Computational Optimization of Metal-Organic Framework (MOF) Arrays for Chemical Sensing

Although commercial gas sensors exist for applications such as product quality control, industrial food monitoring, and smoke detection, there are many potential applications for which adequate gas sensing technology is lacking. There is an unmet need for gas sensors to detect natural gas leaks, for disease detection via breath analysis, and for environmental monitoring, to name just a few examples. Current gas sensors do not exhibit the sensitivity and/or selectivity required to detect trace amounts of the required gases in complex gas mixture environments (e.g., ambient air or a patient’s breath). It is known that arrays of sensors, or electronic noses, improve chemical detection when compared to single sensor elements. Although some work has been done to optimize sensor device performance, there are many potential sensing materials that have not yet been extensively explored. Herein, we explore the use of metal-organic framework (MOF) materials in sensor arrays, exploiting their high adsorption capabilities to yield more selective and sensitive electronic noses. As a relatively new class of materials, MOFs have not been thoroughly investigated for gas sensing applications. In particular, prior to our work, there had only been a few investigations of MOF sensor arrays and those were limited to purely experimental work that relied heavily on trial-and-error. We demonstrate that leveraging computational modeling and optimization to rationally design MOF sensor arrays can yield significantly improved sensing performance. Our novel computational method was carried out first by predicting individual MOF sensor responses via molecular simulations. Then, we developed a method to analyze those individual responses and provide output signals for entire sensor arrays to predict unknown gas mixtures. Following this, the prediction ability of each array was evaluated according to the Kullback-Liebler divergence (KLD), where we determined the best arrays for detecting methane-in-air mixtures. Finally, we developed and validated a genetic algorithm that enables the optimization of large MOF arrays

Applications and Advances in Electronic-Nose Technologies

Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Recent applications of electronic nose technologies have come through advances in sensor design, material improvements, software innovations and progress in microcircuitry design and systems integration. The invention of many new e-nose sensor types and arrays, based on different detection principles and mechanisms, is closely correlated with the expansion of new applications. Electronic noses have provided a plethora of benefits to a variety of commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields. Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes. This paper is a review of the major electronic-nose technologies, developed since this specialized field was born and became prominent in the mid 1980s, and a summarization of some of the more important and useful applications that have been of greatest benefit to man

Development of Surface Acoustic Wave Electronic Nose

The paper proposes an effective method to design and develop surface acoustic wave (SAW) sensor array-based electronic nose systems for specific target applications. The paper suggests that before undertaking full hardware development empirically through hit and trial for sensor selection, it is prudent to develop accurate sensor array simulator for generating synthetic data and optimising sensor array design and pattern recognition system. The latter aspects are most time-consuming and cost-intensive parts in the development of an electronic nose system. This is because most of the electronic sensor platforms, circuit components, and electromechanical parts are available commercially-off-the-shelve (COTS), whereas knowledge about specific polymers and data analysis software are often guarded due to commercial or strategic interests. In this study, an 11-element SAW sensor array is modelled to detect and identify trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapours in the presence of toluene, benzene, di-methyl methyl phosphonate (DMMP) and humidity as interferents. Additive noise sources and outliers were included in the model for data generation. The pattern recognition system consists of: (i) a preprocessor based on logarithmic data scaling, dimensional autoscaling, and singular value decomposition-based denoising, (ii) principal component analysis (PCA)-based feature extractor, and (iii) an artificial neural network (ANN) classifier. The efficacy of this approach is illustrated by presenting detailed PCA analysis and classification results under varied conditions of noise and outlier, and by analysing comparative performance of four classifiers (neural network, k-nearest neighbour, naïve Bayes, and support vector machine).Defence Science Journal, 2010, 60(4), pp.364-376, DOI:http://dx.doi.org/10.14429/dsj.60.49

Advancements in microfabricated gas sensors and microanalytical tools for the sensitive and selective detection of odors

In recent years, advancements in micromachining techniques and nanomaterials have enabled the fabrication of highly sensitive devices for the detection of odorous species. Recent efforts done in the miniaturization of gas sensors have contributed to obtain increasingly compact and portable devices. Besides, the implementation of new nanomaterials in the active layer of these devices is helping to optimize their performance and increase their sensitivity close to humans’ olfactory system. Nonetheless, a common concern of general-purpose gas sensors is their lack of selectivity towards multiple analytes. In recent years, advancements in microfabrication techniques and microfluidics have contributed to create new microanalytical tools, which represent a very good alternative to conventional analytical devices and sensor-array systems for the selective detection of odors. Hence, this paper presents a general overview of the recent advancements in microfabricated gas sensors and microanalytical devices for the sensitive and selective detection of volatile organic compounds (VOCs). The working principle of these devices, design requirements, implementation techniques, and the key parameters to optimize their performance are evaluated in this paper. The authors of this work intend to show the potential of combining both solutions in the creation of highly compact, low-cost, and easy-to-deploy platforms for odor monitoringPostprint (published version

Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples

Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System

We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the remainder being a temperature and a humidity sensor, respectively. To identify a new analyte sample and then to estimate its concentration, we use both some machine learning techniques and the least square regression principle. In fact, we apply two different training models; the first one is based on the Support Vector Machine (SVM) approach and is aimed at teaching the system how to discriminate among different gases, while the second one uses the least squares regression approach to predict the concentration of each type of analyte

Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

Electronic nose implementation for biomedical applications

The growing rate of diabetes and undiagnosed diabetes related diseases is becoming a worldwide major health concern. The motivation of this thesis was to make use of a technology called the ‘electronic nose’ (eNose) for diagnosing diseases. It presents a comprehensive study on metabolic and gastro-intestinal disorders, choosing diabetes as a target disease. Using eNose technology with urinary volatile organic compounds (VOCs) is attractive as it allows non-invasive monitoring of various molecular constituents in urine. Trace gases in urine are linked to metabolic reactions and diseases. Therefore, urinary volatile compounds were used for diagnosis purposes in this thesis. The literature on existing eNose technologies, their pros and cons and applications in biomedical field was thoroughly reviewed, especially in detecting headspace of urine. Since the thesis investigates urinary VOCs, it is important to discover the stability of urine samples and their VOCs in time. It was discovered that urine samples lose their stability and VOCs emission after 9 months. A comprehensive study with 137 diabetic and healthy control urine samples was done to access the capability of commercially available eNose instruments for discrimination between these two groups. Metal oxide gas sensor based commercial eNose (Fox 4000, AlphaMOS Ltd) and field asymmetric ion mobility spectrometer (Lonestar, Owlstone Ltd) were used to analyse volatiles in urinary headspace. Both technologies were able to distinguish both groups with sensitivity and specificity of more than 90%. Then the project moved onto developing a Non-dispersive infrared (NDIR) sensor system that is non-invasive, low-cost, precise, rapid, simple and patient friendly, and can be used at both hospitals and homes. NDIR gas sensing is one of the most widely used optical gas detection techniques. NDIR system was used for diagnosing diabetes and gastro related diseases from patient’s wastes. To the best of the authors’ knowledge, this is the first and only developed tuneable NDIR eNose system. The developed optical eNose is able to scan the whole infrared range between 3.1μm and 10.5 μm with step size of 20 nm. To simulate the effect of background humidity and temperature on the sensor response, a gas test rig system that includes gas mixture, VOC generator, humidity generator and gas analyser was designed to enable the user to have control of gas flow, humidity and temperature. This also helps to find out system’s sensitivity and selectivity. Finally, after evaluating the sensitivity and selectivity of optical eNose, it was tested on simple and complex odours. The results were promising in discriminating the odours. Due to insufficient sample batches received from the hospital, synthetic urine samples were purchased, and diabetic samples were artificially made. The optical eNose was able to successfully separate artificial diabetic samples from non-diabetic ones

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables