Volatile Organic Compounds Sensor Research Articles

A Dual-Channel MoS2-Based Selective Gas Sensor for Volatile Organic Compounds.

Significant progress has been made in two-dimensional material-based sensing devices over the past decade. Organic vapor sensors, particularly those using graphene and transition metal dichalcogenides as key components, have demonstrated excellent sensitivity. These sensors are highly active because all the atoms in the ultra-thin layers are exposed to volatile compounds. However, their selectivity needs improvement. We propose a novel gas-sensing device that addresses this challenge. It consists of two side-by-side sensors fabricated from the same active material, few-layer molybdenum disulfide (MoS₂), for detecting volatile organic compounds like alcohol, acetone, and toluene. To create a dual-channel sensor, we introduce a simple step into the conventional 2D material sensor fabrication process. This step involves treating one-half of the few-layer MoS₂ using ultraviolet-ozone (UV-O3) treatment. The responses of pristine few-layer MoS₂ sensors to 3000 ppm of ethanol, acetone, and toluene gases are 18%, 3.5%, and 49%, respectively. The UV-O3-treated few-layer MoS₂-based sensors show responses of 13.4%, 3.1%, and 6.7%, respectively. This dual-channel sensing device demonstrates a 7-fold improvement in selectivity for toluene gas against ethanol and acetone. Our work sheds light on understanding surface processes and interaction mechanisms at the interface between transition metal dichalcogenides and volatile organic compounds, leading to enhanced sensitivity and selectivity.

An odorant-binding protein based electrical sensor to detect volatile organic compounds

Artificial olfaction systems, such as electronic nose devices (ENs), can be used in various fields, from healthcare to food industry and the environmental sector. ENs consist of an array of sensors composed of different sensing materials. Incorporating Odorant Binding Proteins (OBPs) into gas-sensing materials can increase volatile organic compounds (VOCs) recognition and selectivity. OBPs are small soluble proteins found in vertebrates and insects, responsible for VOCs solubilization and transportation. In this work, OBP3 from Rattus norvegicus was selected to develop an OBP-based electrical VOC sensor, by optimizing protein production and immobilization on sensors surface. OBP3 was successfully produced in E. coli host cells (94 mg/L) and purified with high purity (88% purification yield, 96% purity). The protein folding and thermal stability were assessed by circular dichroism (Tm=71±1ºC) whereas ligand binding activity was verified in solution by fluorescence displacement against diisopropylphenol (Kd=0.24 µM). For the immobilization of OBP3 on gold interdigitated electrodes modified with reduced graphene oxide, we explored two strategies (covalent and non-covalent), establishing a reproducible and cost-effective methodology to develop OBP3-based electrical sensors. The non-covalent immobilization of a linker to the graphene-modified surface showed improved outcomes compared to the carbodiimide crosslinking chemistry. OBP3-sensors presented selectivity towards distinct model compounds in the gas phase (diisopropylphenol, dimethylpyrazine, menthone and decanol), in correlation with the dissociation constants measured by fluorescence displacement assays in solution. As a result, this study expands the practical applications of OBPs for gas-phase sensors, showcasing their potential for enhancing VOC detection.

Sensing of volatile organic compounds using one-dimensional photonic crystal Bloch surface waves and internal optical modes

Bloch surface wave (BSW) and internal optical modes (IOMs) of one-dimensional photonic crystals (1DPhCs) have been explored for sensing purposes in the photonic crystal coupled reflectance (PCCR) geometry using the Kretschmann-Raether (KR) prism-coupling configuration. 1DPhCs coated with polyvinyl alcohol (PVA) thin films of two distinct thicknesses: 18 nm (referred to as PhC18) and 24 nm (referred to as PhC24) were fabricated. The response of the PCCR to the changes occurring in the PVA coated 1DPhC structures were studied by spraying volatile organic compounds, 1-propanol and 2-propanol onto the surface of the 1DPhCs. The BSWs excited under TE polarization are found to be very sensitive to perturbations at the 1DPhC interface. 1-propanol displays larger wavelength shifts and longer recovery times of the 1DPhC modes than 2-propanol because of its lower vapor pressure and higher reactivity to PVA.

TiO2-ZnO Composite thin films fabricated by Sol-Gel spin coating for room temperature impedometric acetone sensing

Metal oxide semiconductor based volatile organic compounds (VOCs) sensors lack room temperature operation and have a poor limit of detection. In this work, TiO2-ZnO composite thin film based acetone sensors with room temperature (29 ± 2 °C) operation and good sensor response have been fabricated by sol-gel based spin coating. The thin films based on the single-phase TiO2 and ZnO together with their composites have been evaluated for their structural and surface morphological properties through X-ray diffraction and field emission scanning electron microscopy respectively. The surface chemical state of the thin films surface has been analyzed through the X-ray photoelectron spectroscopy. The optical properties of the single-phase as well as the composite phase thin films were studied by optical absorption spectroscopy. The acetone sensing characteristics of these thin films based sensors were evaluated through impedance spectroscopy. The composite thin films were found to exhibit the maximum sensor response of 93.83% with a detection threshold of 3 ppm for acetone.

Urinary cancer detection by the target urine volatile organic compounds biosensor platform

Volatile organic compounds (VOCs) have grown due to their crucial role in transitioning from invasive to noninvasive cancer diagnostic methods. This study aimed to assess the feasibility of the metal oxide biosensor platform using urine VOCs for detecting genitourinary cancers. Five different commercially available semiconductor sensors were chosen to detect specific VOCs (methane, iso-butane, hydrogen, ethanol, hydrogen sulfide, ammonia, toluene, butane, propane, trimethylamine, and methyl-mercaptan). Changes in electrical resistance due to temperature variations from the voltage heater were examined to characterize VOC metabolism. Logistic regression and ROC analysis were employed to evaluate potential urine VOCs for genitourinary cancer determination. This study involved 64 participants which were categorized into a cancer and a non-cancer group. The genitourinary cancer (confirmed by tissue pathology) comprised 32 patients, including renal cell carcinoma (3.1%), transitional cell carcinoma (46.9%), and prostate cancer (50%). The non-cancer comprised 32 patients, with 9 healthy subjects and 23 individuals with other genitourinary diseases. Results indicated that VOC sensors for methane, iso-butane, hydrogen, and ethanol, at a voltage heater of 2000 mV, demonstrated a significant predictive capability for genitourinary cancer with P = 0.013. The ROC of these biomarkers also indicated statistical significance in predicting the occurrence of the disease (P < 0.05). This report suggested that methane, iso-butane, hydrogen, and ethanol VOCs exhibited potential for diagnosing genitourinary cancer. Developing gas metal oxide sensors tailored to these compounds, and monitoring changes in electrical resistance, could serve as an innovative tool for identifying this specific type of cancer.

Electrostatically formed nanowire (EFN) transistor—An ultrasensitive VOC and gas sensor

The perpetual need for high-performance volatile organic compound (VOC) sensors remains prevalent across diverse sectors including environmental health monitoring, industrial operations, and medical diagnostics. Within this context, the electrostatically formed nanowire (EFN) sensor, a silicon-on-insulator-based multiple-gate field-effect transistor, is an ultrasensitive and selective VOC and gas sensing platform. Unlike conventional silicon nanowires (also known for their superior sensitivity to chemical species), in EFN, the nanowire is defined electrostatically post-fabrication through appropriate biasing of the surrounding gates. The fabrication of the EFN leverages established CMOS compatible silicon processing technologies, facilitating the production of inexpensive, scalable, and robust sensors. By precisely controlling gate biases, a conductive channel with a tunable diameter is formed, allowing for the formation of nanowire with diameter below 20 nm. The adjustable size and shape of the nanowire offer tunable sensing parameters, including sensitivity, limit of detection, and dynamic range. The multiple parameters also yield a unique fingerprint for each VOC, thus enabling selective detection of VOCs. By simply altering the biasing configuration, a single EFN sensor can achieve high sensitivity and a broad dynamic range, which is limited in the case of physically defined silicon NW sensors. This review provides a comprehensive overview encompassing the EFN sensor's design, fabrication considerations, process flow, electrical characterization methods, sensing performances to VOCs, and gases at room temperature. Moreover, the scope of advanced sensor designs with array of EFN sensors and integrated heaters is also discussed. Finally, some future perspectives of this technology are presented.

Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Using wavelet transform and hybrid CNN – LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14–21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4–5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.

Using Machine Learning to Overcome Interfering Oxygen Effects in a Graphene Volatile Organic Compound Sensor.

Discriminating between volatile organic compounds (VOCs) for applications including disease diagnosis and environmental monitoring, is often complicated by the presence of interfering compounds such as oxygen. Graphene sensors are effective at detecting VOCs; however, they are also known to be highly sensitive to oxygen. Therefore, the combined effects of each of these gases on graphene sensors must be understood. In this work, we use graphene variable capacitor (varactor) sensors to examine the cross-selectivity of oxygen at 3 concentrations and 3 VOCs (ethanol, methanol, and methyl ethyl ketone) at 5 concentrations each. The sensor responses exhibit distinct shapes dependent on the relative concentrations in mixtures of oxygen and VOCs. Because the entire response shape is therefore informative for distinguishing between each gas mixture, a classification algorithm that utilizes entire sequences of data is needed. Accordingly, a long short-term memory (LSTM) network is used to classify the mixtures and VOC concentrations. The model achieves 100% accurate classification of the VOC type, even in the presence of varying levels of oxygen. When the VOC type and VOC concentration are classified, we show that the sensors can provide VOC concentration resolution within approximately 200 ppm. Throughout this work, we also demonstrate that an effective gas mixture classification can be achieved, even while the sensors exhibit varied drift patterns typical of graphene sensors. This is made possible due to the data analysis and machine learning methods employed.

Aluminium phosphide (Al12P12) nanocage as a potential sensor for volatile organic compounds: A DFT study.

The efficacy of aluminium phosphide (Al12P12) nanocage toward sensing methanol (MeOH) and ethanol (EtOH) volatile organic compounds (VOCs) was herein thoroughly elucidated utilizing various density functional theory (DFT) computations. In this perspective, MeOH⋯ and EtOH⋯Al12P12 complexes were investigated within all plausible configurations. According to the energetic features, the EtOH⋯Al12P12 complexes exhibited larger negative values of adsorption and interaction energies with values up to -27.23 and -32.84 kcal mol-1, respectively, in comparison to the MeOH⋯Al12P12 complexes. Based on the symmetry-adapted perturbation theory (SAPT) results, the electrostatic forces were pinpointed as the predominant component beyond the adsorption process within the preferable MeOH⋯ and EtOH⋯Al12P12 complexes. The findings of the noncovalent interaction (NCI) index and quantum theory of atoms in molecules (QTAIM) outlined the closed-shell nature of the interactions within the studied complexes. Substantial variations were found in the molecular orbitals distribution patterns of MeOH/EtOH molecules and Al12P12 nanocage, outlining the occurrence of the adsorption process within the complexes under investigation. Thermodynamic parameters were denoted with negative values, demonstrating the spontaneous exothermic nature of the most favorable complexes. New energy states were observed within the extracted density of states plots, confirming the impact of adsorbing MeOH and EtOH molecules on the electronic properties of the Al12P12 nanocage. The appearance of additional peaks in Infrared Radiation (IR) and Raman spectra revealed the apparent effect of the adsorption process on the features of the utilized sensor. The emerging results declared the potential uses of Al12P12 nanocage as a promising candidate for sensing VOCs, particularly MeOH and EtOH.

Comprehensive Qualitative and Quantitative Colorimetric Sensing of Volatile Organic Compounds Using Monolayered Metal-Organic Framework Films.

Cross-responsive chemical sensors are in high demand owing to their ability to distinguish a broad range of analytes. In this study, a vapochromic sensor array based on metal-organic frameworks (MOFs), which exhibits distinct patterns when exposed to volatile organic compounds (VOCs) and humidity, is developed. Conventional sensor arrays consist of various receptors that produce different responses. The vapochromic MOF-based sensor comprises dicopper paddlewheel clusters and dimethylamine azobenzene as binary colorimetric sensing moieties. Upon exposure to VOCs, the constructed sensor encompasses a broad spectrum of colors, ranging from green to red. Furthermore, the color of the MOF is influenced by the solvent used during the pretreatment. Consequently, monolayered MOF thin films can be adapted to multicomponent array systems by immersing the MOF in different solvents. This system provides both qualitative and quantitative sensing, generating unique color patterns corresponding to specific VOC types. Notably, the sensor successfully discriminates each of 14 common VOCs and water and accurately categorizes unknown samples. Moreover, the system undergoes reversible color changes in response to humidity, obviating the need for high-temperature regeneration steps. This novel approach offers insights into the versatile applications of MOFs by creating a colorimetric sensor array capable of detecting various analytes.

Fruit Freshness Monitoring Employing Chemiresistive Volatile Organic Compound Sensor and Machine Learning

Fruit Freshness Monitoring Employing Chemiresistive Volatile Organic Compound Sensor and Machine Learning

Impedimetric Early Sensing of Volatile Organic Compounds Released from Li-Ion Batteries at Elevated Temperatures

Lithium-ion batteries prove to be a promising technology for achieving present and future goals regarding energy resources. However, a few cases of lithium-ion battery fires and failures caused by thermal runaway have been reported in various news articles; therefore, it is important to enhance the safety of the batteries and their end users. The early detection of thermal runaway by detecting gases/volatile organic compounds (VOCs) released at the initial stages of thermal runaway can be used as a warning to end users. An interdigitated platinum electrode spin-coated with a sub-micron thick layer of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) showed sensitivity for two VOCs (ethyl-methyl carbonate and methyl formate) released from Li-ion batteries during thermal runaway, as well as their binary mixtures at elevated temperatures, which were measured using impedance spectroscopy over a frequency range of 1 MHz to 1 Hz. The sensor response was tested at three different high temperatures (40 °C, 55 °C, and 70 °C) for single analytes and binary mixtures of two VOCs at 5 ppm, 15 ppm, and 30 ppm concentrations. Equivalent electrical parameters were derived from impedance data. A machine learning approach was used to classify the sensor’s response. Classification algorithms classify the sensor’s response at elevated temperatures for different analytes with an accuracy greater than 70%. The success of the reported sensors will enhance battery safety via the early detection of thermal runaway.

Selective flexible sensor for monitoring volatile organic compounds in museum display cases

Aim of this work is the development of a new kind of corrosion sensors, based on low-cost flexible substrates, specifically designed to detect Volatile Organic Compounds (VOCs) producing significant degradation of artifacts in museum display cases. Sensors have been fabricated and tested in relevant environment with three of the most relevant VOCs (acetic acid, formic acid and formaldehyde) to the artifacts preservation. Sensitivity has been measured to be lower than 1 ppm, which is several times better than what reported in the literature on corrosion sensors for the same VOCs. In addition, different sensors, arranged with different active materials, have been implemented for the first time into a so-called multi-sensor architecture, aimed at demonstrating VOC specificity: in particular, we report here on a preliminary test in which the multi-sensor has been able to be selective between acetic acid and formaldehyde.

Disposable, strain-insensitive, and room-temperature-operated flexible humidity and VOC sensor with enhanced sensitivity and selectivity through interface control

As people are paying more attention to the health and comfort of the indoor environment, the sensors detecting the relative humidity (RH) and volatile organic compounds (VOCs) have attracted widely research interests. However, how to improve the sensitivity and selectivity remains a challenge, especially for flexible sensors working at room temperature (around 25 ℃). In this work, the controlling of solid-gas interface was found to be able to enhance the sensitivity and the manipulation of solid-solid interface increased the selectivity of the sensor for VOC detection. The flexible sensor based on electrospinning cellulose (EC) show high sensitivity for humidity detection due to its large solid-gas interface, which can be used for real-time monitoring of human breathing and speech recognition, simultaneously, it can also detect a variety of VOCs. By introducing silver nanowires (AgNWs) to establish solid-solid interface between silver and expanded graphite (EG), the selectivity of the sensor for VOCs detection was improved, which shown higher sensitivity for EtOH with faster response/recovery time, as well as long-term cycle stability and bending insensitivity. Furthermore, the sensor also can be decomposed by burning, offering a sustainable disposal method. This versatile, low-cost and environmentally friendly flexible electronic device may be used for wearable air monitoring and environmental controlling.

A turn-on type vapofluorochromic methoxybenzylidene methanone Schiff base applied to wearable VOC sensor and smartphone-based detection

This study presents the synthesis, crystallographic data and photophysical properties of a donor-acceptor (D-A) compound, methoxybenzylidene methanone Schiff base (HMPPM). It shows remarkable vapofluorochromism, embodied as turn-on red fluorescence in response to volatile organic compounds (VOCs), with fast response and high signal contrast. HMPPM is further developed into a cellulose sensor via dip-dying method for VOCs detection. We further use RGB and CIELAB color spaces to quantify the vapofluorochromic fluorescence. The limits of detection (LOD) of the sensor reach ppm level. Furthermore, it was fabricated into a wearable VOC sensor, with the introduction of an endogenous/exogenous reference, which conduces to high accuracy and visual identification. The cellulose sensor can be tailored into diversified patterns, with controllable shape and size, which facilitated the encapsulation into various wearables, including shoes, hat and smart band, etc., succeeding in scenario-based VOCs detection. In addition, we built a smartphone-based sensing platform based on HMPPM, which achieves a ppm-level LOD in VOCs 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.

Microwave-assisted solvothermal synthesis of In-MIL-68 derived hollow In2O3 microrods for enhanced 1-pentanol sensing performance

The development of microbial volatile organic compounds (MVOCs) sensors is helpful due to the detection of toxic contaminants in foods, non-invasive disease diagnosis, and monitoring of contamination in industrial processes. Materials that are typically applied in the sensors field are semiconductor metal oxides (SMOx) since they have shown efficiency in identifying gases. Using metal-organic frameworks (MOFs) as precursors is an exciting approach to producing well-structured SMOx. Therefore, we report the preparation and characterization of In-MIL-68 (a MOF composed of terephthalic acid) derived hollow In2O3 microrods. The sensor material was obtained by the microwave-assisted solvothermal (MAS) method and subsequent calcination. We also show the application of the hollow In2O3 microrods as a 1-pentanol sensor for the first time. The sensor demonstrated exceptional sensitivity to this MVOC compared to other sensors reported in the literature, achieving a great response of 390 for 100 ppm of this compound at 350 °C.

Wearable Device for Cumulative Chlorobenzene Detection and Accessible Mitigation Strategies.

Chronic exposure to low concentrations of volatile organic compounds (VOCs), such as chlorobenzene, is not being monitored in industrializing countries, although VOC exposure is associated with carcinogenic, organ-toxic, and endocrine-disrupting effects. Current VOC-sensing technologies are inaccessible due to high cost, size, and maintenance or are ineffective due to poor sensitivity or reliability. In particular, marginalized individuals face barriers to traditional prescription VOC treatments due to cost, lack of transportation, and limited access to physicians; thus, alternative treatments are needed. Here, we created a novel cumulative wearable color-changing VOC sensor with a paper-based polydiacetylene sensor array for chlorobenzene. With a single smartphone picture, the sensor displays 14 days of logged chlorobenzene exposure data, interpreted by machine-learning (ML) techniques, including principal component analysis. Further, we explored the efficacy of affordable and accessible treatment options to mitigate a VOC's toxic effects. Vitamin D and sulforaphane are naturally found in cruciferous vegetables, like broccoli, and can be used to treat chlorobenzene-mediated bone degradation. Our platform combines these components into a smartphone app that photographs the sensor's colorimetric data, analyzes the data via ML techniques, and offers accessible treatments based on exposure data.

Block Copolymer-Templated, Single-Step Synthesis of Transition Metal Oxide Nanostructures for Sensing Applications.

The synthesis of transition metal oxide nanostructures, thanks to their high surface-to-volume ratio and the resulting large fraction of surface atoms with high catalytic activity, is of prime importance for the development of new sensors and catalytic materials. Here, we report an economical, time-efficient, and easily scalable method of fabricating nanowires composed of vanadium, chromium, manganese, iron, and cobalt oxides by employing simultaneous block copolymer (BCP) self-assembly and selective sequestration of metal-organic acetylacetonate complexes within one of the BCP blocks. We discuss the mechanism and the primary factors that are responsible for the sequestration and conformal replication of the BCP template by the inorganic material that is obtained after the polymer template is removed. X-ray photoelectron spectroscopy (XPS) and powder X-ray diffraction (PXRD) studies indicate that the metal oxidation state in the nanowires produced by plasma ashing the BCP template closely matches that of the precursor complex and that their structure is amorphous, thus requiring high-temperature annealing in order to sinter them into a crystalline form. Finally, we demonstrate how the developed nanowire array fabrication scheme can be used to rapidly pattern a multilayered iron oxide nanomesh, which we then used to construct a prototype volatile organic compound sensor.