelessly. Moreover, a filter was designed to quickly remove interfering species in a biofluid sample (e.g., Cl-). By detecting the lost Ag+ taken by bacteria, the bacterial number could be elucidated. With this sensor system, bacteria numbers could be detected as low as 80 CFU/mL (LOD) within 15 min, which is sufficient for many diagnoses (e.g., urinary tract infection >1000 CFU/mL). An amplification method was presented for single-digit bacteria detection. Overall, we are presenting a bacteria detector with three innovative components: the electrode (signal transduction and detection), the potentiometer (transducer and data processing), and the 3D printed filter (sample preparation), which showed robust and improved (than previously reported ones) analytical merits. The low-cost and customizable (the electrode and the open-source coding) nature enhances the transnationality of the system, especially in underdeveloped areas.

Automated summary

This study introduces a new potentiometric sensor for sensitive, accurate, and quick detection of bacterial infections, such as urinary tract infections. The sensor system consists of three innovative components: a customizable 3D printed Ag+ selective electrode, a novel potentiometer developed with Arduino for data transducing and transferring, and a designed filter to remove interfering species in biofluid samples efficiently. The sensor can detect bacterial numbers as low as 80 CFU/mL within 15 minutes, demonstrating robust and improved analytical merits compared to existing methods.