Development of use-specific high-performance cyber-nanomaterial optical detectors by effective choice of machine learning algorithms

Davoud Hejazi,Amirreza Farnoosh,Shuangjun Liu,Sarah Ostadabbas,Swastik Kar

doi:10.1088/2632-2153/ab8967

Davoud Hejazi, Amirreza Farnoosh + Show 3 more

Open Access

https://doi.org/10.1088/2632-2153/ab8967

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Abstract

Due to their inherent variabilities, nanomaterials-based sensors are challenging to translate into real-world applications, where reliability and reproducibility are key. Machine learning can be a powerful approach for obtaining reliable inferences from data generated by such sensors. Here, we show that the best choice of ML algorithm in a cyber-nanomaterial detector is largely determined by the specific use-considerations, including accuracy, computational cost, speed, and resilience against drifts and long-term ageing effects. When sufficient data and computing resources are provided, the highest sensing accuracy can be achieved by the k-nearest neighbors (kNNs) and Bayesian inference algorithms, however, these algorithms can be computationally expensive for real-time applications. In contrast, artificial neural networks (ANNs) are computationally expensive to train (off-line), but they provide the fastest result under testing conditions (on-line) while remaining reasonably accurate. When access to data is limited, support vector machines (SVMs) can perform well even with small training sample sizes, while other algorithms show considerable reduction in accuracy if data is scarce, hence, setting a lower limit on the size of required training data. We also show by tracking and modeling the long-term drifts of the detector performance over a one year time-frame, it is possible to dramatically improve the predictive accuracy without any re-calibration. Our research shows for the first time that if the ML algorithm is chosen specific to the use-case, low-cost solution-processed cyber-nanomaterial detectors can be practically implemented under diverse operational requirements, despite their inherent variabilities.

Highlights

Nanomaterials are very attractive for building sensors, and various examples of using 2D nanomaterials, nano-tubes, quantum-dots, etc., can be found in the fabrication of optical detectors,[1,2,3] molecular and bio-sensors,[4,5,6,7,8] ion and radiation sensors,9chemical sensors[10,11] gas sensors,[12,13] temperature sensors[14] and many other cases of detection and sensing
We have successfully demonstrated the efficacy of various machine learning (ML) techniques in estimating the wavelength of any narrow-band incident light in spectrum range 351–1100 nm with high accuracy using the optical transmittance information collected from a few low-cost nanomaterial filters that require minimal control in fabrication
With the available data the k-nearest neighbors (kNN) algorithm shows highest accuracy with the average estimation errors reaching to 0.2 nm over the entire 351–1100 nm spectrum range, where the training set is collected with 1 nm spectral resolution; but this method is not suitable for real-time applications since the required testing time is linearly proportional to the training set size

Summary

Introduction

Nanomaterials are very attractive for building sensors, and various examples of using 2D nanomaterials, nano-tubes, quantum-dots, etc., can be found in the fabrication of optical detectors,[1,2,3] molecular and bio-sensors,[4,5,6,7,8] ion and radiation sensors,9chemical sensors[10,11] gas sensors,[12,13] temperature sensors[14] and many other cases of detection and sensing. There are many aspects that make nanomaterials promising candidates for these applications compared to the bulk materials. Their enhanced optoelectronic and novel chemical/physical properties make them efficient choices for sensing, while their small dimensions will lead to devices with lower power consumption and smaller size. Nanomaterials are much more attractive than conventional semiconductor sensors due to their low-cost, earth-abundant availability, and compatibility with affordable solution-processable techniques. Their high surface-to-volume ratio makes them highly sensitive as chemical sensors, whereas their quantum confinement or excitonic processes enables them to be excellent target-specific photodetectors. Over the past decades, there has been a tremendous progress in fundamental understanding and proof-of-concept demonstrations of chemical, biological, optical, radiological and a variety of other sensors using nanomaterials. 1–5,7–16

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