Hybrid Nanomaterial Boosts Glucose Sensor Performance in Organic Transistors

By Dr. Noopur JainReviewed by Bethan DaviesMay 1 2025

Researchers have developed a nanocomposite-enhanced transistor that significantly boosts glucose sensing performance by improving conductivity and integrating machine learning for accurate detection.

The innovation centers on a hybrid material—metal-organic frameworks (MOF)-MoS₂ nanosheets—doped into the conductive polymer PEDOT:PSS to enhance the channel properties of organic electrochemical transistors (OECTs). By optimizing both electronic and ionic transport within the device, the approach leads to higher transconductance and greater sensor sensitivity.

Background

While PEDOT:PSS has long been valued for its good conductivity and biocompatibility, its performance in high-sensitivity applications has reached a plateau. To address this, recent work has focused on doping PEDOT:PSS with nanomaterials to enhance charge transport and ion mobility.

The MOF-MoS₂ hybrid brings together the high electron mobility of MoS₂ with the porosity and large surface area of MOFs, producing a material with complementary properties. This combination can establish a highly conductive and ion-permeable network within the PEDOT:PSS matrix, effectively overcoming limitations of conventional materials.

Previous studies have already pointed to the potential of such nanocomposites to significantly boost device transconductance and sensitivity, critical factors in biosensing applications such as detecting glucose, lactate, and dopamine. Meanwhile, machine learning (ML) is emerging as a valuable tool in sensor systems, helping manage device variability and interpret complex signal patterns.

The Current Study

In this study, the research team synthesized MOF-MoS₂ hybrid nanosheets by combining ZIF-67 (a cobalt-based MOF) and MoS₂ crystals in isopropanol. These nanosheets were then mixed into PEDOT:PSS solutions at concentrations ranging from 0 % to 7 %, and the resulting blends were solution-cast onto flexible polyethylene terephthalate (PET) substrates. Atomic force microscopy (AFM) was used to examine the surface morphology and roughness of the resulting films.

Electrical performance was evaluated through transconductance measurements of the OECTs. For glucose sensing, the researchers applied a gate electrode coated with glucose oxidase (GO_x) and Nafion. Glucose that diffused through the Nafion membrane underwent enzymatic oxidation, producing hydrogen peroxide (H₂O₂). The electro-oxidation of H₂O₂ at the gate modulated the gate voltage, which in turn altered the drain current. Devices were tested across a wide glucose concentration range—from nanomolar to millimolar levels—with a focus on maintaining consistency across different devices.

Results and Discussion

The incorporation of MOF-MoS₂ nanosheets led to a clear improvement in device performance. Transconductance increased by approximately threefold compared to undoped PEDOT:PSS devices, reaching around 19.34 mS.

This improvement was attributed to the complementary functions of the nanomaterials: MoS₂ provided efficient electron transport pathways, while the porous MOF structure supported ion storage and movement. AFM analysis confirmed increased surface roughness in the doped films, correlating with a larger active surface area and more effective charge transfer.

The enhanced transconductance directly translated into better glucose sensing capabilities. The doped OECTs exhibited a strong, concentration-dependent current response to glucose levels ranging from 30 nanomolar to 1 millimolar. The enzymatic reaction at the gate electrode produced H₂O₂, which modulated the gate potential and, in turn, altered the channel conductivity. The devices also demonstrated high selectivity for glucose, largely due to the specificity of the GO_x enzyme and the protective properties of the Nafion membrane, which helped minimize interference from other compounds.

Machine learning played a critical role in analyzing the sensor data. A Random Forest model trained on response features accurately predicted glucose concentrations, outperforming traditional calibration techniques. The ML approach also compensated for device-to-device variation and external noise, improving reproducibility and robustness.

The authors noted that combining nanomaterial doping with ML created a strong foundation for next-generation biosensors. While the hybrid material broadened the detection range and enhanced sensitivity, ML algorithms ensured consistent data interpretation, even under changing conditions. Together, these advances point toward practical applications in portable and wearable health monitoring systems.

Looking ahead, the researchers proposed expanding this approach to detect other biomarkers, refining nanomaterial synthesis for improved stability and recyclability, and integrating the entire system into compact, wireless devices for real-world use.

Conclusion

This study marks a meaningful step toward high-performance organic electrochemical transistors for biosensing. By doping PEDOT:PSS with MOF-MoS₂ nanosheets, researchers achieved substantial improvements in transconductance and glucose sensitivity. The hybrid nanocomposite enhances both electronic and ionic conduction, supporting precise analyte detection over a wide concentration range. Coupled with machine learning, this approach promises greater accuracy, selectivity, and stability—qualities essential for next-generation biosensing technologies.

Journal Reference

Sun Y., Zhou Y. et al. (2025). MOF-MoS₂ Nanosheets Doped PEDOT:PSS for Organic Electrochemical Transistors in Enhanced Glucose Sensing and Machine Learning-based Concentration Prediction. Materials Futures. DOI: 10.1088/2752-5724/adccdf, https://iopscience.iop.org/article/10.1088/2752-5724/adccdf