Development of a Sensing Array for Human Breath Analysis Based on SWCNT Layers Functionalized with Semiconductor Organic Molecules

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000 ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800 ppb) to people at high risk of DKA (>75 ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations in simulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.

Glucose-metabolism-triggered colorimetric sensor array for point-of-care differentiation and antibiotic susceptibility testing of bacteria

Simple and sensitive discrimination of multiple bacteria and antimicrobial susceptibility test (AST) are significant for food safety, clinical diagnosis and treatment. Herein, based on different metabolic ability of bacteria on glucose, we presented a colorimetric sensor array for point-of-care testing (POCT) of multiple bacteria with methyl red (MER), bromothymol blue (BTB) and bromocresol green (BCG) as probes. Different bacteria resulted in different color changes of three probes, which was converted to RGB (Red (R)/Green (G)/Blue (B)) signals by the color recognizer APP loaded on smartphone. The sensor array performed differentiation of eleven species of bacteria, achieving the quantitative analysis of individual bacteria in tap water and differentiation of bacterial mixtures. Interestingly, the sensor array can be used for AST and evaluating minimal inhibitory concentration (MIC) of antibiotics to bacteria. The research provided meaningful guidance for distinguishing multiple bacteria and evaluating MIC, presenting great potential in practical application.

Hydrophobic functionalization of a metal-organic framework as an ammonia visual sensing material under high humidity conditions

Since exhaled ammonia (NH3) is one of the metabolic markers of liver and kidney diseases, ammonia visual sensing materials in humid environments have received extensive attention and investigation. Herein, through a tailor-made pore environment provided by metal-organic framework (MOF) materials (CH3-Cu(BDC)), we achieved NH3 anti-interference sensing with apparent color changing under humid conditions. With methyl (CH3-) functionalization, CH3-Cu(BDC) demonstrated a strong response for trace ammonia and showed high selectivity under a humid environment. Grand canonical Monte Carlo (GCMC) simulations indicated that CH3-Cu(BDC) showed stronger attraction towards NH3 molecules than H2O. Benefiting from the target changing coordination environment, CH3-Cu(BDC) showed a rapid response and simple analysis properties for patients' exhaled air. The strategy used in this study not only provides a demonstration case for NH3 colorimetric sensing with high humidity and anti-interference but also introduces a new method for painless and quick exhaled breath analysis for diagnosis of patients with kidney and liver diseases.

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 %.

p-Type Functionalized Carbon Nanohorns and Nanotubes in Perovskite Solar Cells

The incorporation of p-type functionalized carbon nanohorns (CNHs) in perovskite solar cells (PSCs) and their comparison with p-type functionalized single- and double-walled carbon nanotubes (SWCNTs and DWCNTs) are reported in this study for the first time. These p-type functionalized carbon nanomaterial (CNM) derivatives were successfully synthesized by [2 + 1] cycloaddition reaction with nitrenes formed from triphenylamine (TPA) and 9-phenyl carbazole (Cz)-based azides, yielding CNHs-TPA, CNHs-Cz, SWCNTs-Cz, SWCNTs-TPA, DWCNTs-TPA, and DWCNTs-Cz. These six novel CNMs were incorporated into the spiro-OMeTAD-based hole transport layer (HTL) to evaluate their impact on regular mesoporous PSCs. The photovoltaic results indicate that all p-type functionalized CNMs significantly improve the power conversion efficiency (PCE), mainly by enhancing the short-circuit current density (Jsc) and fill factor (FF). TPA-functionalized derivatives increased the PCE by 12-17% compared to the control device without CNMs, while Cz-functionalized derivatives resulted in a PCE increase of 4-8%. Devices prepared with p-type functionalized CNHs exhibited a slightly better PCE compared with those based on SWCNTs and DWCNTs derivatives. The increase in hole mobility of spiro-OMeTAD, additional p-type doping, better energy alignment with the perovskite layer, and enhanced morphology and contact interface play important roles in enhancing the performance of the device. Furthermore, the incorporation of p-type functionalized CNMs into the spiro-OMeTAD layer increased device stability by improving the hydrophobicity of the layer and enhancing the hole transport across the MAPI/spiro-OMeTAD interface. After 28 days under ambient conditions and darkness, TPA-functionalized CNMs maintained the performance of the device by over 90%, while Cz-functionalized CNMs preserved it between 75 and 85%.

Treatment of Multi-Walled Carbon Nanotubes with Dichromic Acid: Oxidation and Appearance of Intercalation

This work is dedicated to the study of the treatment of multi-walled carbon nanotubes (MWCNTs) with dichromic acid. The dichromic acid was formed by dissolving different concentrations of CrO3 in water. The effect of the concentration of dichromic acid on the change in texture characteristics, elemental composition, defectiveness, graphitization degree, and surface chemistry of MWCNTs was investigated using various analytical techniques, such as transmission electron microscopy, energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction, and X-ray photoelectron spectroscopy (XPS). Testing of MWCNTs as electrodes for supercapacitors in 3.5 M H2SO4 solution was carried out using cyclic voltammetry. A decrease in the average diameter of CNTs after treatment was found. The EDX and XPS showed that the oxygen content on the surface of MWCNTs increased after treatment with dichromic acid. The formation of Cr2O3 after treatment with dichromic acid was detected by XPS. High angle annular dark field scanning transmission electron microscopy was used to confirm the intercalation of the chromium-containing compound between graphene layers of MWCNTs after treatment with dichromic acid. It was found that two different types of MWCNTs showed diverse behavior after treatment. The highest specific capacitance of the MWCNTs after treatment was 141 F g-1 (at 2 mV s-1) compared to 0.3 F g-1 for the untreated sample.

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

Peroxidase-Mimicking DNAzymes as Receptors for Label-Free Discriminating Heavy Metal Ions by Chemiluminescence Sensor Arrays

Receptors are crucial to the analytical performance of sensor arrays. Different from the previous receptors in sensor arrays, herein, peroxidase-mimicking DNAzymes were innovatively used as receptors to develop a label-free chemiluminescence sensor array for discriminating various heavy metal ions in complex samples. The peroxidase-mimicking DNAzymes are composed of functional oligonucleotides and hemin, including G-triplex-hemin DNAzyme (G3-DNAzyme), G-quadruplex-hemin DNAzyme (G4-DNAzyme), and the dimer of G-quadruplex-hemin DNAzyme (dG4-DNAzyme). Circular dichroism (CD) spectroscopy demonstrated that different metal ions diversely affect the conformation of G-quadruplex and G-triplex, resulting in a change in the activity of peroxidase-mimicking DNAzyme. Thus, the unique fingerprints formed to easily discriminate seven kinds of heavy metal ions by principal component analysis (PCA) within 20 min. The discrimination of unknown metal ions in tap water further confirmed its ability for discriminating multiple heavy metal ions. Moreover, it will not bring water pollution due to the good biocompatibility of DNA. Therefore, it not only merely offers a label-free, rapid, environment-friendly, and cheap (1.49 $) sensor assay for discriminating metal ions but also comes up with an innovative way for developing sensor arrays.

A Chemiresistor Sensor Array Based on Graphene Nanostructures: From the Detection of Ammonia and Possible Interfering VOCs to Chemometric Analysis

Sensor arrays are currently attracting the interest of researchers due to their potential of overcoming the limitations of single sensors regarding selectivity, required by specific applications. Among the materials used to develop sensor arrays, graphene has not been so far extensively exploited, despite its remarkable sensing capability. Here we present the development of a graphene-based sensor array prepared by dropcasting nanostructure and nanocomposite graphene solution on interdigitated substrates, with the aim to investigate the capability of the array to discriminate several gases related to specific applications, including environmental monitoring, food quality tracking, and breathomics. This goal is achieved in two steps: at first the sensing properties of the array have been assessed through ammonia exposures, drawing the calibration curves, estimating the limit of detection, which has been found in the ppb range for all sensors, and investigating stability and sensitivity; then, after performing exposures to acetone, ethanol, 2-propanol, sodium hypochlorite, and water vapour, chemometric tools have been exploited to investigate the discrimination capability of the array, including principal component analysis (PCA), linear discriminant analysis (LDA), and Mahalanobis distance. PCA shows that the array was able to discriminate all the tested gases with an explained variance around 95%, while with an LDA approach the array can be trained to accurately recognize unknown gas contribution, with an accuracy higher than 94%.

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.

Bio-Inspired Synthesis of Carbon-Based Nanomaterials and Their Potential Environmental Applications: A State-of-the-Art Review

Providing safe drinking water and clean water is becoming a more challenging task all around the world. Although some critical issues and limits remain unsolved, implementing ecologically sustainable nanomaterials (NMs) with unique features, e.g., highly efficient and selective, earth-abundance, renewability, low-cost manufacturing procedures, and stability, has become a priority. Carbon nanoparticles (NPs) offer tremendous promise in the sectors of energy and the environment. However, a series of far more ecologically friendly synthesis techniques based on natural, renewable, and less expensive waste resources must be explored. This will reduce greenhouse gas emissions and harmful material extraction and assist the development of green technologies. The progress achieved in the previous 10 years in the fabrication of novel carbon-based NMs utilizing waste materials as well as natural precursors is reviewed in this article. Research on carbon-based NPs and their production using naturally occurring precursors and waste materials focuses on this review research. Water treatment and purification using carbon NMs, notably for industrial and pharmaceutical wastes, has shown significant potential. Research in this area focuses on enhanced carbonaceous NMs, methods, and novel nano-sorbents for wastewater, drinking water, groundwater treatment, as well as ionic metal removal from aqueous environments. Discussed are the latest developments and challenges in environmentally friendly carbon and graphene quantum dot NMs.

NO2 Sensing Behavior of Compacted Chemically Treated Multi-Walled Carbon Nanotubes

This article is devoted to the investigation of the sensing behavior of chemically treated multi-walled carbon nanotubes (MWNTs) at room temperature. Chemical treatment of MWNTs was carried out with a solution of either sulfuric or chromic acids. The materials obtained were investigated by transmission electron microscopy, scanning electron microscopy, Raman-spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy. The active layer of chemiresistive gas sensors was obtained by cold pressing (compaction) at 11 MPa of powders of bare and treated multi-walled carbon nanotubes. The sensing properties of pellets were investigated using a custom dynamic type of station at room temperature (25 ± 2 °C). Detection of NO₂ was performed in synthetic air (79 vol% N₂, 21 vol% O₂). It was found that the chemical treatment significantly affects the sensing properties of multi-walled carbon nanotubes, which is indicated by increasing the response of the sensors toward 100-500 ppm NO₂ and lower concentrations.

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.

Combined DFT and Molecular Mechanics Modeling of the Adsorption of Semiconducting Molecules on an Ionic Substrate: PTCDA and CuPc on NaCl

Experimental results suggest that molecular geometry and energies can be influenced by the presence of thin film substrates as well as surrounding molecules. It is imperative that computational models take this influence into account. The accurate computational modeling of these molecules is an efficient way of carrying out chemistry calculations and reinforcing experimental findings. In our study, density functional theory (DFT) and molecular mechanics (MM) are used to model the configurations of the organic semiconducting materials, 3,4,9,10-perylene tetracarboxylic dianhydride, C₂₄H₈O₆ (PTCDA), and copper(II) phthalocyanine, C₃₂H₁₆CuN₈ (CuPc), as adsorbed on single- and double-layer NaCl substrates of various dimensions and charge settings. After a geometry and charge optimization of the molecules using DFT, the molecular geometries are optimized under different environments using computational calculations with specific force-field settings in HyperChem Professional 8.0(TM) software using MM. Energies and geometries of the molecules are then recorded, and our data are compared to experimental results of similar systems. We find that, with the appropriate choice of substrate properties, the calculated molecular configurations directly reflect those found experimentally. Our results support the idea that this method of simulation can produce reliable models in the field of physical chemistry.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Highly sensitive and chemically stable NH3 sensors based on an organic acid-sensitized cross-linked hydrogel for exhaled breath analysis

Due to interference by the high moisture content and complicated compositions of human exhaled breath, the trace-level detection of ammonia (NH₃) with desirable selectivity and stability is a large challenge for exhaled breath analysis. Carboxyl-sensitized hydrogels can be activated by moisture to exhibit a significant response and excellent selectivity to NH₃. However, the high activity of carboxyl groups in hydrogels is a double-edged sword, resulting in poor chemical stability during NH₃ detection. Herein, organic acids were embedded into a cross-linked poly(ethylene glycol) diacrylate (PEGDA) hydrogel via thiol-ene photochemistry to form stable hydrogels for NH₃ detection in a humid atmosphere. As a result, under high humidity conditions (80% RH), the optimal sensors exhibited superior selectivity to NH₃ among various interfering gas species, a remarkably high NH₃ response (Z_a/Z_g=6.20) towards 20 ppm NH₃, and an extremely low actual detection limit (50 ppb) at room temperature. Moreover, the sensors exhibited excellent chemical stability due to the moderate equilibrium water content of the hydrogel composites and acid dissociation constant of the acid groups. The moisture-activated NH₃ sensing mechanism was thoroughly investigated by complex impedance spectroscopy (CIS), quartz crystal microbalance (QCM) measurements, Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). To explore the application prospects of cross-linked hydrogel sensors for detecting NH₃ in exhaled breath, a simulated exhaled breath test was also performed.

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.

Recent Progress of Nanostructured Sensing Materials from 0D to 3D: Overview of Structure-Property-Application Relationship for Gas Sensors

Along with the progress of nanoscience and nanotechnology, nanomaterials with attractive structural and functional properties have gained more attention than ever before, especially in the field of electronic sensors. In recent years, the gas sensing devices have made great achievement and also created wide application prospects, which leads to a new wave of research for designing advanced sensing materials. There is no doubt that the characteristics are highly governed by the sensitive layers. For this reason, important advances for the outstanding, novel sensing materials with different dimensional structures including 0D, 1D, 2D, and 3D are reported and summarized systematically. The sensing materials cover noble metals, metal oxide semiconductors, carbon nanomaterials, metal dichalcogenides, g-C₃ N₄ , MXenes, and complex composites. Discussion is also extended to the relation between sensing performances and their structure, electronic properties, and surface chemistry. In addition, some gas sensing related applications are also highlighted, including environment monitoring, breath analysis, food quality and safety, and flexible wearable electronics, from current situation and the facing challenges to the future research perspectives.

Recent Advances in Ammonia Gas Sensors Based on Carbon Nanomaterials

This review paper is devoted to an extended analysis of ammonia gas sensors based on carbon nanomaterials. It provides a detailed comparison of various types of active materials used for the detection of ammonia, e.g., carbon nanotubes, carbon nanofibers, graphene, graphene oxide, and related materials. Different parameters that can affect the performance of chemiresistive gas sensors are discussed. The paper also gives a comparison of the sensing characteristics (response, response time, recovery time, operating temperature) of gas sensors based on carbon nanomaterials. The results of our tests on ammonia gas sensors using various techniques are analyzed. The problems related to the recovery of sensors using various approaches are also considered. Finally, the impact of relative humidity on the sensing behavior of carbon nanomaterials of various different natures was estimated.

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.