Review of Gravimetric Sensing of Volatile Organic Compounds

Tracking Molecular Signatures at ppb Sensitivity Using Fluctuational Kinetics in Metal-Organic Frameworks

Biological systems achieve parts-per-billion (ppb) sensitivity in gas detection by tracking molecular fluctuations over time─a level of precision that remains difficult to replicate in engineered sensors. Conventional sensing relies on adsorption processes that require activation energies (Ea) ∼10 kBT, resulting in exponentially long equilibration times and limited selectivity due to small differences in Ea among analytes. Here, we show that volatile organics interacting with a ∼200 nm-thick nanoporous metal-organic framework (MOF), when subjected to shear-induced strain via a quartz crystal microbalance (QCM), exhibit a secondary fluctuational adsorption time scale distinct from the steady-state response. This emergent kinetic signature allows for reliable molecular discrimination at sensitivities down to ∼100 ppb. Our approach introduces a new selectivity metric based on dynamic adsorption kinetics, opening avenues for real-time molecular identification in environmental monitoring, portable diagnostics, and selective detection in chemically complex settings.

Integrated Optics Waveguides and Mesoporous Oxides for the Monitoring of Volatile Organic Compound Traces in the Mid-Infrared

Volatile organic compounds (VOCs) are an ever-growing hazard for health and environment due to their increased emissions and accumulation in the air. Quantum cascade laser-based infrared (QCL-IR) sensors hold significant promise for gas monitoring, thanks to their compact, rugged design, high laser intensity, and high molecule-specific detection capabilities within the mid-infrared spectrum's fingerprint region. In this work, tunable external cavity QCLs were complemented by an innovative germanium-on-silicon integrated optics waveguide sensing platform with integrated microlenses for efficient backside optical interfacing for the tunable laser spectrometer. The waveguide chip was coated with a mesoporous silica coating, thereby increasing the signal by adsorptive enhancement of VOCs while at the same time limiting water vapor interferences. Different least square fitting methods were explored to deconvolute the resulting spectra, showing subparts-per-million by volume (sub-ppmv) limits of detection and enrichment factors of up to 22 000 while keeping the footprint of the setup small (29 × 23 × 11 cm³). Finally, a use-case simulation for the continuous detection of VOCs in a process analytical technology environment confirmed the high potential of the technique for the monitoring of contaminants. By successfully demonstrating the use of photonic waveguides for the monitoring of VOCs, this work offers a promising avenue for the further development of fully integrated sensors on a chip.

Artificial Intelligence in Gas Sensing: A Review

The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based intelligent gas sensors include environmental monitoring, industrial safety, remote sensing, and medical diagnostics. AI, ML, and DL methods can process and interpret complex sensor data, allowing for improved accuracy, sensitivity, and selectivity, enabling rapid gas detection and quantitative concentration measurements based on sophisticated multiband, multispecies sensor systems. These methods can discern subtle patterns in sensor signals, allowing sensors to readily distinguish between gases with similar sensor signatures, enabling adaptable, cross-sensitive sensor systems for multigas detection under various environmental conditions. Integrating AI in gas sensor technology represents a paradigm shift, enabling sensors to achieve unprecedented performance, selectivity, and adaptability. This review describes gas sensor technologies and AI while highlighting approaches to AI-sensor integration.

Covalent Organic Framework as Selective Fluorescence Sensors for Cancer Inducing Volatile Organic Compounds

Certain volatile organic compounds (VOCs), such as formaldehyde, acetone, and ethanol, are overexpressed in some terminal diseases like cancer, diabetes, Alzheimer's, etc. Therefore, high-precision detection and quantification of VOCs is imperative for early diagnosis of such detrimental diseases. Non-invasive and accurate fluorescence-based detection of such analytes has garnered widespread attention. The inherent luminescent properties of covalent organic frameworks (COFs), resulting from their extensive π-conjugation, have made them suitable for sensing applications. Structural tunability and strong covalent linkers facilitate sensing by COFs. Appropriate choices of linker and skeletal units of the COF can help detect various biologically important analytes selectively. The most common linkers used in this regard is the imine linker, which can undergo excellent hydrogen bonding with different protic VOCs e. g., ethanol, methanol, etc. Besides imine detection, hydrogen bonding also proved useful for detection of aldehydes. Suitable combinations of donors and acceptors enable the COFs to have specific charge transfer interactions with many electron-rich and electron-poor VOCs. In this review, we have highlighted the syntheses of selective COFs incorporating linkers designed for sensing cancer-inducing VOCs. A detailed discussion of the interaction mechanisms between COFs and these VOCs is provided, along with examples from recent literature in this field.

A comprehensive review on advancements in sensors for air pollution applications

Air pollution, originating from both natural and human-made sources, presents significant threats to human health and the environment. This review explores the latest technological advancements in air quality sensors focusing on their applications in monitoring a wide range of pollution sources from volcanic eruptions and wildfires to industrial emissions, transportation, agricultural activities and indoor air quality. The review categorizes these sources and examines the operational principles, system architectures, and effectiveness of various air quality monitoring instruments including low-cost sensors, gas analyzers, weather stations, passive sampling devices and remote sensing technologies such as satellite and LiDAR. Key insights include the rapid evolution of sensor technology driven by the need for more accurate, real-time monitoring solutions that are both cost-effective and widely accessible. Despite significant advancements, challenges such as sensor calibration, standardization, and data integration remain critical for ensuring reliable air quality assessments. The manuscript concludes by emphasizing the need for continued innovation and the integration of advanced sensor technologies with regulatory frameworks to enhance environmental management and public health protection.

A Low-Cost Method for Producing User-Specified Concentrations of VOCs

Monitoring volatile organic compounds is critical to mitigate the risks they pose to human health and the environment. Developing technologies for detection of VOCs requires methods to produce the desired concentrations for benchmarking. Herein, we present a simple, inexpensive, and flexible platform capable of producing user-specified concentrations of a gas-phase analyte for the purpose of fine-tuning and benchmarking VOC detection technologies. This technology, the gas-phase dilution apparatus (GPDA), is built around two mass flow controllers that mix precise flows of the analyte and dilution gas. We used a custom APPI-MS configuration as well as a commercial photoionization detector to detect benzene and toluene. These two detection methods were employed to assess the linear output of concentrations over a combined range of 1-20 000 ppbV which yielded average R2 values of 0.9980 and 0.9988 for benzene and toluene, respectively. Additionally, output stability was assessed at 10 ppbV, 1 ppmV, and 5 ppmV of benzene and toluene. Six measurements were averaged over the course of 30 min, and RSDs were below 2% for all three concentrations of both compounds. These results suggest that GPDA is capable of producing precise and repeatable concentrations of gas-phase analytes.

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 μA/μM/cm2), a broad linear range (0.002-8 μM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

Doping Strategy of Monolayer MoS2 to Realize the Monitoring of Environmental Concentration of Desflurane: A First-Principles Study

Desflurane is a new volatile inhalation anesthetic that is widely used in medical operation. However, various diseases can be caused by chronic exposure to desflurane, which is also a greenhouse gas. Therefore, it is urgent to find a suitable method for monitoring desflurane. In this paper, the process of doping of Pd, Pt, and Ni on the MoS2 surface is simulated to determine the stability of the doping structure based on first-principles. The adsorption properties and sensing properties of Pd-MoS2, Pt-MoS2, and Ni-MoS2 on desflurane are explored by parameters including independent gradient model based on Hirshfeld partition (IGMH), electron localization function (ELF), and density of states (DOS), sensibility, and recovery time, subsequently. The doping results show that the three doping systems (Pd-MoS2, Pt-MoS2, and Ni-MoS2) are structurally stable, and the chemical bonds are formed with MoS2. The adsorption results show the best chemisorption between Pt-MoS2 and desflurane with the chemical bonds between them. The results of IGMH, ELF, and DOS also confirm it. The sensing characterization results show that the recovery time of Pt-MoS2 ranges between 85.27 and 0.027 s, and the sensitivity ranges from 99.26 to 25.69%, all of which can meet the requirements of the sensor. Considering the adsorption effect and sensing characteristics, Pt-MoS2 can be used as a gas-sensitive material for detecting the concentration of desflurane.

Hybrid plasmonic aerogel with tunable hierarchical pores for size-selective multiplexed detection of VOCs with ultrahigh sensitivity

Sensitive and rapid identification of volatile organic compounds (VOCs) at ppm level with complex composition is vital in various fields ranging from respiratory diagnosis to environmental safety. Herein, we demonstrate a SERS gas sensor with size-selective and multiplexed identification capabilities for VOCs by executing the pre-enrichment strategy. In particular, the macro-mesoporous structure of graphene aerogel and micropores of metal-organic frameworks (MOFs) significantly improved the enrichment capacity (1.68 mmol/g for toluene) of various VOCs near the plasmonic hotspots. On the other hand, molecular MOFs-based filters with different pore sizes could be realized by adjusting the ligands to exclude undesired interfering molecules in various detection environments. Combining these merits, graphene/AuNPs@ZIF-8 aerogel gas sensor exhibited outstanding label-free sensitivity (up to 0.1 ppm toluene) and high stability (RSD=14.8%, after 45 days storage at room temperature for 10 cycles) and allowed simultaneous identification of multiple VOCs in a single SERS measurement with high accuracy (error < 7.2%). We visualize that this work will tackle the dilemma between sensitivity and detection efficiency of gas sensors and will inspire the design of next-generation SERS technology for selective and multiplexed detection of VOCs.

N, S-Codoped Carbon Dots-Based Reusable Solvatochromic Organogel Sensors for Detecting Organic Solvents

The visualization and analysis of organic solvents using fluorescent sensors are crucial, given their association with environmental safety and human health. Conventional fluorescent sensors are typically single-use sensors and they often require sophisticated measurement instruments, which limits their practical and diverse applications. Herein, we develop solvatochromic nitrogen and sulfur codoped carbon dots (NS-CDs)-based organogel sensors that display color changes in response to different solvents. NS-CDs are synthesized using a solvothermal method to produce monodispersed particles with exceptional solubility in various organic solvents. NS-CDs exhibit distinct photoluminescent emission spectra that correlate with the solvent polarity, and the solvent-dependent photoluminescent mechanism is investigated in detail. To highlight the potential application of solvatochromic NS-CDs, portable and low-cost NS-CDs-embedded organogel sensors are fabricated. These sensors exhibit highly robust solvatochromic performance despite repeated solvent switches, thus ensuring consistent and reliable measurements in practical applications. This study provides valuable insights into the solvatochromism of carbon dots and opens up new avenues for designing real-time organic solvent sensing platforms.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

Demonstration of cross reaction in hybrid graphene oxide/tantalum dioxide guided mode resonance sensor for selective volatile organic compound

This paper experimentally demonstrates a crossed reaction of pure and hybrid graphene oxide (GO)/tantalum dioxide (TaO2) as a volatile organic compound (VOC) absorber in a guided mode resonance (GMR) sensing platform. The proposed GMR platform has a porous TaO2 film as the main guiding layer, allowing for more molecular adsorption and enhanced sensitivity. GO is applied on top as an additional VOC absorber to increase the selectivity. The hybrid sensing mechanism is introduced by varying the concentration of the GO aqueous solution. The experimental results show that the pure TaO2-GMR has a high tendency to adsorb most of the tested VOC molecules, with the resonance wavelength shifting accordingly to the physical properties of the VOCs (molecular weight, vapor pressure, etc). The largest signal appears in the large molecule such as toluene, and its sensitivity is gradually reduced in the hybrid sensors. At the optimum GO concentration of 3 mg/mL, the hybrid GO/TaO2 -GMR is more sensitive to methanol, while the pure GO sensor coated with GO at 5 mg/mL is highly selective to ammonia. The sensing mechanisms are verified using the distribution function theory (DFT) to simulate the molecular absorption, along with the measured functional groups measured on the sensor surface by the Fourier transform infrared spectroscopy (FTIR). The crossed reaction of these sensors is further analyzed by means of machine learning, specifically the principal component analysis (PCA) method and decision tree algorithm. The results show that this sensor is a promising candidate for quantitative and qualitative VOCs detection in sensor array platform.

High-performance fixed-bed in situ mass analyzer-ISMA

We demonstrate a newly developed high-performance fixed-bed reactor combined with an in situ mass analyzer (ISMA). The ISMA is particularly relevant to sub-second time-resolved studies where mass changes occur due to, e.g., chemical reactions and process conditions such as choice of solid, temperature, gas atmosphere, and pressure. The mass is determined from the optically measured oscillation frequency of a quartz element, yielding a mass resolution below 10 μg-typically 2-3 μg-for samples up to ∼500 mg. By placing the quartz element and optical sensor inside stainless steel pipes and providing heat from the outside, the instrument is applicable up to ∼62 bars and 700 °C. By surrounding this core part of the instrument with a suitable feed system and product analysis instruments, in combination with computer control and logging, time-resolved studies are enabled. The instrument with surrounding feed and product analysis infrastructure is fully automated. Emphasis has been put on making the instrument robust, safe, operationally simple, and user-friendly. We demonstrate the ISMA instrument on selected samples.

Highly Efficient, Dual-Functional Self-Assembled Electrospun Nanofiber Filters for Simultaneous PM Removal and On-Site Eye-Readable Formaldehyde Sensing

Air pollution containing particulate matter (PM) and volatile organic compounds has caused magnificent burdens on individual health and global economy. Although advances in highly efficient or multifunctional nanofiber filters have been achieved, many existing filters can only deal with one type of air pollutant, such as capturing PM or absorbing and detecting toxic gas. Here, highly efficient, dual-functional, self-assembled electrospun nanofiber (SAEN) filters were developed for simultaneous PM removal and onsite eye-readable formaldehyde sensing fabricated on a commercial fabric mask. With the use of an electrolyte solution containing a formaldehyde-sensitive colorimetric agent as a collector during electrospinning, the one-step fabrication of the dual-functional SAEN filter on commercial masks, such as a fabric mask and a daily disposable mask, was achieved. The electrolyte solution also allowed the uniform deposition of electrospun nanofibers, thereby achieving the high efficiency of PM filtration with an increased quality factor up to twice that of commercial masks. The SAEN filter enabled onsite and eye-readable formaldehyde gas detection by changing its color from yellow to red under a 5 ppm concentrated formaldehyde gas atmosphere. The repetitive fabrication and detachment of the SAEN filter on a fabric mask minimized the waste of the mask while maintaining high filtration efficiency by replenishing the SAEN filters and reusing the fabric mask. Given the dual functionality of SAEN filters, this process could provide new insights into designing and developing high performance and dual-functional electrospun nanofiber filters for various applications, including individual protection and indoor purification applications.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42765-023-00279-3.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.

A review of piezoelectric MEMS sensors and actuators for gas detection application

Piezoelectric microelectromechanical system (piezo-MEMS)-based mass sensors including the piezoelectric microcantilevers, surface acoustic waves (SAW), quartz crystal microbalance (QCM), piezoelectric micromachined ultrasonic transducer (PMUT), and film bulk acoustic wave resonators (FBAR) are highlighted as suitable candidates for highly sensitive gas detection application. This paper presents the piezo-MEMS gas sensors' characteristics such as their miniaturized structure, the capability of integration with readout circuit, and fabrication feasibility using multiuser technologies. The development of the piezoelectric MEMS gas sensors is investigated for the application of low-level concentration gas molecules detection. In this work, the various types of gas sensors based on piezoelectricity are investigated extensively including their operating principle, besides their material parameters as well as the critical design parameters, the device structures, and their sensing materials including the polymers, carbon, metal-organic framework, and graphene.

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

The chemical modification of polymer nanofiber-based ammonia sensors by introducing dopants into the active layers has been proven as one of the low-cost routes to enhance their sensing performance. Herein, we investigate the influence of different citric acid (CA) concentrations on electrospun polyvinyl acetate (PVAc) nanofibers coated on quartz crystal microbalance (QCM) transducers as gravimetric ammonia sensors. The developed CA-doped PVAc nanofiber sensors are tested against various concentrations of ammonia vapors, in which their key sensing performance parameters (i.e., sensitivity, limit of detection (LOD), limit of quantification (LOQ), and repeatability) are studied in detail. The sensitivity and LOD values of 1.34 Hz ppm^-1 and 1 ppm, respectively, can be obtained during ammonia exposure assessment. Adding CA dopants with a higher concentration not only increases the sensor sensitivity linearly, but also prolongs both response and recovery times. This finding allows us to better understand the dopant concentration effect, which subsequently can result in an appropriate strategy for manufacturing high-performance portable nanofiber-based sensing devices.

Molecularly imprinted polymers for sensing gaseous volatile organic compounds: opportunities and challenges

Chemical sensors that can detect volatile organic compounds (VOCs) are the subject of extensive research efforts. Among various sensing technologies, molecularly imprinted polymers (MIPs) are regarded as a highly promising option for their detection with many advantageous properties, e.g., specific binding-site for template molecules, high recognition specificity, ease of preparation, and chemical stability. This review covers recent advances in the sensing application of MIPs toward various types of VOCs (e.g., aliphatic and aromatic compounds). Particular emphasis has been placed on multiple approaches to the synthesis of MIP-based VOC sensors in association with their performance and sensing mechanisms. Current challenges and opportunities for new VOC-sensing applications are also discussed based on MIP technology.

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.

Template Imprinting Versus Porogen Imprinting of Small Molecules: A Review of Molecularly Imprinted Polymers in Gas Sensing

The selective sensing of gaseous target molecules is a challenge to analytical chemistry. Selectivity may be achieved in liquids by several different methods, but many of these are not suitable for gas-phase analysis. In this review, we will focus on molecular imprinting and its application in selective binding of volatile organic compounds and atmospheric pollutants in the gas phase. The vast majority of indexed publications describing molecularly imprinted polymers for gas sensors and vapour monitors have been analysed and categorised. Specific attention was then given to sensitivity, selectivity, and the challenges of imprinting these small volatile compounds. A distinction was made between porogen (solvent) imprinting and template imprinting for the discussion of different synthetic techniques, and the suitability of each to different applications. We conclude that porogen imprinting, synthesis in an excess of template, has great potential in gas capture technology and possibly in tandem with more typical template imprinting, but that the latter generally remains preferable for selective and sensitive detection of gaseous molecules. More generally, it is concluded that gas-phase applications of MIPs are an established science, capable of great selectivity and parts-per-trillion sensitivity. Improvements in the fields are likely to emerge by deviating from standards developed for MIP in liquids, but original methodologies generating exceptional results are already present in the literature.

Gas Identification by Simultaneous Permeation through Parallel Membranes: Proof of Concept

This paper describes an experimental system for simultaneous permeation of a pressurized test gas through different gas permeable membranes and provides a proof of concept for a novel approach for gas identification/fingerprinting for potential construction of electronic noses. The design, construction, and use of a six-channel system which allows simultaneous gas permeation from a single pressurized gas compartment through six different parallel membranes are presented. The permeated gas is accumulated in confined spaces behind the respective membranes. The rate of gas pressure accumulation behind each membrane is recorded and used as a measure of the gas permeation rate through the membrane. The utilized gas permeable membranes include Teflon AF, silicone rubber, track-etch hydrophilic polycarbonate, track-etch hydrophobic polycarbonate, track-etch polyimide, nanoporous anodic aluminum oxide, zeolite ZSM-5, and zeolite NaY. An analogy between the rate of pressure accumulation of the permeating gas behind the membrane and the charging of an electric capacitor in a single series RC circuit is proposed and thoroughly validated. The simultaneous permeation rates through different membranes demonstrated a very promising potential as characteristic fingerprints for 10 test gases, that is, helium, neon, argon, hydrogen, nitrogen, carbon dioxide, methane, ethane, propane, and ethylene, which are selected as representative examples of mono-, di-, tri-, and polyatomic gases and to include some homologous series as well as to allow testing the potential of the proposed system to discriminate between closely related gases such as ethane and ethylene or carbon dioxide and propane which have almost identical molecular masses. Finally, a preliminary investigation of the possibility of applying the developed gas permeation system for semiquantitative analysis of the CO₂-N₂ binary mixture is also presented.

From 0D to 2D: N-doped carbon nanosheets for detection of alcohol-based chemical vapours

The application of N-doped carbon nanosheets, with and without embedded carbon dots, as active materials for the room temperature chemoresistive detection of methanol and/or ethanol is presented. The new carbons were made by converting 0D N-doped carbon dots (NCDs) to 2D nitrogen-doped carbon nanosheets by heat treatment (200–700 °C). The nanosheets exhibited a lateral size of ∼3 μm and a thickness of ∼12 nm at the highest annealing temperature. Both Raman and TEM analyses showed morphological transitions of the dots to the sheets, whilst XPS analysis revealed transformation of the N-bonding states with increasing temperature. PDF analysis confirmed the presence of defective carbon sheets. Room temperature screening of the chemical vapours of two alcohols (methanol and ethanol), revealed that the structure and the type of N-configuration influenced the detection of the chemical vapours. For instance, the lateral size of the nanosheets and the high charge density N-configurations promoted detection of both methanol and ethanol vapours at good sensitivity (−16.8 × 10⁻⁵ ppm⁻¹_EtOH and 1.2 × 10⁻⁵ ppm⁻¹_MeOH) and low LoD (∼44 ppm_EtOH and ∼30.3 ppm_MeOH) values. The study showed that the composite nature as well as the large basal area of the carbon nanosheets enabled generation of adequate defective sites that facilitated easy adsorption of the VOC analyte molecules, thereby eliminating the need to use conducting polymers or the formation of porous molecular frameworks for the alcohol detection.

2D layered carbon nanostructures made by annealing 0D carbon dots, have been used as ethanol/methanol sensors.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Layer by Layer Optimization of Langmuir–Blodgett Films for Surface Acoustic Wave (SAW) Based Sensors for Volatile Organic Compounds (VOC) Detection

Rayleigh surface acoustic wave (RSAW)-based resonant sensors, functionalized with single and multiple monomolecular layers of Langmuir–Blodgett (LB) films, were thickness and density optimized for the detection of volatile organic compounds (VOC), which could impose a serious threat on the environment and human health. Single layers of a phospholipid (SLP), hexane dissolved arachidic acid (HDAA), and chloroform dissolved arachidic acid (CDAA) were used for the LB film preparation. Several layers of these compounds were deposited on top of each other onto the active surface of high-Q 434 MHz two-port RSAW resonators in a LB trough to prepare a highly sensitive vapor detection quartz surface microbalance (QSM). Frequency shift was measured with a vector network analyzer (VNA). These devices were probed with saturated vapors of hexane, chloroform, methanol, acetone, ethanol, and water after each deposited layer to test the behavior of the QSM’s insertion loss, loaded Q, vapor sensitivity, and to find the optimum trade-off between these parameters for the best real-life sensor performance. With 2200 ppm and 3700 ppm sensitivity to chloroform, HDAA and CDAA coated QSM devices reached the optimum sensor performance at 15 and 11–15 monolayers, respectively. Surface pressure optimized single monolayers of phospholipid LB films were found to provide up to 530 ppm sensitivity to chloroform vapors with a negligible reduction in loss and loaded Q. This vapor sensitivity is higher than the mass of the sensing layer itself, making SLP films an excellent choice for QSM functionalization.

Volatile Organic Compounds as Potential Biomarkers for Noninvasive Disease Detection by Nanosensors: A Comprehensive Review

Biomarkers are biological molecules associated with physiological changes of the body and aids in the detecting the onset of disease in patients. There is an urgent need for self-monitoring and early detection of cardiovascular and other health complications. Several blood-based biomarkers have been well established in diagnosis and monitoring the onset of diseases. However, the detection level of biomarkers in bed-side analysis is difficult and complications arise due to the endothelial dysfunction. Currently single volatile organic compounds (VOCs) based sensors are available for the detection of human diseases and no dedicated nanosensor is available for the elderly. Moreover, accuracy of the sensors based on a single analyte is limited. Hence, breath analysis has received enormous attention in healthcare due to its relatively inexpensive, rapid, and noninvasive methods for detecting diseases. This review gives a detailed analysis of how biomarker imprinted nanosensor can be used as a noninvasive method for detecting VOC to health issues early using exhaled breath analysis.

Porous carbon nanoarchitectonics for the environment: detection and adsorption

As a post-nanotechnology concept, nanoarchitectonics has emerged from the 20th century to the 21st century. This review summarizes the recent progress in the field of metal-free porous carbon nanoarchitectonics.

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

Construction of a Self-Assembled Polyelectrolyte/Graphene Oxide Multilayer Film and Its Interaction with Metal Ions

In this study, a composite multilayer film onto gold was constructed from two charged building blocks, i.e., negatively charged graphene oxide (GO) and a branched polycation (polyethylenimine, PEI) via layer-by-layer (LbL) self-assembly technology, and this process was monitored in situ with quartz crystal microbalance (QCM) under different experimental conditions. This included the differences in frequency (Δf) as well as the changes in dissipation to yield information on the absorbed mass and viscoelastic properties of the formed PEI/GO multilayer films. The experimental conditions were optimized to obtain a high amount of the adsorbed mass of the self-assembled multilayer film. The surface morphology of the PEI/GO multilayer film onto gold was studied with atomic force microscopy (AFM). It was found that the positively charged PEI chains were combined with the oppositely charged GO to form an assembled film on the QCM sensor surface, in a wrapped and curled fashion. Raman and UV-vis spectra also showed that the intensities of the GO-characteristic signals are almost linearly related to the layer number. To explore the films for their use in divalent ion detection, the frequency response of the PEI/GO multilayer-modified QCM sensor to the exposure of aqueous solutions solution of Cu²⁺, Ca²⁺, Zn²⁺, and Sn²⁺ was further studied using QCM. Based on the Sauerbrey equation and the weight of different ions, the number of metal ions adsorbed per unit area on the surface of QCM sensors was calculated. For metal ion concentrations of 40 ppm, the adsorption capacities per unit area of Cu²⁺, Zn²⁺, Sn²⁺, and Ca²⁺ were found to be 1.7, 3.2, 0.7, and 4.9 nmol/cm², respectively. Thus, in terms of the number of adsorbed ions per unit area, the QCM sensor modified by PEI/GO multilayer film shows the largest adsorption capacity of Ca²⁺. This can be rationalized by the relative hydration energies.

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.

An Overview of Artificial Olfaction Systems with a Focus on Surface Plasmon Resonance for the Analysis of Volatile Organic Compounds

The last three decades have witnessed an increasing demand for novel analytical tools for the analysis of gases including odorants and volatile organic compounds (VOCs) in various domains. Traditional techniques such as gas chromatography coupled with mass spectrometry, although very efficient, present several drawbacks. Such a context has incited the research and industrial communities to work on the development of alternative technologies such as artificial olfaction systems, including gas sensors, olfactory biosensors and electronic noses (eNs). A wide variety of these systems have been designed using chemiresistive, electrochemical, acoustic or optical transducers. Among optical transduction systems, surface plasmon resonance (SPR) has been extensively studied thanks to its attractive features (high sensitivity, label free, real-time measurements). In this paper, we present an overview of the advances in the development of artificial olfaction systems with a focus on their development based on propagating SPR with different coupling configurations, including prism coupler, wave guide, and grating.

Coating-Based Quartz Crystal Microbalance Detection Methods of Environmentally Relevant Volatile Organic Compounds

Volatile organic compounds (VOCs) that evaporate under standard atmospheric conditions are of growing concern. This is because it is well established that VOCs represent major contamination risks since release of these compounds into the atmosphere can contribute to global warming, and thus, can also be detrimental to the overall health of worldwide populations including plants, animals, and humans. Consequently, the detection, discrimination, and quantification of VOCs have become highly relevant areas of research over the past few decades. One method that has been and continues to be creatively developed for analyses of VOCs is the Quartz Crystal Microbalance (QCM). In this review, we summarize and analyze applications of QCM devices for the development of sensor arrays aimed at the detection of environmentally relevant VOCs. Herein, we also summarize applications of a variety of coatings, e.g., polymers, macrocycles, and ionic liquids that have been used and reported in the literature for surface modification in order to enhance sensing and selective detection of VOCs using quartz crystal resonators (QCRs) and thus QCM. In this review, we also summarize novel electronic systems that have been developed for improved QCM measurements.

Enabling the Selective Detection of Endocrine-Disrupting Chemicals via Molecularly Surface-Imprinted "Coffee Rings"

Molecularly imprinted polymers (MIPs) represent an intriguing class of synthetic materials that can selectively recognize and bind chemical or biological molecules in a variety of value-added applications in sensors, catalysis, drug delivery, antibodies, and receptors. In this context, many advanced methods of implementing functional MIP materials have been actively studied. Herein, we report a robust strategy to produce highly ordered arrays of surface-imprinted polymer patterns with unprecedented regularity for MIP-based sensor platform, which involves the controlled evaporative self-assembly process of MIP precursor solution in a confined geometry consisting of a spherical lens on a flat Si substrate (i.e., sphere-on-flat geometry) to synergistically utilize the "coffee-ring" effect and repetitive stick-slip motions of the three-phase contact line simply by solvent evaporation. Highly ordered arrays of the ring-patterned MIP films are then polymerized under UV irradiation to achieve semi-interpenetrating polymer networks. The extraction of templated target molecules from the surface-imprinted ring-patterned MIP films leaves behind copious cavities for the recognizable specific "memory sites" to efficiently detect small molecules. As a result, the elaborated surface structuring effect, sensitivity, and specific selectivity of the coffee-ring-based MIP sensors are scrutinized by capitalizing on an endocrine-disrupting chemical, 2,4-dichlorophenoxyacetic acid (2,4-D), as an example. Clearly, the evaporative self-assembly of nonvolatile solutes in a confined geometry renders the creation of familiar yet ordered coffee-ring-like patterns for a wide range of applications, including sensors, scaffolds for cell motility, templates, substrates for neuron guidance, etc., thereby dispensing with the need of multistep lithography techniques and external fields.

Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation

Olfaction guides navigation and decision-making in organisms from multiple animal phyla. Understanding how animals use olfactory cues to guide navigation is a complicated problem for two main reasons. First, the sensory cues used to guide animals to the source of an odor consist of volatile molecules, which form plumes. These plumes are governed by turbulent air currents, resulting in an intermittent and spatiotemporally varying olfactory signal. A second problem is that the technologies for chemical quantification are cumbersome and cannot be used to detect what the freely moving animal senses in real time. Understanding how the olfactory system guides this behavior requires knowing the sensory cues and the accompanying brain signals during navigation. Here, we present a method for real-time monitoring of olfactory information using low-cost, lightweight sensors that robustly detect common solvent molecules, like alcohols, and can be easily mounted on the heads of freely behaving mice engaged in odor-guided navigation. To establish the accuracy and temporal response properties of these sensors we compared their responses with those of a photoionization detector (PID) to precisely controlled ethanol stimuli. Ethanol-sensor recordings, deconvolved using a difference-of-exponentials kernel, showed robust correlations with the PID signal at behaviorally relevant time, frequency, and spatial scales. Additionally, calcium imaging of odor responses from the olfactory bulbs (OBs) of awake, head-fixed mice showed strong correlations with ethanol plume contacts detected by these sensors. Finally, freely behaving mice engaged in odor-guided navigation showed robust behavioral changes such as speed reduction that corresponded to ethanol plume contacts.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

Study of Room Temperature Ionic Liquids as Gas Sensing Materials in Quartz Crystal Microbalances

Twenty-eight quartz crystal microbalance (QCM) sensors coated with different sensing films were tested and analyzed in this work; twenty-three sensors were coated in different room temperature ionic liquids (RTILs) and five additional QCM sensors were coated with conventional films commonly used as stationary phases in gas chromatography. Four volatile organic compounds (VOCs), in gaseous phase—hexanol, butyl acetate, 2-hexanone, and hexanoic acid—were measured. Two transducer mechanisms were used; resonant frequency shift and resistance shift of a QCM Mason equivalent circuit. The sensors were characterized by their sensitivity to the VOCs and their discrimination power of the four VOCs. The highest separation among VOCs was obtained when frequency and resistance information of both RTIL and conventional films was used, a sensor array composed by two RTILs (1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide and 1-hexyl-3-methylimidazolium hexafluorophosphate) and two conventional films (tricresyl phosphate and apiezon-L) was found to improve the Wilks lambda separation for the tested gases two orders of magnitude compared to the Wilks lambda using only a conventional films array.