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A Bidirectional-Current CMOS Potentiostat for Fast-Scan Cyclic Voltammetry Detector Arrays
IEEE Transactions on Biomedical Circuits and Systems
|July 12, 2018
Summary
A novel potentiostat circuit enables bidirectional current detection for microelectrode arrays. This technology supports advanced techniques like fast-scan cyclic voltammetry (FSCV) for high-throughput screening and event recording.
Area of Science:
- Electrical Engineering
- Neuroscience
- Materials Science
Background:
- Microelectrode arrays are crucial for electrochemical sensing and neural recording.
- Existing potentiostat designs face limitations in handling bidirectional currents and parallel processing.
- Fast-scan cyclic voltammetry (FSCV) requires high-speed, sensitive detection systems.
Purpose of the Study:
- To present a novel potentiostat circuit capable of bipolar electrode voltages and bidirectional current detection.
- To enable parallel recording of quantal release events and electrode impedance characterization using microelectrode arrays.
- To facilitate high-throughput drug screening applications.
Main Methods:
- Development of a potentiostat circuit utilizing a regulated-cascode amplifier for positive currents and an active-input regulated-cascode mirror for negative currents.
- Fabrication of a 64-channel FSCV detector array using a 0.5-μm, 5-V CMOS process.
- Validation using prerecorded FSCV data from carbon-fiber microelectrodes.
Main Results:
- Demonstration of a compact 64-channel FSCV detector array (45 μm × 30 μm per detector) with minimal components (14 transistors, 50-fF capacitor).
- The potentiostat topology effectively handles both positive and negative input currents.
- Successful validation of the system's performance with real-world FSCV data.
Conclusions:
- The presented potentiostat circuit and CMOS detector array offer a powerful platform for advanced electrochemical analysis.
- This technology significantly enhances capabilities for parallel recording, impedance characterization, and drug screening.
- The system paves the way for more efficient and high-throughput studies in neuroscience and materials science.
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