Multi-Oxyanion Detection by an Organic Field-Effect Transistor with Pattern Recognition Techniques and Its Application to Quantitative Phosphate Sensing in Human Blood Serum.

We herein report an organic field-effect transistor (OFET) based chemical sensor for multi-oxyanion detection with pattern recognition techniques. The oxyanions ubiquitously play versatile roles in biological systems, and accessing the chemical information they provide would potentially facilitate fundamental research in diagnosis and pharmacology. In this regard, phosphates in human blood serum would be a promising indicator for early case detection of significant diseases. Thus, the development of an easy-to-use chemical sensor for qualitative and quantitative detection of oxyanions is required in real-world scenarios. To this end, an extended-gate-type OFET has been functionalized with a metal complex consisting of 2,2'-dipicolylamine and a copper(II) ion (CuII-dpa), allowing a compact chemical sensor for oxyanion detection. The OFET combined with a uniform CuII-dpa-based self-assembled monolayer (SAM) on the extended-gate gold electrode shows a cross-reactive response, which suggests a discriminatory power for pattern recognition. Indeed, the qualitative detection of 13 oxyanions (i.e., hydrogen monophosphate, pyrophosphate, adenosine monophosphate, adenosine diphosphate, adenosine triphosphate, terephthalate, phthalate, isophthalate, malonate, oxalate, lactate, benzoate, and acetate) has been demonstrated by only using a single OFET-based sensor with linear discriminant analysis, which has shown 100% correct classification. The OFET has been further applied to the quantification of hydrogen monophosphate in human blood serum using a support vector machine (SVM). The multiple predictions of hydrogen monophosphate at 49 and 89 μM have been successfully realized with low errors, which indicates that the OFET-based sensor with pattern recognition techniques would be a practical sensing platform for medical assays. We believe that a combination of the OFET functionalized with the SAM-based recognition scaffold and powerful pattern recognition methods can achieve multi-analyte detection from just a single sensor.