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.

Synthetic DNA aptamer receptors could boost the prospects of carbon nanotube (CNT)-based electronic biosensors if signal transduction can be understood and engineered. Here, we report CNT aptasensors for potassium ions that clearly demonstrate aptamer-induced electrostatic gating of electronic conduction. The CNT network devices were fabricated on flexible substrates via a facile solution processing route and non-covalently functionalised with potassium binding aptamers. Monotonic increases in CNT conduction were observed in response to increasing potassium ion concentration, with a level of detection as low as 10 picomolar. The signal was shown to arise from a specific aptamer–target interaction that stabilises a G-quadruplex structure, bringing high negative charge density near the CNT channel. Electrostatic gating is established via the specificity and the sign of the current response, and by observing its suppression when higher ionic strength decreases the Debye length at the CNT–water interface. Sensitivity towards potassium and selectivity against other ions is demonstrated in both resistive mode and real time transistor mode measurements. The effective device architecture presented, along with the identification of clear response signatures, should inform the development of new electronic biosensors using the growing library of aptamer receptors.

The authors demonstrate a small molecule 17 β-estradiol (E2) sensor based on aptamer functionalized carbon nanotube network film field effect transistors (CNT FETs). The real time current response for the 35-mer E2 aptamer functionalized CNT FET shows a clear increase in current over the range of 50 nM to 1.6 μM of E2. The E2 response using a longer 75-mer version of the aptamer functionalized CNT FETs, where the aptamer/E2 binding occurs beyond the Debye length, shows no obvious evidence of sensing. The CNT FET sensing platform has been fabricated via a simple surfactant free solution processing route, compatible with further carbon nanotube functionalization to develop a versatile sensing platform. The CNT FET aptasensors are able to perform real time monitoring of E2 levels for selective and quantitative detection of E2 in liquids.

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.