Search Results (4)

Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency. The integration of a 1D CNN model on FPGA hardware for adaptive, non-linear noise filtering sets this design apart from conventional filtering approaches by achieving high accuracy and low power consumption in a portable format. This integration of SiNW-FET with FPGA-based CNN noise reduction offers a unique approach, as prior noise reduction techniques for biosensors typically rely on linear filtering or digital smoothing, which lack adaptive capabilities for complex, non-linear noise patterns. By introducing the 1D CNN on FPGA, this architecture enables real-time, high-fidelity noise reduction, preserving critical signal characteristics without compromising processing speed. Notably, the findings presented in this work are based exclusively on comprehensive simulations using COMSOL and MATLAB, as no physical prototypes or biomarker detection experiments were conducted. The SiNW-FET biosensor, functionalized with antibodies specific to viral antigens, detects impedance shifts caused by antibody–antigen interactions, providing a highly sensitive platform for viral detection. A high-gain folded-cascade amplifier enhances the Signal-to-Noise Ratio (SNR) to approximately 70 dB, verified through COMSOL and MATLAB simulations. Additionally, a 1D CNN model is employed for adaptive noise reduction, filtering out non-linear noise patterns and achieving an approximate 75% noise reduction across a broad frequency range. The CNN model, implemented on an Altera DE2 FPGA, enables high-throughput, low-latency signal processing, making the system viable for real-time applications. Performance evaluations confirmed the proposed system’s capability to enhance the SNR significantly while maintaining a compact and energy-efficient design suitable for portable diagnostics. This integrated architecture thus provides a powerful solution for high-precision, real-time viral detection, and continuous health monitoring, advancing the role of biosensors in accessible point-of-care diagnostics. Full article

The rapid and accurate detection of pathogens is vital for effective disease management and control. This paper introduces a novel framework for integrating artificial intelligence (AI) into electrochemical biosensors for pathogen detection. Real-world samples often present unwanted noise in the signal, particularly when utilizing portable point-of-care devices. To overcome this challenge, a framework using AI for noise reduction from a portable potentiostat is proposed in this work. This approach involves employing a denoising autoencoder (DAE) to effectively remove noise from the electrochemical signals generated from a portable potentiostat by utilizing training datasets generated from benchtop potentiostat for training the DAE, bringing the performance of portable devices on par with their benchtop counterparts. This enhancement is crucial for point-of-care applications where environmental and operational factors often compromise data quality. Smartphones are often used as interfaces for portable electrochemical devices, the proposed framework can leverage the computational capabilities of smartphones to run the DAE model for processing electrochemical signals in real-time, thus making it compatible with fully point-of-care solution. The proposed system has been validated using COVID-19 and dengue DPV data, demonstrating its potential as a powerful tool in the rapid and accurate detection of SARS-CoV-2 and other pathogens. The integration of AI into electrochemical biosensing offers a more reliable and accessible option for healthcare professionals and researchers. Full article

The electrochemical label-free aptamer-based biosensors (also known as aptasensors) are highly suitable for point-of-care applications. The well-established C-MEMS (carbon microelectromechanical systems) platforms have distinguishing features which are highly suitable for biosensing applications such as low background noise, high capacitance, high stability when exposed to different physical/chemical treatments, biocompatibility, and good electrical conductivity. This study investigates the integration of bipolar exfoliated (BPE) reduced graphene oxide (rGO) with 3D C-MEMS microelectrodes for developing PDGF-BB (platelet-derived growth factor-BB) label-free aptasensors. A simple setup has been used for exfoliation, reduction, and deposition of rGO on the 3D C-MEMS microelectrodes based on the principle of bipolar electrochemistry of graphite in deionized water. The electrochemical bipolar exfoliation of rGO resolves the drawbacks of commonly applied methods for synthesis and deposition of rGO, such as requiring complicated and costly processes, excessive use of harsh chemicals, and complex subsequent deposition procedures. The PDGF-BB affinity aptamers were covalently immobilized by binding amino-tag terminated aptamers and rGO surfaces. The turn-off sensing strategy was implemented by measuring the areal capacitance from CV plots. The aptasensor showed a wide linear range of 1 pM–10 nM, high sensitivity of 3.09 mF cm⁻² Logc⁻¹ (unit of c, pM), and a low detection limit of 0.75 pM. This study demonstrated the successful and novel in-situ deposition of BPE-rGO on 3D C-MEMS microelectrodes. Considering the BPE technique’s simplicity and efficiency, along with the high potential of C-MEMS technology, this novel procedure is highly promising for developing high-performance graphene-based viable lab-on-chip and point-of-care cancer diagnosis technologies. Full article

A novel procedure is developed for producing bulk modified Molecularly Imprinted Polymer (MIP) screen-printed electrodes (SPEs), which involves the direct mixing of the polymer particles within the screen-printed ink. This allowed reduction of the sample preparation time from 45 min to 1 min, and resulted in higher reproducibility of the electrodes. The samples are measured with a novel detection method, namely, thermal wave transport analysis (TWTA), relying on the analysis of thermal waves through a functional interface. As a first proof-of-principle, MIPs for dopamine are developed and successfully incorporated within a bulk modified MIP SPE. The detection limits of dopamine within buffer solutions for the MIP SPEs are determined via three independent techniques. With cyclic voltammetry this was determined to be 4.7 × 10⁻⁶ M, whereas by using the heat-transfer method (HTM) 0.35 × 10⁻⁶ M was obtained, and with the novel TWTA concept 0.26 × 10⁻⁶ M is possible. This TWTA technique is measured simultaneously with HTM and has the benefits of reducing measurement time to less than 5 min and increasing effect size by nearly a factor of two. The two thermal methods are able to enhance dopamine detection by one order of magnitude compared to the electrochemical method. In previous research, it was not possible to measure neurotransmitters in complex samples with HTM, but with the improved signal-to-noise of TWTA for the first time, spiked dopamine concentrations were determined in a relevant food sample. In summary, novel concepts are presented for both the sensor functionalization side by employing screen-printing technology, and on the sensing side, the novel TWTA thermal technique is reported. The developed bio-sensing platform is cost-effective and suitable for mass-production due to the nature of screen-printing technology, which makes it very interesting for neurotransmitter detection in clinical diagnostic applications. Full article