Comparison of Duplex and Quadruplex Folding Structure Adenosine Aptamers for Carbon Nanotube Field Effect Transistor Aptasensors

Nguyen, Hong Phan T.; Murugathas, Thanihaichelvan; Plank, Natalie O. V.

doi:10.3390/nano11092280

Cited by 8 publications

(21 citation statements)

References 55 publications

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“…Explaining all of details of the functionalization of these sensors is beyond the scope of this paper. However, detailed information on the 35-mer adenosine sensor, including transistor fabrication and aptamer functionalization, can be found in [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

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Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals’ lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks’ performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.

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Section: Methodsmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

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“…In the literature, the junctionless-carbon-nanotube-tunnel field-effect transistors (JL CNTTFETs) have shown promising subthreshold and switching performance [ 5 , 10 , 11 ] due to the amazing characteristics of carbon nanotube (CNT) as mature channel material, such as atomic structure, tunable band gap, high electrical conductivity, quasi-ballistic property, high Fermi velocity, and high sensitivity to its surrounded electrostatics (i.e., the electrostatic gating) [ 12 , 13 , 14 , 15 ]. However, as any electronic nanodevice, the ultrascaled JL CNTTFET suffers from some weaknesses, namely the low on-current and the issue of direct source-to-drain tunneling (DSDT), which is attributed to the low effective mass in the carbon nanotube [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Synergy of Electrostatic and Chemical Doping to Improve the Performance of Junctionless Carbon Nanotube Tunneling Field-Effect Transistors: Ultrascaling, Energy-Efficiency, and High Switching Performance

et al. 2022

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The low on-current and direct source-to-drain tunneling (DSDT) issues are the main drawbacks in the ultrascaled tunneling field-effect transistors based on carbon nanotube and ribbons. In this article, the performance of nanoscale junctionless carbon nanotube tunneling field-effect transistors (JL CNTTFETs) is greatly improved by using the synergy of electrostatic and chemical doping engineering. The computational investigation is conducted via a quantum simulation approach, which solves self-consistently the Poisson equation and the non-equilibrium Green’s function (NEGF) formalism in the ballistic limit. The proposed high-performance JL CNTTFET is endowed with a particular doping approach in the aim of shrinking the band-to-band tunneling (BTBT) window and dilating the direct source-to-drain tunneling window, while keeping the junctionless paradigm. The obtained improvements include the on-current, off-current, ambipolar behavior, leakage current, I60 metric, subthreshold swing, current ratio, intrinsic delay, and power-delay product. The scaling capability of the proposed design was also assessed, where greatly improved switching performance and sub-thermionic subthreshold swing were recorded by using JL CNTTFET with 5 nm gate length. Moreover, a ferroelectric-based gating approach was employed for more enhancements, where further improvements in terms of switching performance were recorded. The obtained results and the conducted quantum transport analyses indicate that the proposed improvement approach can be followed to improve similar cutting-edge ultrascaled junctionless tunnel field-effect transistors based on emerging atomically thin nanomaterials.

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“…A detailed description of these biosensors’ components and their sensing protocols can be found in our previous article [ 20 ]. In addition, comprehensive information on the 35-mer adenosine biosensor, such as fabricating the transistor and functionalizing the aptamer, can be found in [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks

et al. 2023

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Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies. We developed autoencoder-based prediction models to automatically detect anomalous data recorded by three electrochemical aptasensors, with variations in the signals’ lengths for particular concentrations, analytes, and bioreceptors. Prediction models employed autoencoder networks and the kernel density estimation (KDE) method for finding the threshold to detect anomalies. Moreover, the autoencoder networks were vanilla, unidirectional long short-term memory (ULSTM), and bidirectional LSTM (BLSTM) autoencoders for the training stage of the prediction models. However, the decision-making was based on the result of these three networks and the integration of vanilla and LSTM networks’ results. The accuracy as a performance metric of anomaly prediction models showed that the performance of vanilla and integrated models were comparable, while the LSTM-based autoencoder models showed the least accuracy. Considering the integrated model of ULSTM and vanilla autoencoder, the accuracy for the dataset with the lengthier signals was approximately 80%, while it was 65% and 40% for the other datasets. The lowest accuracy belonged to the dataset with the least normal data in its dataset. These results demonstrate that the proposed vanilla and integrated models can automatically detect abnormal data when there is sufficient normal data for training the models.

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Comparison of Duplex and Quadruplex Folding Structure Adenosine Aptamers for Carbon Nanotube Field Effect Transistor Aptasensors

Cited by 8 publications

References 55 publications

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Synergy of Electrostatic and Chemical Doping to Improve the Performance of Junctionless Carbon Nanotube Tunneling Field-Effect Transistors: Ultrascaling, Energy-Efficiency, and High Switching Performance

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks

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