Machine-Learning-Aided NO2 Discrimination with an Array of Graphene Chemiresistors Covalently Functionalized by Diazonium Chemistry

Boosted by the emerging need for highly integrated gas sensors in the internet of things (IoT) ecosystems, electronic noses (e-noses) are gaining interest for the detection of specific molecules over a background of interfering gases. The sensing of nitrogen dioxide is particularly relevant for applications in environmental monitoring and precision medicine. Here we present an easy and efficient functionalization procedure to covalently modify graphene layers, taking advantage of diazonium chemistry. Separate graphene layers were functionalized with one of three different aryl rings: 4-nitrophenyl, 4-carboxyphenyl and 4-bromophenyl. The distinct modified graphene layers were assembled with a pristine layer into an e-nose for NO2 discrimination. A remarkable sensitivity to NO2 was demonstrated through exposure to gaseous solutions with NO2 concentrations in the 1-10 ppm range at room temperature. Then, the discrimination capability of the sensor array was tested by carrying out exposure to several interfering gases and analyzing the data through multivariate statistical analysis. This analysis showed that the e-nose can discriminate NO2 among all the interfering gases in a two-dimensional principal component analysis space. Finally, the e-nose was trained to accurately recognize NO2 contributions with a linear discriminant analysis approach, thus providing a metric for discrimination assessment with a prediction accuracy above 95 %.

Targeting biomarkers in the gas phase through a chemoresistive electronic nose based on graphene functionalized with metal phthalocyanines

Electronic noses (e-noses) have received considerable interest in the past decade as they can match the emerging needs of modern society such as environmental monitoring, health screening, and food quality tracking. For practical applications of e-noses, it is necessary to collect large amounts of data from an array of sensing devices that can detect interactions with molecules reliably and analyze them via pattern recognition. The use of graphene (Gr)-based arrays of chemiresistors in e-noses is still virtually missing, though recent reports on Gr-based chemiresistors have disclosed high sensing performances upon functionalization of the pristine layer, opening up the possibility of being implemented into e-noses. In this work, with the aim of creating a robust and chemically stable interface that combines the chemical properties of metal phthalocyanines (M-Pc, M = Fe, Co, Ni, Zn) with the superior transport properties of Gr, an array of Gr-based chemiresistor sensors functionalized with drop-cast M-Pc thin layers has been developed. The sensing capability of the array was tested towards biomarkers for breathomics application, with a focus on ammonia (NH₃). Exposure to NH₃ has been carried out drawing the calibration curve and estimating the detection limit for all the sensors. The discrimination capability of the array has then been tested, carrying out exposure to several gases (hydrogen sulfide, acetone, ethanol, 2-propanol, water vapour and benzene) and analysing the data through principal component analysis (PCA). The PCA pattern recognition results show that the developed e-nose is able to discriminate all the tested gases through the synergic contribution of all sensors.

Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.

Pushing Down the Limit of NH3 Detection of Graphene-Based Chemiresistive Sensors through Functionalization by Thermally Activated Tetrazoles Dimerization

An easy and cost-effective method is presented to functionalize graphene through thermally activated dimerization of 2,5-diaryltetrazoles. Consistently with the experimental spectroscopic results, theoretical calculations demonstrate that during the thermal treatment a dimerization process to tetrazine is energetically more favorable than covalent grafting. Since both the functionalization method by thermal activation and the use of tetrazoles have never been considered before to prepare graphene-based chemiresistors, this represents a promising approach to develop graphene-related sensing platforms. Five different 2,5-diaryltetrazoles have been tested here for the effective functionalization of low-defect graphene layers on silicon nitride. Based on these layers, an array of sensors has been prepared for testing upon ammonia exposure. The tests on the sensing performances clearly show sensitivity to ammonia, extending the current range of ammonia detection with a graphene-based chemiresistor down to the sub-ppm range, as results from a benchmarking with data available in the literature. Furthermore, all sensors perform better than bare graphene. Density functional theory (DFT) calculations, carried out on a model of the best performing layer of the array, provided the theoretical framework to rationalize the sensing mechanism and disclose a dual role played by the tetrazine molecules, (i) acting as ammonia concentrators and (ii) mediating the electron transfer between ammonia and graphene.

Chemical Defect-Driven Response on Graphene-Based Chemiresistors for Sub-ppm Ammonia Detection

Gas sensors are essential in several fields and, in general, features such as high sensitivity, quick response, and fast recovery are required, along with low power consumption and low cost. Graphene is considered a promising material for gas sensing applications, its functionalization often being a requisite. In the present study, we developed competitive and promising gas sensors for ammonia detection. Interestingly, we present an easy and efficient method to functionalize graphene by using diazonium chemistry with different functional groups. Moreover, we prove the superior sensing capability of our covalently modified graphene layers. These experimental data have been consistently interpreted by theoretical calculations, which reveal a defect-driven sensor's response to ammonia. These results open the possibility of a comprehensive design and use of these graphene-based sensors in real applications.

Enhancement of Room Temperature Ethanol Sensing by Optimizing the Density of Vertically Aligned Carbon Nanofibers Decorated with Gold Nanoparticles

An ethanol gas sensor based on carbon nanofibers (CNFs) with various densities and nanoparticle functionalization was investigated. The CNFs were grown by means of a Plasma-Enhanced Chemical Vapor Deposition (PECVD), and the synthesis conditions were varied to obtain different number of fibers per unit area. The devices with a larger density of CNFs lead to higher responses, with a maximal responsivity of 10%. Furthermore, to simultaneously improve the sensitivity and selectivity, CNFs were decorated with gold nanoparticles by an impaction printing method. After metal decoration, the devices showed a response 300% higher than pristine devices toward 5 ppm of ethanol gas. The morphology and structure of the different samples deposited on a silicon substrate were characterized by TEM, EDX, SEM, and Raman spectroscopy, and the results confirmed the presence of CNF decorated with gold. The influence of operating temperature (OT) and humidity were studied on the sensing devices. In the case of decorated samples with a high density of nanofibers, a less-strong cross-sensitivity was observed toward a variation in humidity and temperature.

Exploring the performance of a functionalized CNT-based sensor array for breathomics through clustering and classification algorithms: from gas sensing of selective biomarkers to discrimination of chronic obstructive pulmonary disease

An array of carbon nanotube (CNT)-based sensors was produced for sensing selective biomarkers and evaluating breathomics applications with the aid of clustering and classification algorithms. We assessed the sensor array performance in identifying target volatiles and we explored the combination of various classification algorithms to analyse the results obtained from a limited dataset of exhaled breath samples. The sensor array was exposed to ammonia (NH₃), nitrogen dioxide (NO₂), hydrogen sulphide (H₂S), and benzene (C₆H₆). Among them, ammonia (NH₃) and nitrogen dioxide (NO₂) are known biomarkers of chronic obstructive pulmonary disease (COPD). Calibration curves for individual sensors in the array were obtained following exposure to the four target molecules. A remarkable response to ammonia (NH₃) and nitrogen dioxide (NO₂), according to benchmarking with available data in the literature, was observed. Sensor array responses were analyzed through principal component analysis (PCA), thus assessing the array selectivity and its capability to discriminate the four different target volatile molecules. The sensor array was then exposed to exhaled breath samples from patients affected by COPD and healthy control volunteers. A combination of PCA, supported vector machine (SVM), and linear discrimination analysis (LDA) shows that the sensor array can be trained to accurately discriminate healthy from COPD subjects, in spite of the limited dataset.

Extensive application of clustering and classification algorithms shows the potential of a CNT-based sensor array in breathomics.

Low-fouling CNT-PEG-hydrogel coated quartz crystal microbalance sensor for saliva glucose detection

Saliva glucose detection based on a quartz crystal microbalance (QCM) sensor has emerged as a promising tool and a non-invasive diagnostic technique for diabetes. However, the low glucose concentration and strong protein interference in the saliva hinder the QCM sensors from practical applications. In this study, we present a robust and simple anti-fouling CNT-PEG-hydrogel film-coated QCM sensor for the detection of saliva glucose with high sensitivity. The CNT-PEG-hydrogel film consists of two layers; the bottom base PBA-hydrogel film is designed to recognize the glucose while the top CNT-PEG layer is used to restrict protein adsorption and improve the biocompatibility. Our results show that this CNT-PEG-hydrogel film exhibited a 10-fold enhancement on the detection limit compared to the PBA-hydrogel. Meanwhile, the adsorption of proteins on the surface of the CNT-PEG-hydrogel film, including bovine serum albumin (BSA), mucin (MUC), and fibrinogen (FIB), were reduced by 99.1%, 77.8%, and 83.7%, respectively. The CNT-PEG-hydrogel film could detect the typical saliva glucose level (0-50 mg L-1) in 10% saliva with a good responsivity. To sum up, this new tool with low-fouling film featuring high stability, specificity, and selectivity holds great potential for non-invasive monitoring of saliva glucose in human physiological levels.

On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials

This paper presents a methodology to quantify oxidizing and reducing gases using n-type and p-type chemiresistive sensors, respectively. Low temperature sensor heating with pulsed UV or visible light modulation is used together with the application of the fast Fourier transform (FFT) to extract sensor response features. These features are further processed via principal component analysis (PCA) and principal component regression (PCR) for achieving gas discrimination and building concentration prediction models with R² values up to 98% and RMSE values as low as 5% for the total gas concentration range studied. UV and visible light were used to study the influence of the light wavelength in the prediction model performance. We demonstrate that n-type and p-type sensors need to be used together for achieving good quantification of oxidizing and reducing species, respectively, since the semiconductor type defines the prediction model’s effectiveness towards an oxidizing or reducing gas. The presented method reduces considerably the total time needed to quantify the gas concentration compared with the results obtained in a previous work. The use of visible light LEDs for performing pulsed light modulation enhances system performance and considerably reduces cost in comparison to previously reported UV light-based approaches.

Gold-carbonaceous materials based heterostructures for gas sensing applications

The carbon nanostructures such as carbon nanotubes and graphene decorated with gold nanoparticles exhibit promising gas sensing applications with enhanced sensitivity.

Gas Sensing with Solar Cells: The Case of NH3 Detection through Nanocarbon/Silicon Hybrid Heterojunctions

Photovoltaic (PV) cells based on single-walled carbon nanotube (SWCNT)/silicon (Si) and multiwalled carbon nanotube (MWCNT)/Si junctions were tested under exposure to NH₃ in the 0-21 ppm concentration range. The PV cell parameters remarkably changed upon NH₃ exposure, suggesting that these junctions, while being operated as PV cells, can react to changes in the environment, thereby acting as NH₃ gas sensors. Indeed, by choosing the open-circuit voltage, V_OC, parameter as read-out, it was found that these cells behaved as gas sensors, operating at room temperature with a response higher than chemiresistors developed on the same layers. The sensitivity was further increased when the whole current-voltage (I-V) curve was collected and the maximum power values were tracked upon NH₃ exposure.

A nanoscale paper-based near-infrared optical nose (NIRON)

Electronic noses (e-nose) and optical noses (o-nose) are two emerging approaches for the development of artificial olfactory systems for flavor and smell evaluation. The current work leverages the unique optical properties of semiconducting single-wall carbon nanotubes (SWCNTs) to develop a prototype of a novel paper-based near-infrared optical nose (NIRON). We have drop-dried an array of SWCNTs encapsulated with a wide variety of peptides on a paper substrate and continuously imaged the emitted SWCNTs fluorescence using a CMOS camera. Odors and different volatile molecules were passed above the array in a flow chamber, resulting in unique modulation patterns of the SWCNT photoluminescence (PL). Quartz crystal microbalance (QCM) measurements performed in parallel confirmed the direct binding between the vapor molecules and the peptide-SWCNTs. PL levels measured before and during exposure demonstrate distinct responses to the four tested alcoholic vapors (ethanol, methanol, propanol, and isopropanol). In addition, machine learning tools directly applied to the fluorescence images allow us to distinguish between the aromas of red wine, beer, and vodka. Further, we show that the developed sensor can detect limonene, undecanal, and geraniol vapors, and differentiate between their smells utilizing the PL response pattern. This novel paper-based optical biosensor provides data in real-time, and is recoverable and suitable for working at room temperature and in a wide range of humidity levels. This platform opens new avenues for real-time sensing of volatile chemical compounds, odors, and flavors.