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Monolithic design of an organic electrochemical transistor array for multimodal bioelectronic interfacing

Nature Sensors (2026)Cite this article

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Abstract

Bioelectronic interfaces aim to seamlessly connect biological systems with electronics, enabling real-time monitoring and modulation of physiological activity. Organic electrochemical transistors (OECTs) offer operation relying on mixed ionic–electronic conduction suitable for multimodal biosensing, yet integrating chemical detection, electrophysiology and on-device processing within a single architecture has remained elusive. Here we introduce a monolithically fabricated, flexible OECT array that functions as a multimodal sensing platform. By combining electrolyte-specific regions across the array, we achieve dual-scale dopamine detection across physiological concentrations with picomolar sensitivity, high-bandwidth electroencephalography recording enabled by ion–gel gating, and hydrogel-gated neuromorphic computation capable of classifying seizure activity with 87.8% accuracy, comparable to inorganic neuromorphic hardware. This multifunctional integration demonstrates that a single organic semiconductor can support chemical sensing, signal amplification and on-device computation, extending OECTs beyond single-function electronics. This platform establishes a pathway towards compact, intelligent biointerface sensing systems for continuous monitoring and real-time signal interpretation.

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Fig. 1: Schematics and components of the multifunctional OECT-based platform, highlighting its functions based on the electrolyte.
Fig. 2: OECT characterization.
Fig. 3: High-C-scale dopamine sensing.
Fig. 4: Low-C-scale dopamine sensing in human saliva.
Fig. 5: EEG monitoring.
Fig. 6: Neuromorphic computing and ANN-based seizure classifier on the flexible OECT array.

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Data availability

The EEG data used for simulated signal recordings, ANN model training and model testing are from the open-source CHB-MIT scalp EEG database at https://physionet.org/content/chbmit/1.0.0/. Figure source data in this study are available via GitHub at https://github.com/wskaust/Mono_BioInterfacing. Further data that support the plots within this paper and other findings of this study are available from the corresponding author upon request.

Code availability

The MATLAB code used to fit both the LTP and LTD data in this study is available via GitHub at https://github.com/wskaust/Mono_BioInterfacing.

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Acknowledgements

We acknowledge H. Ombao and his team (Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST), including I. I. Gauran and F. A. Gomawi, for the design of the clinical study involving saliva, and A. Hama (Biological and Environmental Science and Engineering Division, KAUST) for his help with the design and fabrication of the PCB used in this work. We thank N. El-Atab and D. Kumar (Smart Advanced Memory Devices and Applications Laboratory, KAUST) for providing the inorganic neuromorphic device. We acknowledge M. Amad and P. L. Sanchez (Analytical Chemistry Core Lab, KAUST) for their assistance with dopamine sensing using ELISA. This publication is based on work supported by KAUST under awards ORA-2021-CRG10-4650 and OFP2023-5522 and the KAUST Center of Excellence for Smart Health (KCSH) under award 5932. We acknowledge funding by the European Union as part of the IONBIKE 2.0 project (grant agreement no. 101129945; https://doi.org/10.3030/101129945). Participants of this study provide their consent for the publication of the manuscript detailed above, including any accompanying images or data contained within the manuscript that indirectly disclose their identities.

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Authors and Affiliations

  1. Organic Bioelectronics Laboratory, Biomedical Sciences Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Yizhou Zhong, Wentao Shan, Yazhou Wang, Haya Aldosari, Danilo Arcangeli & Sahika Inal

  2. Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    Haya Aldosari

  3. POLYMAT, University of the Basque Country, Donostia-San Sebastian, Guipuzcoa, Spain

    Naroa Lopez-Larrea & David Mecerreyes

  4. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    David Mecerreyes

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Contributions

Y.Z. fabricated the device, performed OECT characterization, electrochemical characterization, dopamine-sensing measurements and electrophysiological monitoring measurements. W.S. prepared the [BMIM][TFSI] IL gel, performed the neuromorphic computing measurements and developed the machine learning methods. Y.W. provided the semiconducting polymer PIBET-AO. H.A. performed the UV–vis spectroscopy measurements. D.A. helped with the design of the dopamine-sensing measurements and contributed to the sensing mechanism investigations. N.L.-L. and D.M. provided the hydrogel. S.I. conceived the research, designed the experiments, and supervised the work. Y.Z. and S.I. wrote the paper and interpreted the results with input from all authors.

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Correspondence to Sahika Inal.

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Extended data

Extended Data Fig. 1 Details of flexible OECT array.

a The exploded-view schematics showing the components of the flexible OECT array. b The interconnects on the flexible OECT array. c Microscope image of all 64 channels on the flexible OECT array. d A close-up view of four channels, with the dimensions of L = 10 μm, W = 100 μm, and d = 73 nm. e The photograph of the PCB housing the flexible OECT array, enabling communication from the OECT array to the external analyzing instrument. f Hardware setup for connecting the flexible OECT array to the external instrument for all applications demonstrated in this work.

Extended Data Fig. 2 Electrochemical characterization of PIBET-AO film.

a 50 successive CV curves of a PIBET-AO film recorded in PBS or IL. The scan rate was 50 mV/s. b The Nyquist plot of a PIBET-AO film recorded in PBS or IL. Insets show the data points at low impedance values and the equivalent circuit model used to fit the impedance spectra, consisting of the electrolyte resistance (Rs), the polymer capacitance (Cp), the charge transfer resistance (Rct), and the Warburg impedance (ZW). c The Bode plot of a PIBET-AO film recorded in PBS or IL (solid lines and triangle symbols correspond to the magnitude and the phase of the impedance, respectively). d Volumetric capacitance (C*) and Rct of the PIBET-AO film recorded in PBS or IL. Data are presented as mean values ± s.d., calculated from 3 PIBET-AO film samples.

Extended Data Fig. 3 The electrochemical potential of OECT terminals.

a The schematic of multichannel potentiostat setup to determine the electrochemical potential of transistor terminals with respect to the Ag/AgCl reference electrode during device operation. b Real-time electrochemical potential changes at the OECT terminals (gate potential, EG, drain potential, ED, and source potential, ES) were measured in PBS with dopamine. VD was at −0.2 V, and VG changed from 0 to −0.5 V with a step of 0.1 V.

Extended Data Fig. 4 Dopamine oxidation at the PIBET-AO film.

a The real-time OCP change of the PIBET-AO thin film as dopamine or oxidized dopamine are added to the PBS at various concentrations. The arrows mark the addition of each concentration. The OCP shift resulting from blank PBS addition is also plotted. b The change in OCP when correcting by the change caused by PBS pipetting (ΔOCP) of as a function of dopamine concentrations.

Extended Data Fig. 5 Scan rate dependent CV measurements.

Five successive CV curves of a PIBET-AO film recorded in PBS and dopamine-containing PBS with different concentrations (1 pM, 20 pM, 1 nM, 20 nM, 500 nM, 10 μM, 500 μM, and 10 mM). The scan rates were 25 mV/s, 50 mV/s, and 100 mV/s for each measurement.

Extended Data Fig. 6 EEG frequency spectra comparison.

Comparison of EEG frequency spectra between simulator output (red) and OECT-acquired signals (blue) across all eight recordings, processed using a fast Fourier transform. Spectra were normalized to the peak amplitude within the 0.5 to 10 Hz range.

Extended Data Fig. 7 The flexible OECT array configuration to acquire EEG from the volunteer.

a The configuration includes attaching OECT array on the subject’s forehead and setting other gadgets (polymer electrodes, FFC, and PDMS). Photographs of b polymer electrode coated by PEDOT:PSS and c FFC.

Extended Data Fig. 8 Assessment of the flexible OECT array for concurrent multimodal applications.

a The real-time change in OECT | ID| in response to dopamine at concentrations of 10 μM, 500 μM, and 10 mM, measured at VD = -0.2 V and VG = -0.25 V. b The LR calculated from the data in a. c The concurrent simulated real-time EEG signal recorded during the dopamine sensing. d Comparison of the LR results with the calibration curve obtained from Supplementary Fig. 14. e Comparison of EEG signals in the frequency domain from the beginning and end sections of the real-time measurement.

Extended Data Fig. 9 Assessment of the interfacial stability between the gels and the PIBET-AO thin film.

a Schematic of interfacial impedance measurement using the 3-electrode configuration. b Attachment of flexible OECT array on a chicken breast to simulate a wearable condition. c The Nyquist plot of a PIBET-AO film with the hydrogel. d The Bode plot of the PIBET-AO film with the hydrogel. e The Nyquist plot of a PIBET-AO film with the IL gel. f The Bode plot of the PIBET-AO film with the IL gel. The data in c-f were recorded right after the simulated wearable configuration (0 h) and 1, 2, 4, 8, 24 hours after the first measurement.

Extended Data Fig. 10 LTP/LTD operation of the hydrogel-gated OECT.

ΔID measured over 500 pulsing cycles and using time intervals of tp = Δt = 2 ms. Measurements were conducted: a after initial contact with the hydrogel and after b 1, c 2, d 4, and e 8 hours, respectively. VG pulses alternated between 0 and -0.6 V, with VD fixed at -0.6 V.

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Zhong, Y., Shan, W., Wang, Y. et al. Monolithic design of an organic electrochemical transistor array for multimodal bioelectronic interfacing. Nat. Sens. (2026). https://doi.org/10.1038/s44460-026-00052-0

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