Ionic Liquid-Carbon Nanotube Sensor Arrays for Human Breath Related Volatile Organic Compounds

Mechanisms and effects of gas intercalation into ionic liquids confined within charged nanoscale volumes

Understanding the behavior of gas within confined ionic liquids (ILs) is important for a wide range of emerging energy, separation, and sensing technologies. However, the mechanisms governing gas solubility and molecular structure within these systems remain largely unknown. Here, we investigate the factors that dictate the intercalation and arrangement of CO2, N2 and O2, in a commonly used IL (1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM+][PF6-]) confined within neutral and charged 2.1 nm diameter carbon nanotubes (CNTs) via molecular dynamics simulations and enhanced free energy sampling methods. Our simulations show that the gas selectivity in these systems can be explained by a competitive complex interplay between confinement, charge state of CNTs, and IL properties. We then experimentally validate a subset of these predictions using a novel device consisting of electrically addressable, IL-infilled CNTs which we expose to CO2 and O2 in a N2 background. Our findings help to disentangle the relative importance of tuning gas solubility and preferential proximity to the CNT wall for maximizing measurable changes of electrochemical signals. These insights provide a foundation for engineering future electrochemical systems utilized in gas sensing or separation applications.

Mechanistic Analysis of Peptide Affinity to Single-Walled Carbon Nanotubes and Volatile Organic Compounds Using Chemiresistors

Peptides, due to their diverse and controllable properties, are used as both liquid and gas phase recognition elements for both biological and chemical targets. While it is well understood how binding of a peptide to a biomolecule can be converted into a sensing event, there is not the same mechanistic level of understanding with regard to how peptides modulate the selectivity of semiconductor/conductor-based gas sensors. Notably, a rational, mechanistic study has not yet been performed to correlate peptide properties to the sensor response for volatile organic compounds (VOCs) as a function of chemical properties. Here, we have designed a peptide that has (1) two amino acid residues that bind the sensor surface, (2) two flexible linkers (GG) that eliminate steric strain, and (3) a five amino-acid repeat that can bind the analyte of interest either by formation of a binding pocket (such as from peptides selected by phage display) or by forming a semiselective adsorption layer. The nine peptide sequences containing both a six amino acid constant sequence (WGGWGG) and a five amino acid variable sequence (XXXXX) were synthesized, and their impact on the selectivity and sensitivity of carbon nanotube (CNT) gas sensors was explored. The response of each sensor to the following VOCs with diverse chemical properties: isopropyl alcohol (polar protic), acetone (polar aprotic), isoprene (nonpolar, linear hydrocarbon), and toluene (nonpolar aromatic), was then recorded and analyzed. This study revealed multiple key factors that influence the response of peptides on CNTs to select VOCs. First, the stability of the CNT-peptide aqueous dispersion correlated to the aromaphilicity of the side chain, strongly suggesting that the side chains of peptides are interfacing with the CNT, and not the peptide backbone. Second, the sensing response profile cannot solely be explained by peptides adsorbing to the gas molecules with similar polarities/dielectrics and may instead be due to analyte displacement of the peptide side chain on the CNT surface as measured by changes in the peptide bond orientation using near-edge X-ray absorption fine structure spectroscopy (NEXAFS). These two observations create a new paradigm to explain how peptides confer selectivity to semiconductor-/conductor-based gas sensors and can provide insights into future design and implementation of peptide-coated solid state sensors for gas targets.

Recent advances in biosensors detecting biomarkers from exhaled breath and saliva for respiratory disease diagnosis

The global demand for rapid and non-invasive diagnostic methods for respiratory diseases has significantly intensified due to the wide spread of respiratory infectious diseases. Recent advancements in respiratory disease diagnosis through the analysis of exhaled breath and saliva has attracted great attention all over the world. Among various analytical methods, biosensors can offer non-invasive, efficient, and cost-effective diagnostic capabilities, emerging as promising tools in this area. This review intends to provide a comprehensive overview of various biosensors for the detection of respiratory disease related biomarkers in exhaled breath and saliva. Firstly, the characteristics of exhaled breath and saliva, including their generation, composition, and relevant biomarkers are introduced. Subsequently, the design and application of various biosensors for detecting these biomarkers are presented, along with the innovative materials employed as sensitive components. Different types of biosensors are reviewed, including electrochemical, optical, piezoelectric, semiconductor, and other novel biosensors. At last, the challenges, limitations, and future trends of these biosensors are discussed. It is anticipated that biosensors will play a significant role in respiratory disease diagnosis in the future.

Smart Gas Sensors: Recent Developments and Future Prospective

Gas sensor is an indispensable part of modern society with wide applications in environmental monitoring, healthcare, food industry, public safety, etc. With the development of sensor technology, wireless communication, smart monitoring terminal, cloud storage/computing technology, and artificial intelligence, smart gas sensors represent the future of gas sensing due to their merits of real-time multifunctional monitoring, early warning function, and intelligent and automated feature. Various electronic and optoelectronic gas sensors have been developed for high-performance smart gas analysis. With the development of smart terminals and the maturity of integrated technology, flexible and wearable gas sensors play an increasing role in gas analysis. This review highlights recent advances of smart gas sensors in diverse applications. The structural components and fundamental principles of electronic and optoelectronic gas sensors are described, and flexible and wearable gas sensor devices are highlighted. Moreover, sensor array with artificial intelligence algorithms and smart gas sensors in "Internet of Things" paradigm are introduced. Finally, the challenges and perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

Advances in ionic liquids as fluorescent sensors

Ionic liquids (ILs) are a class of liquid salts with characteristics such as a low melting point, an ionic nature, non-volatility, and tunable properties. Because of their adaptability, they have a significant influence in the field of fluorescence. This paper reviews the primary literature on the use of ILs in fluorescence sensing technologies. The kind of target material is utilized to classify the fluorescence sensors made with the use of ILs. They include using ILs as probes for metals, nitro explosives, small organic compounds, anions, and gases. The efficacy of an IL-based fluorescence sensor depends on the precise design to guarantee specificity, sensitivity, and a consistent reaction to the desired analyte. The precise method can differ depending on the chemical properties of the IL, the choice of fluorophore, and the interactions with the analyte. Overall, the viability of the aforementioned materials for chemical analysis is evaluated, and prospective possibilities for further development are identified.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Temperature-modulated sensing characteristics of ultrafine Au nanoparticle-loaded porous ZnO nanobelts for identification and determination of BTEX

The identification and determination of benzene, toluene, ethylbenzene, and xylene (BTEX) has always been a formidable challenge for chemiresistive metal oxide sensors owing to their structural similarity and low reactivity, as well as the intrinsic cross sensitivity of metal oxides. In this paper, a temperature-modulated sensing strategy is proposed for the identification and determination of BTEX using a high-performance chemiresistive sensor. Ultrafine Au nanoparticle-loaded porous ZnO nanobelts as sensing materials were synthesized through an exchange reaction followed by thermal oxidation, which exhibited high response toward BTEX. Under dynamic modulation of working temperature, the distinguishable characteristic curves were demonstrated for each BTEX compound. By employing the linear discrimination and convolutional neural network analyses, highly effective BTEX identification was achieved among all investigated volatile organic compounds, which is difficult to realize for single chemiresistive sensors at constant working temperatures. Furthermore, quantitative analysis of BTEX concentrations was accomplished by establishing the relationship between concentration and response at specific points on their response curves. This developed strategy is expected to pave a new way for constructing highly sensitive gas sensors for the identification and analysis of hazardous gases, thereby enhancing their applicability in environmental monitoring.

Peptide-Functionalized Carbon Nanotube Chemiresistors: The Effect of Nanotube Density on Gas Sensing

Biorecognition element (BRE)-based carbon nanotube (CNT) chemiresistors have tremendous potential to serve as highly sensitive, selective, and power-efficient volatile organic compound (VOC) sensors. While many research groups have studied BRE-functionalized CNTs in material science and device development, little attention has been paid to optimizing CNT density to improve chemiresistor performance. To probe the effect of CNT density on VOC detection, we present the chemiresistor-based sensing results from two peptide-based CNT devices counting more than 60 different individual measurements. We find that a lower CNT density shows a significantly higher noise level and device-to-device variation while exhibiting mildly better sensitivity. Further investigation with SEM images suggests that moderately high CNT density with a stable connection of the nanotube network is desirable to achieve the best signal-to-noise ratio. Our results show an essential design guideline for tuning the nanotube density to provide sensitive and stable chemiresistors.

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH2O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH2O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. It is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

Pancreatic Cancer and Detection Methods

The pancreas is a vital organ with exocrine and endocrine functions. Pancreatitis is an inflammation of the pancreas caused by alcohol consumption and gallstones. This condition can heighten the risk of pancreatic cancer (PC), a challenging disease with a high mortality rate. Genetic and epigenetic factors contribute significantly to PC development, along with other risk factors. Early detection is crucial for improving PC outcomes. Diagnostic methods, including imagining modalities and tissue biopsy, aid in the detection and analysis of PC. In contrast, liquid biopsy (LB) shows promise in early tumor detection by assessing biomarkers in bodily fluids. Understanding the function of the pancreas, associated diseases, risk factors, and available diagnostic methods is essential for effective management and early PC detection. The current clinical examination of PC is challenging due to its asymptomatic early stages and limitations of highly precise diagnostics. Screening is recommended for high-risk populations and individuals with potential benign tumors. Among various PC screening methods, the N-NOSE plus pancreas test stands out with its high AUC of 0.865. Compared to other commercial products, the N-NOSE plus pancreas test offers a cost-effective solution for early detection. However, additional diagnostic tests are required for confirmation. Further research, validation, and the development of non-invasive screening methods and standardized scoring systems are crucial to enhance PC detection and improve patient outcomes. This review outlines the context of pancreatic cancer and the challenges for early detection.

Immobilizing Ionic Liquids onto Functionalized Surfaces for Sensing Volatile Organic Compounds

Herein, a highly sensitive volatile organic compound (VOC) gas sensor is demonstrated using immobilized ionic liquid (IL), 1-butyl-3-methylimidazolium hexafluorophosphate, onto surfaces functionalized by the quaternary ammonium group -N⁺R, -COOH, and -NH₂, i.e., N⁺-IL, COOH-IL, and NH₂-IL, respectively. These functional groups ensure highly tunable interactions between the IL and surfaces, efficiently modulating the electrical resistance of the immobilized IL upon exposure to acetone and toluene. The immobilized IL to both acetone and toluene displays significant electronic resistance changes at a concentration of 150 ppm, falling in the order NH₂-IL > N⁺-IL > COOH-IL for acetone while COOH-IL > NH₂-IL > N⁺-IL for toluene. A better gaseous sensing ability is achieved in COOH-IL for toluene than acetone, while this does not hold in the case of NH₂-IL and N⁺-IL surfaces because of the completely different ion structuring of the IL at these functionalized surfaces. The accelerated ion mobility in the IL that is immobilized onto functionalized surfaces is also responsible for the strong gaseous sensing response, which is demonstrated further by the atomic force microscopy-measured smaller friction coefficient. This is highly encouraging and suggests that ILs can be immobilized by a network formed by surface functionalization to easily and cheaply detect VOCs at ppm 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.

MoSe2/multiwalled carbon nanotube composite for ammonia sensing in natural humid environment

Herein, we report ammonia sensing in a natural highly humid environment using MoSe₂/multi-walled carbon nanotube (MWCNT) composite as sensing platform. The composite synthesis involved two steps, in the first step, MWCNTs were treated in an acidic medium to obtain -COOH group functionalized MWCNTs. In the second step, functionalized MWCNTs were probe sonicated with MoSe₂ to obtain MoSe₂/MWCNT composite. Proposed device exhibited superior sensing properties at a temperature down to 16^∘ C and relative humidity of 80%. Under these extreme natural environmental conditions, the device exhibited a relative response of 21% for 0.5 ppm of ammonia and superior noise free signal further suggests their use even below this concentration. Composite based device has also displayed better adsorption selectivity towards NH₃ as compared with other reducing and oxidizing gas molecules. Density functional theory simulations were further employed to understand the underlying adsorption process and selectivity behavior of the composite. Simulations predicted lowest negative adsorption energy for ammonia, implying physisorption (-0.387 eV) type exothermic adsorption process. Present results indicate that a composite with the rightly engineered MoSe₂ and MWCNTs weight ratio may serve as a potential candidate for ammonia sensing in a highly humid environment.

The Progress of Research into Flexible Sensors in the Field of Smart Wearables

In modern society, technology associated with smart sensors made from flexible materials is rapidly evolving. As a core component in the field of wearable smart devices (or 'smart wearables'), flexible sensors have the advantages of excellent flexibility, ductility, free folding properties, and more. When choosing materials for the development of sensors, reduced weight, elasticity, and wearer's convenience are considered as advantages, and are suitable for electronic skin, monitoring of health-related issues, biomedicine, human-computer interactions, and other fields of biotechnology. The idea behind wearable sensory devices is to enable their easy integration into everyday life. This review discusses the concepts of sensory mechanism, detected object, and contact form of flexible sensors, and expounds the preparation materials and their applicability. This is with the purpose of providing a reference for the further development of flexible sensors suitable for wearable devices.

Determination of tuberculosis-related volatile organic biomarker methyl nicotinate in vapor using fluorescent assay based on quantum dots and cobalt-containing porphyrin nanosheets

Methyl nicotinate (MN) is a representative and typical volatile organic marker of Mycobacterium tuberculosis, and the specific detection of MN in human breath facilitates non-invasive, rapid, and accurate epidemic screening of tuberculosis infection. Herein, we constructed a fluorescent assay consisted of CdTe quantum dots (QD) and cobalt-metalized tetrakis(4-carboxyphenyl) porphyrin (CoTCPP) nanosheets to determine methyl nicotinate (MN) in vapor samples. Red-emission QD (λ_ex=370 nm, λ_em=658 nm) acts as signal switches whose fluorescence signals can be effectively quenched by CoTCPP nanosheets but restored in the presence of MN. The strategy relied on the distinct binding affinity of cobalt ion and MN. MN restored the fluorescence of QD quenched by CoTCPP in a concentration-dependent manner, which exhibited a well-linear relationship in the range 1-100 μM, and a limit of detection of 0.59 μM. The proposed platform showed sensitivity and selectivity to detect MN in vapor samples with satisfactory RSD below 3.33%. The method is cheap, simple, and relatively rapid (detected within 4 min), which suggests a potential in tuberculosis diagnosis in resource- and professional-lacked areas.

Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO₂, HCHO, and NH₃ and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.

Detection of Covid-19 through a Heptanal Biomarker Using Transition Metal Doped Graphene

A rapid and noninvasive way to monitor the spread of COVID-19 is the detection of SARS-CoV-2 biomarkers from exhaled breath. Heptanal was identified as a key biomarker which was significantly elevated in the breath of SARS-CoV-2 patients. In this study, the adsorption behaviors of heptanal on pristine and transition metal (Pd, Pt, and Ag) doped graphene were studied based on density functional theory. The results indicated that heptanal was weakly adsorbed on pristine graphene with an adsorption energy of -0.015 eV while it was strongly adsorbed on Pd-, Pt-, and Ag-doped graphene with adsorption energies of -0.404, - 0.356, and -0.755 eV, respectively. Also, the electronic properties of Pd-, Pt-, and Ag-doped graphene changed more dramatically after heptanal adsorption than pristine graphene. The recovery times were estimated to be 6.13 × 10-6, 9.57 × 10-7, and 4.83 s for Pd-, Pt-, and Ag-doped graphene, respectively, showing that Pd-, Pt-, and Ag-doped graphene are suitable as reversible sensors. Our results conclude that Pd-, Pt-, and Ag-doped graphene are potential candidates as gas sensors for heptanal detection, and Ag-doped graphene is the most promising one.

Realization of Oriented and Nanoporous Bismuth Chalcogenide Layers via Topochemical Heteroepitaxy for Flexible Gas Sensors

Most van der Waals two-dimensional (2D) materials without surface dangling bonds show limited surface activities except for their edge sites. Ultrathin Bi₂Se₃, a topological insulator that behaves metal-like under ambient conditions, has been overlooked on its surface activities. Herein, through a topochemical conversion process, ultrathin nanoporous Bi₂Se₃ layers were epitaxially deposited on BiOCl nanosheets with strong electronic coupling, leading to hybrid electronic states with further bandgap narrowing. Such oriented nanoporous Bi₂Se₃ layers possessed largely exposed active edge sites, along with improved surface roughness and film forming ability even on inkjet-printed flexible electrodes. Superior room-temperature NO₂ sensing performance was achieved compared to other 2D materials under bent conditions. Our work demonstrates that creating nanoscale features in 2D materials through topochemical heteroepitaxy is promising to achieve both favorable electronic properties and surface activity toward practical applications.

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.

Functional Ionic Liquids Decorated Carbon Hybrid Nanomaterials for the Electrochemical Biosensors

Ionic liquids are gaining high attention due to their extremely unique physiochemical properties and are being utilized in numerous applications in the field of electrochemistry and bio-nanotechnology. The excellent ionic conductivity and the wide electrochemical window open a new avenue in the construction of electrochemical devices. On the other hand, carbon nanomaterials, such as graphene (GR), graphene oxide (GO), carbon dots (CDs), and carbon nanotubes (CNTs), are highly utilized in electrochemical applications. Since they have a large surface area, high conductivity, stability, and functionality, they are promising in biosensor applications. Nevertheless, the combination of ionic liquids (ILs) and carbon nanomaterials (CNMs) results in the functional ILs-CNMs hybrid nanocomposites with considerably improved surface chemistry and electrochemical properties. Moreover, the high functionality and biocompatibility of ILs favor the high loading of biomolecules on the electrode surface. They extremely enhance the sensitivity of the biosensor that reaches the ability of ultra-low detection limit. This review aims to provide the studies of the synthesis, properties, and bonding of functional ILs-CNMs. Further, their electrochemical sensors and biosensor applications for the detection of numerous analytes are also discussed.

A flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.

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.

Bifunctional Metal Meshes Acting as a Semipermeable Membrane and Electrode for Sensitive Electrochemical Determination of Volatile Compounds

The monitoring of toxic inorganic gases and volatile organic compounds has brought the development of field-deployable, sensitive, and scalable sensors into focus. Here, we attempted to meet these requirements by using concurrently microhole-structured meshes as (i) a membrane for the gas diffusion extraction of an analyte from a donor sample and (ii) an electrode for the sensitive electrochemical determination of this target with the receptor electrolyte at rest. We used two types of meshes with complementary benefits, i.e., Ni mesh fabricated by robust, scalable, and well-established methods for manufacturing specific designs and stainless steel wire mesh (SSWM), which is commercially available at a low cost. The diffusion of gas (from a donor) was conducted in headspace mode, thus minimizing issues related to mesh fouling. When compared with the conventional polytetrafluoroethylene (PTFE) membrane, both the meshes (40 μm hole diameter) led to a higher amount of vapor collected into the electrolyte for subsequent detection. This inedited fashion produced a kind of reverse diffusion of the analyte dissolved into the electrolyte (receptor), i.e., from the electrode to bulk, which further enabled highly sensitive analyses. Using Ni mesh coated with Ni(OH)₂ nanoparticles, the limit of detection reached for ethanol was 24-fold lower than the data attained by a platform with a PTFE membrane and placement of the electrode into electrolyte bulk. This system was applied in the determination of ethanol in complex samples related to the production of ethanol biofuel. It is noteworthy that a simple equation fitted by machine learning was able to provide accurate assays (accuracies from 97 to 102%) by overcoming matrix effect-related interferences on detection performance. Furthermore, preliminary measurements demonstrated the successful coating of the meshes with gold films as an alternative raw electrode material and the monitoring of HCl utilizing Au-coated SSWMs. These strategies extend the applicability of the platform that may help to develop valuable volatile sensing solutions.

Ionic Network Based on Dynamic Ionic Liquids for Electronic Tattoo Application

Electronic tattoos as an emerging epidermal electronic are alluring in the field of wearable electronics for their lightweight and noninvasive properties. However, the combination of flexibility, skin biocompatibility, adhesion, repairability, and erasability remains a challenge for fabricating electronic tattoos. Hence, a dynamic ionic liquid is prepared which is ideally suited for making an electronic tattoo with these challenging features at the same time. Such an intrinsically flexible electronic tattoo can be firmly attached to human skin with negligible irritation. More importantly, the existence of dynamic covalent chemistry provides the electronic tattoo with healing and erasable abilities under mild redox conditions. Owing to the high ionic conductivity of ionic liquids, the electronic tattoo exhibits excellent sensing performance in response to the temperature variation and tensile strain, which can intelligently monitor body temperature, pulse, and movement. As an extension of the application, a specially designed quadrilateral electronic tattoo can sense and distinguish multiple signals simultaneously. This concept of electronic tattoo based on the dynamic ionic liquid shows great potentials in the applications of intelligent wearable electronics.

Size Discrimination of Carbohydrates via Conductive Carbon Nanotube@Metal Organic Framework Composites

Traditional chemical sensing methodologies have typically relied on the specific chemistry of the analyte for detection. Modifications to the local environment surrounding the sensor represent an alternative pathway to impart selective differentiation. Here, we present the hybridization of a 2-D metal organic framework (Cu₃(HHTP)₂) with single-walled carbon nanotubes (SWCNTs) as a methodology for size discrimination of carbohydrates. Synthesis and the resulting conductive performance are modulated by both mass loading of SWCNTs and their relative oxidation. Liquid gated field-effect transistor (FET) devices demonstrate improved on/off characteristics and differentiation of carbohydrates based on molecular size. Glucose molecule detection is limited to the single micromolar concentration range. Molecular Dynamics (MD) calculations on model systems revealed decreases in ion diffusivity in the presence of different sugars as well as packing differences based on the size of a given carbohydrate molecule. The proposed sensing mechanism is a reduction in gate capacitance initiated by the filling of the pores with carbohydrate molecules. Restricting diffusion around a sensor in combination with FET measurements represents a new type of sensing mechanism for chemically similar analytes.

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning-A Review

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

Tuning of Ionic Liquid Crystal Properties by Combining Halogen Bonding and Fluorous Effect

We report halogen-bonded complexes between 1-polyfluoroalkyl-3-alkylimidazolium iodides and mono-iodoperfluoroalkanes of different chain lengths or di-iodoperfluorooctane. ¹⁹ F NMR analyses revealed that the preferred stoichiometry between the donors and acceptors is 1 : 1 in the cases of the mono-iodoperfluoroalkanes, and 2 : 1 with di-iodoperfluorooctane, as a result of the monodentate behavior of the iodide anion (halogen bond acceptor). Single crystal X-ray diffraction analyses showed the presence of a perfluorinated superanion, which interdigitates with the cation fluorinated chains, favoring the formation of lamellar structures. All of the obtained supramolecular complexes exhibit enantiotropic liquid crystalline phases over a broad range of temperatures. Most of the obtained complexes show melting points lower than 100 °C, two of them being liquid at room temperature, thus representing a new family of fluorinated ionic liquid crystals.

The Applications of Carbon Nanotubes in the Diagnosis and Treatment of Lung Cancer: A Critical Review

The importance of timely diagnosis and the complete treatment of lung cancer for many people with this deadly disease daily increases due to its high mortality. Diagnosis and treatment with helping the nanoparticles are useful, although they have reasonable harms. This article points out that the side effects of using carbon nanotube (CNT) in this disease treatment process such as inflammation, fibrosis, and carcinogenesis are very problematic. Toxicity can reduce to some extent using the techniques such as functionalizing to proper dimensions as a longer length, more width, and greater curvature. The targeted CNT sensors can be connected to various modified vapors. In this regard, with helping this method, screening makes non-invasive diagnosis possible. Researchers have also found that nanoparticles such as CNTs could be used as carriers to direct drug delivery, especially with chemotherapy drugs. Most of these carriers were multi-wall carbon nanotubes (MWCNT) used for cancerous cell targeting. The results of laboratory and animal researches in the field of diagnosis and treatment became very desirable and hopeful. The collection of researches summarized has highlighted the requirement for a detailed assessment which includes CNT dose, duration, method of induction, etc., to achieve the most controlled conditions for animal and human studies. In the discussion section, 4 contradictory issues are discussed which are invited researchers to do more research to get clearer results.

A Behavior-Learned Cross-Reactive Sensor Matrix for Intelligent Skin Perception

Mimicking human skin sensation such as spontaneous multimodal perception and identification/discrimination of intermixed stimuli is severely hindered by the difficulty of efficient integration of complex cutaneous receptor-emulating circuitry and the lack of an appropriate protocol to discern the intermixed signals. Here, a highly stretchable cross-reactive sensor matrix is demonstrated, which can detect, classify, and discriminate various intermixed tactile and thermal stimuli using a machine-learning approach. Particularly, the multimodal perception ability is achieved by utilizing a learning algorithm based on the bag-of-words (BoW) model, where, by learning and recognizing the stimulus-dependent 2D output image patterns, the discrimination of each stimulus in various multimodal stimuli environments is possible. In addition, the single sensor device integrated in the cross-reactive sensor matrix exhibits multimodal detection of strain, flexion, pressure, and temperature. It is hoped that his proof-of-concept device with machine-learning-based approach will provide a versatile route to simplify the electronic skin systems with reduced architecture complexity and adaptability to various environments beyond the limitation of conventional "lock and key" approaches.

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

A sensor array based on heterojunctions between semiconducting organic layers and single walled carbon nanotube (SWCNT) films is produced to explore applications in breathomics, the molecular analysis of exhaled breath. The array is exposed to gas/volatiles relevant to specific diseases (ammonia, ethanol, acetone, 2-propanol, sodium hypochlorite, benzene, hydrogen sulfide, and nitrogen dioxide). Then, to evaluate its capability to operate with real relevant biological samples the array is exposed to human breath exhaled from healthy subjects. Finally, to provide a proof of concept of its diagnostic potential, the array is exposed to exhaled breath samples collected from subjects with chronic obstructive pulmonary disease (COPD), an airway chronic inflammatory disease not yet investigated with CNT-based sensor arrays, and breathprints are compared with those obtained from of healthy subjects. Principal component analysis shows that the sensor array is able to detect various target gas/volatiles with a clear fingerprint on a 2D subspace, is suitable for breath profiling in exhaled human breath, and is able to distinguish subjects with COPD from healthy subjects based on their breathprints. This classification ability is further improved by selecting the most responsive sensors to nitrogen dioxide, a potential biomarker of COPD.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

Proton-Conductive Gas Sensor: a New Way to Realize Highly Selective Ammonia Detection for Analysis of Exhaled Human Breath

The analysis of exhaled human breath has great significance for early noninvasive diagnosis. Poor selectivity and strong humidity are two bottlenecks for the application of gas sensors to exhaled breath analysis. In this work, we utilized the adsorption, dissolution, ionization, and migration processes of ammonia in wet nonconjugated hydrophilic polymers to realize effective ammonia detection. The indispensable high-humidity atmosphere of exhaled breath was turned into a favorable condition for ammonia sensing. Nonconjugated polymer sensors can distinguish ammonia from most other gases because of its extremely high solubility and good ionization ability. A sensor based on poly(vinyl pyrrolidone) (PVP) could detect 0.5 ppm ammonia with an extremely high selectivity. The ammonia-sensing mechanism was thoroughly investigated by complex impedance plots (CIPs) and a quartz crystal microbalance (QCM) measurement. Finally, the potential of the PVP sensor for ammonia detection in exhaled breath was evaluated in simulated environments.

Humidity-Resistive Optical NO Gas Sensor Devices Based on Cobalt Tetraphenylporphyrin Dispersed in Hydrophobic Polymer Matrix

We report on an optical nitrogen oxide (NO) gas sensor device using cobalt tetraphenylporphyrin (CoTPP) dispersed in three kinds of hydrophobic polymer film matrix (polystyrene (PSt), ethylcellulose (EC), and polycyclohexyl methacrylate (PCHMA)) to improve humidity resistance. Our approach is very effective because it allows us to achieve not only high humidity resistance, but also a more than sixfold increase in sensitivity compared with CoTPP film due to the high dispersion of CoTPP in the polymer film. The limit of detection was calculated as 33 ppb for the CoTPP-dispersed EC film, which is lower than that of CoTPP film (92 ppb).

Synthesis of Nano-Praseodymium Oxide for Cataluminescence Sensing of Acetophenone in Exhaled Breath

In this work, we successfully developed a novel and sensitive gas sensor for the determination of trace acetophenone based on its cataluminescence (CTL) emission on the surface of nano-praseodymium oxide (nano-Pr₆O₁₁). The effects of working conditions such as temperature, flow rate, and detecting wavelength on the CTL sensing were investigated in detail. Under the optimized conditions, the sensor exhibited linear response to the acetophenone in the range of 15-280 mg/m³ (2.8-52 ppm), with a correlation coefficient (R²) of 0.9968 and a limit of detection (S/N = 3) of 4 mg/m³ (0.7 ppm). The selectivity of the sensor was also investigated, no or weak response to other compounds, such as alcohols (methanol, ethanol, n-propanol, iso-propanol, n-butanol), aldehyde (formaldehyde and acetaldehyde), benzenes (toluene, o-xylene, m-xylene, p-xylene), n-pentane, ethyl acetate, ammonia, carbon monoxide, carbon dioxide. Finally, the present sensor was applied to the determination of acetophenone in human exhaled breath samples. The results showed that the sensor has promising application in clinical breath analysis.

Chemiresistive Sensor Array and Machine Learning Classification of Food

Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models-a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.

Recent progress on carbon nanomaterials for the electrochemical detection and removal of environmental pollutants

Rapid global industrialization and explosive population growth have resulted in an increase in the discharge of harmful and toxic compounds. These toxic inorganic gases, volatile organic compounds, heavy metals, personal care products, endocrine-disrupting chemicals, dyes, and pharmaceuticals are destroying the balance in the Earth and increasing environmental toxicity at an alarming rate. Thus, their detection, adsorption and removal are of great significance. Various carbon nanomaterials including carbon nanotubes, graphene, mesoporous carbon, carbon dots, and boron-doped diamond have been extensively utilized and further proven to be ideal candidates for resolving environmental problems, emerging as adsorbents, electrochemical sensors and electrodes. Herein, we review the recent advances, progress and achievements in the design and properties of carbon nanomaterials and their applications for the electrochemical detection and removal of environmental pollutants.

A nanoneedle-based reactional wettability variation sensor array for on-site detection of metal ions with a smartphone

An enhancement of the reactional wettability variation (RWV) sensing strategy is achieved based on the wettability switch of a nanoneedle surface. The sensor unit is formed by coating hydrophobic azoimidazole compounds, as the responder compounds onto the originally hydrophilic surface of cobalt hydroxide nanoneedles. The complexation reaction between metal ions and azoimidazole ligands etches the hydrophobic coating and switches the surface wettability, making the surface hydrophilic again. This switch is revealed by a decrease in the static contact angle (CA) and an increase in the sliding angle of the surface. The reactivity is tuned by the derivatization and conformational manipulation of the azoimidazole compounds. A sensor array composed of six as-tuned sensor units is constructed to distinguish among the species and concentrations of Fe³⁺, Ni²⁺ and La³⁺ at a low limit of 10^-6 M using the chemometric method of principal component analysis (PCA). In addition, a new on-site detection strategy is developed based on PCA of the sliding angle, which can be measured conveniently and swiftly with a smartphone app and a commercially available setup. The application of the general RWV strategy is envisioned to open new possibilities for on-site detection.

Light-Regulated Electrochemical Reaction Assisted Core-Shell Heterostructure for Detecting Specific Volatile Markers with Controllable Sensitivity and Selectivity

Breath analysis has been considered a noninvasive, safe, and reliable way to diagnose cancer at very early stage. Rapid detection of cancer volatile markers in breath samples via a portable sensing device will lay the foundation of future early cancer diagnosis. Nevertheless, unsatisfactory sensitivity and specificity of these sensing devices restrain the clinical application of breath analysis. Herein, we proposed the strategy of designing the light-regulated electrochemical reaction assisted core-shell heterostructure to address the issue of concern; that is, the photoactive shell will be designed for trigging the light-regulated electrochemical reaction and enhancing the sensitivity while a catalytic active core will play the function of removing interference gases. After screening of various core candidates, Fe₂O₃ was found to exhibit relatively low conversion rate to 3-methylhexane, which is one of the representative volatile markers for breath analysis, suggesting that mutual interference would be eliminated by Fe₂O₃. Based on this assumption, an electrochemical sensor comprising core-shell Fe₂O₃@ZnO-SE (vs Mn-based RE) was fabricated and sensing properties to 6 kinds of volatile markers was evaluated. Interestingly, the thickness of ZnO shell significantly influenced the response behavior; typically, the Fe₂O₃@ZnO with shell thickness of 4.8 nm offers the sensor high selectivity to 3-methylhexane. In contrast, significantly mutual response interference is observed for the Fe₂O₃@ZnO with extremely thick/thin shell. Particularly, sensing properties are greatly enhanced upon illumination; a detection limit to 3-methylhexane can even be as low as 0.072 ppm which will be useful in clinic application. Besides, the high selectivity of the sensor to 3-methylhexane is further confirmed by the testing of simulated breath samples. In summary, we anticipate that the strategy proposed in this research will be a starting point for artificially tailoring the sensitivity and selectivity of future sensing devices.