KEYWORDS: Time series analysis, Deep learning, Uncertainty analysis, Neural networks, Sensors, Data modeling, Biosensors, Reflectivity, Machine learning, Silicon, Semiconducting wafers
We report a generalizable computational approach to dramatically reduce biomolecular and chemicalsensor response time for applications including medical diagnostics. Comparing the performance of different models, we use experimental data to train ensembles of both traditional recurrent neural networks (RNN) and long short-term memory (LSTM) networks, to accurately predict equilibrium sensor response from data measured over a short time span. This approach is particularly advantageous for sensor platforms with long response times due to poor mass transport, including porous silicon optical biosensors, which we use to validate this methodology through exposure to various concentrations of protein solution and subsequent analysis.
We report the incorporation of porous silicon on paper towards the realization of a low-cost rapid diagnostic testing platform with the capability for quantification of detected molecules. Real-time optical reflectance measurements of bovine serum albumin adsorption were carried out to benchmark the sensor. Simulations and experiments demonstrate a response-time dependence on porous silicon pore size. Approaches to overcome challenges with porous silicon adhesion on paper and porous silicon membrane mechanical robustness will be discussed.
We investigate the utility of various statistical and machine learning techniques for classifying and quantifying selected proteins using an array of porous silicon sensors with uniquely tuned properties. No capture agents or bioreceptors are utilized for the protein detection. The sensing approach relies on differences in non-specific physisorption and represents a step towards a new low cost, simple and robust sensor platform that can detect a vast range of biomolecules.
We present a signal processing method capable of significantly lowering the detection limit of thin film optical biosensors. This signal processing method, which we term LAMP, is based on Morlet wavelet filtering and extracting phase information. LAMP drastically reduces noise contributions typically encountered in sensor measurements. The noise immunity, sensitivity and linearity of LAMP is benchmarked against several other signal processing methods by applying the different techniques to a large set of simulated porous silicon thin film optical spectra that contain various types of noise signatures. The LAMP signal processing technique opens up new applications in disease detection and environmental monitoring for thin film sensors previously precluded by insufficient detection limits.
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