pH-Sensitive Ultra-thin Oxide-Liquid Metal System: Understanding the Fundamental Sensing Mechanism
Abstract
The pH response of liquid metal (eutectic GaInSn) in the form of a pendant drop is investigated and the sensitivity of 92.96 mV in the pH range from 4 to 10 is obtained. Unlike the fundamental limit of pH sensitivity of 59.1 mV in an electrolyte-site binding surface, the super-Nernstian pH sensitivity originated from a spontaneous electrochemical reaction associated with an enhanced ionic exchange at the ultra-thin (1-3 nm) Ga2O3-electrolyte interface which is purely driven by thermodynamics, rendering to the lowest system energy possible involving gallate and bi-gallate ions. A unified Nernst equation is derived by introducing an ion-exchange factor x to explain superNernstian pH sensitivity and found a direct link between pH sensitivity and Pourbaix pH-Potential formulations. It is found that Nernstian sensitivity of 59.1 mV occurs only for symmetric ion exchange (x=1) reaction, whereas asymmetric ion exchanges could result in sensitivity far beyond the Nernst sensitivity. Our findings have great scientific significance, which could redefine the conventional concept of the ion sensing mechanism in a solid-state electrochemical sensor and push forward the future development of the 2D oxide-based electrochemical sensor.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.