Prospects and Challenges of Volatile Organic Compound Sensors in Human Healthcare

Photonic Solutions for Challenges in Sensing

Sensing technologies support timely and critical decisions to save precious resources in healthcare, veterinary care, food safety, and environmental protection. However, the design of sensors demands strict technical characteristics for real-world applications. In this Viewpoint, we discuss the main challenges to tackle in the sensing field and how photonics represents a valuable tool in this sphere.

Emerging trends in metal oxide-based electronic noses for healthcare applications: a review

An electronic nose (E-nose) is a technology fundamentally inspired by the human nose, designed to detect, recognize, and differentiate specific odors or volatile components in complex and chaotic environments. Comprising an array of sensors with meticulously designed nanostructured architectures, E-noses translate the chemical information captured by these sensors into useful metrics using complex pattern recognition algorithms. E-noses can significantly enhance the quality of life by offering preventive point-of-care devices for medical diagnostics through breath analysis, and by monitoring and tracking hazardous and toxic gases in the environment. They are increasingly being used in defense and surveillance, medical diagnostics, agriculture, environmental monitoring, and product validation and authentication. The major challenge in developing a reliable E-nose involves miniaturization and low power consumption. Various sensing materials are employed to address these issues. This review presents the key advancements over the last decade in E-nose technology, specifically focusing on chemiresistive metal oxide sensing materials. It discusses their sensing mechanisms, integration into portable E-noses, and various data analysis techniques. Additionally, we review the primary metal oxide-based E-noses for disease detection through breath analysis. Finally, we address the major challenges and issues in developing and implementing a portable metal oxide-based E-nose.

Recent Progress and Challenges of Implantable Biodegradable Biosensors

Implantable biosensors have evolved to the cutting-edge technology of personalized health care and provide promise for future directions in precision medicine. This is the reason why these devices stand to revolutionize our approach to health and disease management and offer insights into our bodily functions in ways that have never been possible before. This review article tries to delve into the important developments, new materials, and multifarious applications of these biosensors, along with a frank discussion on the challenges that the devices will face in their clinical deployment. In addition, techniques that have been employed for the improvement of the sensitivity and specificity of the biosensors alike are focused on in this article, like new biomarkers and advanced computational and data communicational models. A significant challenge of miniaturized in situ implants is that they need to be removed after serving their purpose. Surgical expulsion provokes discomfort to patients, potentially leading to post-operative complications. Therefore, the biodegradability of implants is an alternative method for removal through natural biological processes. This includes biocompatible materials to develop sensors that remain in the body over longer periods with a much-reduced immune response and better device longevity. However, the biodegradability of implantable sensors is still in its infancy compared to conventional non-biodegradable ones. Sensor design, morphology, fabrication, power, electronics, and data transmission all play a pivotal role in developing medically approved implantable biodegradable biosensors. Advanced material science and nanotechnology extended the capacity of different research groups to implement novel courses of action to design implantable and biodegradable sensor components. But the actualization of such potential for the transformative nature of the health sector, in the first place, will have to surmount the challenges related to biofouling, managing power, guaranteeing data security, and meeting today's rules and regulations. Solving these problems will, therefore, not only enhance the performance and reliability of implantable biodegradable biosensors but also facilitate the translation of laboratory development into clinics, serving patients worldwide in their better disease management and personalized therapeutic interventions.

Fully Transient 3D Origami Paper-Based Ammonia Gas Sensor Obtained by Facile MXene Spray Coating

Developing high-performance chemiresistive gas sensors with mechanical compliance for environmental or health-related biomarker monitoring has recently drawn increasing research attention. Among them, two-dimensional MXene materials hold great potential for room-temperature hazardous gas (e.g., NH3) monitoring regardless of the complicated fabrication process, insufficient 2D/3D flexibilities, and poor environmental sustainability. Herein, a Ti3C2Tx MXene/gelatin ink was developed for patterning electrodes through a facile spray coating. Particularly, the patterned Ti3C2Tx-based coating exhibited good adhesion on the paper substrate against repeated peeling-off and excellent mechanical flexibility against 1000 cyclic stretching. The porous morphology of the coating facilitated the NH3 sensing ability. As a result, the 2D kirigami-shaped NH3 sensor exhibited a good response of 7% to 50 ppm of NH3 with detectable concentrations ranging from 5-500 ppm, decent selectivity over interferences, etc., which could be well-maintained even at 50% stretched state. In addition, with the help of mechanically guided compressive buckling, 3D mesostructured MXene origamis could be obtained, holding promise for detecting the coming direction and height distribution of hazardous gas, e.g., the NH3. More importantly, the as-fabricated MXene/gelatin origami paper could be fully degraded in PBS/H2O2/cellulase solution within 19 days, demonstrating its potential as a high-performance, shape morphable, and environmentally friendly wearable gas sensor.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Advancements in non-invasive skin sampling: Clinical conditions characterization via the assessment of skin surface cytokine biomarkers

The skin is increasingly recognized as a biological active organ interacting with the immune system. Given that the epidermal skin layer actively releases various cytokines, non-invasive skin sampling methods could detect these cytokines, offering insights into clinical conditions. This study aims non-invasively measuring cytokine levels directly from the skin surface to characterize different inflammatory chronic disorders in the adult and elderly population: psoriasis, diabetes type 2, rosacea, chronic kidney disease (CKD) and aging. Cytokines IL-1β, IL-8 and IL-10 were sampled from healthy subjects and patients aged 18-80 using skin surface wash technique. A well with sterile phosphate-buffered saline solution was placed on the skin for 30 min, and the extracted solution was collected from the well for further cytokine levels analysis using ELISA assay. Results show distinct cytokine profiles in different pathological processes, healthy controls, affected and unaffected areas. Aging was associated with increased IL-1β, IL-8, and IL-10 levels in skin. In diabetes, IL-1β and IL-8 levels were elevated in lesional areas, while IL-10 levels were decreased in non-lesional skin. Psoriatic lesions showed elevated levels of IL-1β and IL-8. Rosacea patients had lower IL-10 levels in both lesional and non-lesional areas. CKD patients exhibited significantly lower IL-10 levels compared to healthy individuals. In conclusion, skin surface wash-derived cytokine profiles could serve as "alert biomarkers" for disease prediction, enabling early detection. Additionally, this method's cost-effectiveness allows pre-screening of molecules in clinical studies and holds potential as a tool for biomarkers and omics analysis, enhancing disorder characterization and disease management.

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.

Graphene as a Sensor for Lung Cancer: Insights into Adsorption of VOCs Using vdW DFT

The adsorption mechanism of individual volatile organic compounds (VOCs) on the surface of graphene is investigated using nonempirical van der Waals (vdW) density functional theory. The VOCs chosen as adsorbates are ethanol, benzene, and toluene, which are found in the exhaled breath of lung cancer patients. The most energetically favorable configurations of the adsorbed systems, adsorption energy profiles, charge transfer, and work function are calculated. The fundamental insight into the interactions between the considered VOC molecules and graphene through molecular doping, i.e., charge transfer, is estimated. It is found that the adsorption energy is highly sensitive to the vdW functionals. Adsorption energies calculated by revPBE-vdW are in good agreement with the available experimental data, and the revPBE-vdW functional can cover well the physical phenomena behind the adsorption of these VOCs on graphene. Bader charge analysis shows that 0.064, 0.042, and 0.061e of charge were transferred from the graphene surface to ethanol, benzene, and toluene, respectively. All of the considered VOCs act as electron acceptors from graphene. By analyzing the electronic structure of the adsorption systems, we found that the energy level of the highest occupied molecular orbitals of these considered VOCs is shifted backward toward the Fermi level. The interaction of the VOCs with the π and π* states of the C atoms in graphene breaks the symmetry of graphene, leading to the opening of a band gap at the Fermi level. The adsorption of these considered VOCs onto the pristine graphene produces a band gap of 5-12 meV.

Carbon nanomaterials for sweat-based sensors: a review

Sweat is easily accessible from the human skin's surface. It is secreted by the eccrine glands and contains a wealth of physiological information, including metabolites and electrolytes like glucose and Na ions. Sweat is a particularly useful biofluid because of its easy and non-invasive access, unlike other biofluids, like blood. On the other hand, nanomaterials have started to show promise operation as a competitive substitute for biosensors and molecular sensors throughout the last 10 years. Among the most synthetic nanomaterials that are studied, applied, and discussed, carbon nanomaterials are special. They are desirable candidates for sensor applications because of their many intrinsic electrical, magnetic, and optical characteristics; their chemical diversity and simplicity of manipulation; their biocompatibility; and their effectiveness as a chemically resistant platform. Carbon nanofibers (CNFs), carbon dots (CDs), carbon nanotubes (CNTs), and graphene have been intensively investigated as molecular sensors or as components that can be integrated into devices. In this review, we summarize recent advances in the use of carbon nanomaterials as sweat sensors and consider how they can be utilized to detect a diverse range of analytes in sweat, such as glucose, ions, lactate, cortisol, uric acid, and pH.

Discrimination of toxigenic and non-toxigenic Aspergillus flavus in wheat based on nanocomposite colorimetric sensor array

A novel method was developed for the early detection of wheat infected with Aspergillus flavus (A. flavus) using a nanocomposite colorimetric sensors array (CSA). LC-MS analysis revealed the presence of Aflatoxin B1 (AFB1) and Aflatoxin B2 (AFB2) on day seven, demonstrating mycotoxin variabilities in infected wheat. HS-SPME-GC-MS analysis detected 2-methylbutyral, a gas exclusively associated with toxigenic A. flavus. The CSA was modified using three nanoparticles of MOF and successfully used to detect the wheat infected with A. flavus. Discrimination of different types of infected wheat samples was achieved using the RGB difference map and Principal Component Analysis (PCA) model. Additionally, the Linear Discriminant Analysis (LDA) model accurately predicted the presence of toxigenic A. flavus at various stages of infection. These findings highlight the promising capabilities of nanocomposite CSA for early-stage detection of A. flavus infection in wheat.

Artificial Intelligence-Powered Electronic Skin

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

A PVDF-based colorimetric sensor array for noninvasive detection of multiple disease-related volatile organic compounds

Detection of human-generated volatile organic compounds (VOCs) is a new pathway for assessing health. Herein, a polyvinylidene fluoride (PVDF)-based colorimetric sensor array was designed for detecting disease-related VOCs (DVOCs) within 15 min, using a complex of Cu metal-organic framework, graphene aerogel, and dyes as response materials. Fingermaps derived from 28 DVOCs were obtained for further data processing. Pattern recognition was successfully employed in the correct discrimination of 28 DVOCs in low (10 μM), medium (100 μM), and high (300 μM) concentrations. Importantly, the sensor array also presented excellent discrimination ability and application potential when detecting VOCs produced by human cancer and normal cells. In general, VOC acquisition is noninvasive and harmless, and the PVDF-based sensor arrays are simple and visual. Such advantages expand their further application potential.

Gold Nanoparticles-Functionalized Cotton as Promising Flexible and Green Substrate for Impedometric VOC Detection

This work focuses on the possible application of gold nanoparticles on flexible cotton fabric as acetone- and ethanol-sensitive substrates by means of impedance measurements. Specifically, citrate- and polyvinylpyrrolidone (PVP)-functionalized gold nanoparticles (Au NPs) were synthesized using green and well-established procedures and deposited on cotton fabric. A complete structural and morphological characterization was conducted using UV-VIS and Fourier transform infrared (FT-IR) spectroscopy, atomic force microscopy (AFM), and scanning electron microscopy (SEM). A detailed dielectric characterization of the blank substrate revealed interfacial polarization effects related to both Au NPs and their specific surface functionalization. For instance, by entirely coating the cotton fabric (i.e., by creating a more insulating matrix), PVP was found to increase the sample resistance, i.e., to decrease the electrical interconnection of Au NPs with respect to citrate functionalized sample. However, it was observed that citrate functionalization provided a uniform distribution of Au NPs, which reduced their spacing and, therefore, facilitated electron transport. Regarding the detection of volatile organic compounds (VOCs), electrochemical impedance spectroscopy (EIS) measurements showed that hydrogen bonding and the resulting proton migration impedance are instrumental in distinguishing ethanol and acetone. Such findings can pave the way for the development of VOC sensors integrated into personal protective equipment and wearable telemedicine devices. This approach may be crucial for early disease diagnosis based on nanomaterials to attain low-cost/low-end and easy-to-use detectors of breath volatiles as disease markers.

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.

Noninvasive Colorimetric Detection of Acetic Acid in Human Breath Based on an Alginate/Ni-Al-LDH/Dye Composite Film

Alginate/Ni-Al-layered double hydroxide/dye (Alg/Ni-Al-LDH/dye) composite films were fabricated using the solution casting method. The dyes used included methyl red, phenol red, thymol blue, bromothymol blue, m-cresol purple, methyl orange, bromocresol purple (BP), and bromocresol green (BG) in the overall pH range of 3.8 to 9.6. The chemical composition and morphology of the Alg/Ni-Al-LDH/dye composite film structure were investigated by Fourier transform infrared spectroscopy, FESEM, atomic force microscopy, and X-ray diffraction. The Alg/Ni-Al-LDH/dye composite films were semitransparent and mechanically flexible. Acetic acid was investigated as a respiratory biomarker related to gastrointestinal diseases. The parameters studied included color volume, response time, Ni-Al-LDH nanosheet volume, reusability, and drawing of the calibration curve along with statistical features including standard deviation, relative standard deviation, limit of detection, and limit of quantitation. Colorimetric indicators BP and BG in the presence of acetic acid produce color changes that are almost visible to the naked eye. However, other used indicators have shown almost no change. Therefore, it can be reported that the sensors made in the presence of BP and BG act selectively in relation to acetic acid.

Gas-Phase Biosensors (Bio-Sniffers) for Measurement of 2-Nonenal, the Causative Volatile Molecule of Human Aging-Related Body Odor

The molecule 2-nonenal is renowned as the origin of unpleasant human aging-related body odor that can potentially indicate age-related metabolic changes. Most 2-nonenal measurements rely on chromatographic analytical systems, which pose challenges in terms of daily usage and the ability to track changes in concentration over time. In this study, we have developed liquid- and gas-phase biosensors (bio-sniffers) with the aim of enabling facile and continuous measurement of trans-2-nonenal vapor. Initially, we compared two types of nicotinamide adenine dinucleotide (phosphate) [NAD(P)]-dependent enzymes that have the catalytic ability of trans-2-nonenal: aldehyde dehydrogenase (ALDH) and enone reductase 1 (ER1). The developed sensor quantified the trans-2-nonanal concentration by measuring fluorescence (excitation: 340 nm, emission: 490 nm) emitted from NAD(P)H that was generated or consumed by ALDH or ER1. The ALDH biosensor reacted to a variety of aldehydes including trans-2-nonenal, whereas the ER1 biosensor showed high selectivity. In contrast, the ALDH bio-sniffer showed quantitative characteristics for trans-2-nonenal vapor at a concentration range of 0.4-7.5 ppm (with a theoretical limit of detection (LOD) and limit of quantification (LOQ) of 0.23 and 0.26 ppm, respectively), including a reported concentration (0.85-4.35 ppm), whereas the ER1 bio-sniffer detected only 0.4 and 0.8 ppm. Based on these findings, headspace gas of skin-wiped alcohol-absorbed cotton collected from study participants in their 20s and 50s was measured by the ALDH bio-sniffer. Consequently, age-related differences in signals were observed, suggesting the potential for measuring trans-2-nonenal vapor.

A Novel Urine Test Biosensor Platform for Early Lung Cancer Detection

Lung cancer is the leading cause of cancer-related mortality worldwide. Early detection is essential to achieving a better outcome and prognosis. Volatile organic compounds (VOCs) reflect alterations in the pathophysiology and body metabolism processes, as shown in various types of cancers. The biosensor platform (BSP) urine test uses animals' unique, proficient, and accurate ability to scent lung cancer VOCs. The BSP is a testing platform for the binary (negative/positive) recognition of the signature VOCs of lung cancer by trained and qualified Long-Evans rats as biosensors (BSs). The results of the current double-blind study show high accuracy in lung cancer VOC recognition, with 93% sensitivity and 91% specificity. The BSP test is safe, rapid, objective and can be performed repetitively, enabling periodic cancer monitoring as well as an aid to existing diagnostic methods. The future implementation of such urine tests as routine screening and monitoring tools has the potential to significantly increase detection rate as well as curability rates with lower healthcare expenditure. This paper offers a first instructive clinical platform utilizing VOC's in urine for detection of lung cancer using the innovative BSP to deal with the pressing need for an early lung cancer detection test tool.

Design and Fabrication of Ultrathin Nanoporous Donor-Acceptor Copolymer-Based Organic Field-Effect Transistors for Enhanced VOC Sensing Performance

The development of organic field-effect transistor (OFET) chemical sensors with high sensing performance and good air stability has remained a persistent challenge, thereby hindering their practical application. Herein, an OFET sensor based on a donor-acceptor copolymer is shown to provide high responsivity, sensitivity, and selectivity toward polar volatile organic compounds, as well as good air stability. In detail, a polymer blend of N-alkyl-diketopyrrolo-pyrrole-dithienylthieno[3,2-b]thiophene (DPP-DTT) and polystyrene is coated onto an FET substrate via shearing-assisted phase separation (SAPS) combined with selective solvent etching to fabricate the DPP-DTT-based OFET device having an ultrathin nanoporous structure suitable for gas sensing applications. This is achieved via optimization of the film morphology by varying the shear rate to adjust the dynamic balance between the shear and capillary forces to obtain an ultrathin thickness (∼8 nm) and nanopore size (80 nm) that are favorable for the efficient diffusion and interaction of analytes with the active layer. In particular, the sensor presents high responsivities toward methanol (∼70%), acetone (∼51.3%), ethanol (∼39%), and isopropyl alcohol (IPA) (∼29.8%), along with fast response and recovery times of ∼80 and 234 s, respectively. Moreover, the average sensitivity was determined to be 5.75%/ppm from the linear plot of the responsivity against the methanol concentration in the range of 1-100 ppm. Importantly, the device also exhibits excellent long-term (30-day) air and thermal storage stability, thereby demonstrating its high potential for practical applications.

Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors: Overview

Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring, exhaled breath diagnosis, and food freshness analysis. Among various chemiresistive sensing materials, noble metal-decorated semiconducting metal oxides (SMOs) have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble metals. This review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures (e.g., nanoparticles, nanowires, nanorods, nanosheets, nanoflowers, and microspheres) for high-performance gas sensors with higher response, faster response/recovery speed, lower operating temperature, and ultra-low detection limits. The key topics include Pt, Pd, Au, other noble metals (e.g., Ag, Ru, and Rh.), and bimetals-decorated SMOs containing ZnO, SnO2, WO3, other SMOs (e.g., In2O3, Fe2O3, and CuO), and heterostructured SMOs. In addition to conventional devices, the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also discussed. Moreover, the relevant mechanisms for the sensing performance improvement caused by noble metal decoration, including the electronic sensitization effect and the chemical sensitization effect, have also been summarized in detail. Finally, major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.

Unique Photoactivated Time-Resolved Response in 2D GeS for Selective Detection of Volatile Organic Compounds

Volatile organic compounds (VOCs) sensors have a broad range of applications including healthcare, process control, and air quality analysis. There are a variety of techniques for detecting VOCs such as optical, acoustic, electrochemical, and chemiresistive sensors. However, existing commercial VOC detectors have drawbacks such as high cost, large size, or lack of selectivity. Herein, a new sensing mechanism is demonstrated based on surface interactions between VOC and UV-excited 2D germanium sulfide (GeS), which provides an effective solution to distinguish VOCs. The GeS sensor shows a unique time-resolved electrical response to different VOC species, facilitating identification and qualitative measurement of VOCs. Moreover, machine learning is utilized to distinguish VOC species from their dynamic response via visualization with high accuracy. The proposed approach demonstrates the potential of 2D GeS as a promising candidate for selective miniature VOCs sensors in critical applications such as non-invasive diagnosis of diseases and health monitoring.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

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

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

Calculations of adsorption-dependent refractive indices of metal-organic frameworks for gas sensing applications

Detection of volatile organic compounds (VOCs) is one of the most challenging tasks in modelling breath analyzers because of their low concentrations (parts-per-billion (ppb) to parts-per-million (ppm)) in breath and the high humidity levels in exhaled breaths. The refractive index is one of the crucial optical properties of metal-organic frameworks (MOFs), which is changeable via the variation of gas species and concentrations that can be utilized as gas detectors. Herein, for the first time, we used Lorentz-Lorentz, Maxwell-Ga, and Bruggeman effective medium approximation (EMA) equations to compute the percentage change in the index of refraction (Δn%) of ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr) and HKUST-1 upon exposure to ethanol at various partial pressures. We also determined the enhancement factors of the mentioned MOFs to assess the storage capability of MOFs and the biosensors' selectivity through guest-host interactions, especially, at low guest concentrations.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Connecting the dots for fundamental understanding of structure-photophysics-property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism

Photoactive organic and hybrid organic-inorganic materials such as conjugated polymers, covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and layered perovskites, display intriguing photophysical signatures upon interaction with light. Elucidating structure-photophysics-property relationships across a broad range of functional materials is nontrivial and requires our fundamental understanding of the intricate interplay among excitons (electron-hole pair), polarons (charges), bipolarons, phonons (vibrations), inter-layer stacking interactions, and different forms of structural and conformational defects. In parallel with electronic structure modeling and data-driven science that are actively pursued to successfully accelerate materials discovery, an accurate, computationally inexpensive, and physically-motivated theoretical model, which consistently makes quantitative connections with conceptually complicated experimental observations, is equally important. Within this context, the first part of this perspective highlights a unified theoretical framework in which the electronic coupling as well as the local coupling between the electronic and nuclear degrees of freedom can be efficiently described for a broad range of quasiparticles with similarly structured Holstein-style vibronic Hamiltonians. The second part of this perspective discusses excitonic and polaronic photophysical signatures in polymers, COFs, MOFs, and perovskites, and attempts to bridge the gap between different research fields using a common theoretical construct - the Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art computational approaches with the Multiparticle Holstein Formalism will help identify and establish new, transformative design strategies that will guide the synthesis and characterization of next-generation energy materials optimized for a broad range of optoelectronic, spintronic, and photonic applications.

Rapid Detection of Volatile Organic Compounds by Switch-Scan Tuning of Vernier Quantum-Cascade Lasers

Volatile organic compounds (VOCs) exhibit typically broad and mutually overlapping ro-vibrational absorption fingerprints. This complexity has so far limited the applicability of laser-based spectroscopy for VOC measurements in complex gas matrices. Here, we exploit a Vernier-type quantum-cascade laser (QCL) as an electrically tunable multiwavelength source for selective and sensitive VOC analysis. This emerging class of lasers provides access to several spectral windows by discrete Vernier tuning ("switching") and continuous coverage within these windows ("scanning"). We present a versatile driving technique that efficiently combines the two tuning mechanisms. Applied to our Vernier QCL, it enables the rapid acquisition (within 360 ms) of high-resolution spectra from six individual spectral windows, distributed over a wide range from 1063 to 1102 cm^-1. Gaining access to the broad absorption envelopes of VOCs at multiple frequencies, along with their superimposed fine structure, which are especially pronounced at a reduced sample pressure, offers completely new opportunities in VOC analysis. The potential of this approach is assessed in a direct-laser-absorption setup with acetaldehyde, ethanol, and methanol as benchmark compounds with significant spectral overlaps. A measurement precision of 1-10 ppb is obtained after integration for 10 s at amount fractions below 10 ppm, and excellent linearity is found over at least 3 orders of magnitude. Combined with our dedicated spectral fitting algorithm, we demonstrate highly selective multicompound analyses with less than 3.5% relative expanded uncertainty, even in the presence of a 40× excess of an interfering compound with complete spectral overlap.

Human exhaled air diagnostic markers for respiratory tract infections in subjects receiving mechanical ventilation

Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with thep-values of 2.0 × 10^-5, 1.1 × 10^-4, and 1.7 × 10^-3, respectively, using multiple logistic regression. Furthermore, a score system named 'TrunScore' was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.

A luminescent Eu@SOF film fabricated by electrophoretic deposition as ultrasensitive platform for styrene gas quantitative monitoring through fluorescence sensing and ANNs model

Styrene is a harmful gas widely existing in the air, which can damage human organs. Therefore, it is very crucial to develop a sensitive, portable and simple sensor for monitoring styrene. Herein, we design and fabricate a luminescent Eu@TMA-ME/FTO film (F) through EPD method. F emits bright red light of Eu(III) ions and shows superior fluorescence response to styrene gas as a sensor, which enable real-time and quantitative monitoring for styrene gas. More importantly, F exhibits a linear response to styrene gas in a wide concentration range of 10^-7 to 10^-2 M and a low DL with 0.20 ppm. The efficient PET process to styrene induced by ME and the competitive absorption between styrene and F are responsible for the sensing mechanism. Besides, the detection of styrene solution is also investigated in deionized water, tap water and river water. For the further application, an intelligent ANNs model has been constructed to process the fluorescence sensing results, which can convert fluorescence sensing images to the concentration of styrene gas. The data demonstrates that ANNs model can accurately monitor the concentration of styrene gas via deep ML without tedious data processing.

Role of Wearable Sensing Technology to Manage Long COVID

Long COVID consequences have changed the perception towards disease management, and it is moving towards personal healthcare monitoring. In this regard, wearable devices have revolutionized the personal healthcare sector to track and monitor physiological parameters of the human body continuously. This would be largely beneficial for early detection (asymptomatic and pre-symptomatic cases of COVID-19), live patient conditions, and long COVID monitoring (COVID recovered patients and healthy individuals) for better COVID-19 management. There are multitude of wearable devices that can observe various human body parameters for remotely monitoring patients and self-monitoring mode for individuals. Smart watches, smart tattoos, rings, smart facemasks, nano-patches, etc., have emerged as the monitoring devices for key physiological parameters, such as body temperature, respiration rate, heart rate, oxygen level, etc. This review includes long COVID challenges for frequent monitoring of biometrics and its possible solution with wearable device technologies for diagnosis and post-therapy of diseases.

Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance

The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.

Terahertz Spectroscopic Molecular Sensor for Rapid and Highly Specific Quantitative Analytical Gas Sensing

Quantitative analytical gas sampling is of great importance in a range of environmental, safety, and scientific applications. In this article, we present the design, operation, and performance of a recently developed tabletop terahertz (THz) spectroscopic molecular sensor capable of rapid (minutes) and sensitive detection of polar gaseous analytes with near "absolute" specificity. A novel double-coil absorption cell design and an array of room-temperature sorbent-based preconcentration modules facilitate quantitative THz detection of light polar volatile compounds, which often challenge the capabilities of established gas sensing techniques. Acetone, ethanol, methanol, acetaldehyde, formaldehyde, and isoprene are detected at low parts-per-billion to high parts-per-trillion levels. This work evaluates performance-limiting factors for THz spectroscopy-based chemical identification: (1) spectral signal to noise and (2) preconcentrator efficiency.

Piezoelectric Gas Sensors with Polycomposite Coatings in Biomedical Application

When developing methods for diagnosing pathologies and diseases in humans and animals using electronic noses, one of the important trends is the miniaturization of devices, while maintaining significant information for diagnostic purposes. A combination of several sorbents that have unique sorption features of volatile organic compounds (VOCs) on one transducer is a possible option for the miniaturization of sensors for gas analysis. This paper considers the principles of creating polycomposite coatings on the electrodes of piezoelectric quartz resonators, including the choice of sorbents for the formation of sensitive layers, determining the mass and geometry of the formation of sensitive layers in a polycomposite coating, as well as an algorithm for processing the output data of sensors to obtain maximum information about the qualitative and quantitative composition of the gas phase. A comparative analysis of the efficiency and kinetics of VOC vapor sorption by sensors with polycomposite coatings and a set of sensors with relevant single coatings has been carried out. Regression equations have been obtained to predict the molar-specific sensitivity of the microbalance of VOC vapors by a sensor with a polycomposite coating of three sorbents with an error of 5-15% based on the results of the microbalance of VOC vapors on single coatings. A method for creating "visual prints" of sensor signals with polycomposite coatings is shown, with results comparable to those from an array of sensors. The parameters A_ij∑ are proposed for obtaining information on the qualitative composition of the gas phase when processing the output data of sensors with polycomposite coatings. A biochemical study of exhaled breath condensate (EBC) samples, a microbiological investigation of calf tracheal washes, and a clinical examination were conducted to assess the presence of bovine respiratory disease (BRD). An analysis of the gas phase over EBC samples with an array of sensors with polycomposite coatings was also carried out. The "visual prints" of the responses of sensors with polycomposite coatings and the results of the identification of VOCs in the gas phase over EBC samples were compared to the results of bacteriological studies of tracheal washes of the studied calves. A connection was found between the parameters A_ij∑ of a group of sensors with polycomposite coatings and the biochemical parameters of biosamples. The adequacy of replacing an array of piezoelectric sensors with single coatings by the sensors with polycomposite coatings is shown.

Two-Dimensional (PEA)2PbBr4 Perovskites Sensors for Highly Sensitive Ethanol Vapor Detection

Two-dimensional (2D) perovskite have been widely researched for solar cells, light-emitting diodes, photodetectors because of their excellent environmental stability and optoelectronic properties in comparison to three-dimensional (3D) perovskite. In this study, we demonstrate the high response of 2D-(PEA)₂PbBr₄ perovskite of the horizontal vapor sensor was outstandingly more superior than 3D-MAPbBr₃ perovskite. 2D transverse perovskite layer have the large surface-to-volume ratio and reactive surface, with the charge transfer mechanism, which was suitable for vapor sensing and trapping. Thus, 2D perovskite vapor sensors demonstrate the champion current response ratio R of 107.32 under the ethanol vapors, which was much faster than 3D perovskite (R = 2.92).

Diagnostic Ability of Volatile Organic Compounds in Digestive Cancer: A Systematic Review With Meta-Analysis

Background:

Volatile organic compounds (VOCs) have been involved in cancer diagnosis via breath, urine, and feces. We aimed to assess the diagnostic ability of VOCs on digestive cancers.

Methods:

We systematically reviewed prospective clinical trials evaluating VOCs’ diagnostic ability on esophageal, gastric, colorectal, hepatic, and pancreatic cancer (PC). Databases including PubMed and Ovid-Medline were searched.

Results:

A total of 35 trials with 5314 patient-times qualified for inclusion. The pooled sensitivity of VOCs diagnosing gastroesophageal cancer from healthy controls is 0.89 (95% confidence interval [CI]: 0.82-0.94), the pooled specificity is 0.890 (95% CI: 0.84-0.93), and area under the curve (AUC) of the summary receiver operating characteristic curve is 0.95 (95% CI: 0.93-0.95). The pooled sensitivity of VOCs diagnosing colorectal cancer from heathy controls is 0.92 (95% CI: 0.85-0.96), the pooled specificity is 0.88 (95% CI: 0.77-0.94), and the AUC is 0.96 (95% CI: 0.94-0.97). The pooled sensitivity of VOCs distinguishing gastrointestinal (GI) cancer from precancerous lesions is 0.84 (95% CI: 0.67-0.92), the pooled specificity is 0.74 (95% CI: 0.43-0.91), and the AUC is 0.87 (95% CI: 0.84-0.89). The pooled sensitivity of VOCs diagnosing hepatocellular carcinoma is 0.68 (95% CI: 0.52-0.81), the pooled specificity is 0.81 (95% CI: 0.47-0.96), and the AUC is 0.78 (95% CI: 0.74-0.81). The pooled sensitivity of VOCs diagnosing PC is 0.88 (95% CI: 0.80-0.93), the pooled specificity is 0.82 (95% CI: 0.62-0.93), and the AUC is 0.92 (95% CI: 0.89-0.94).

Conclusions:

Volatile organic compounds have potential role in diagnosing GI cancer with comparatively high sensitivity, specificity, and AUC (PROSPERO registration number: CRD42021260039).

Fully Inkjet-Printed Chemiresistive Sensor Array Based on Molecularly Imprinted Sol-Gel Active Materials

The fabrication of chemiresistive sensors by inkjet printing is recognized as a breakthrough in gas-sensing applications. One challenge of this technology, however, is how to enhance the cross-selectivity of the sensor array. Herein, we present a ketjen black (KB) ink and molecularly imprinted sol-gel (MISG) inks to support the fabrication of a fully inkjet-printed chemiresistive sensor array, enabling the highly accurate recognition of volatile organic acids (VOAs) on the molecular level. The MISG/KB sensor array was prepared on a glossy photographic paper with a three-layer structure: a circuit layer by a commercial silver ink, a conductive layer by a KB ink, and an active selective layer by MISG inks imprinted by different templates. Hexanoic acid (HA), heptanoic acid, and octanoic acid were used as templates to prepare the MISGs and as targets to evaluate the detection and discrimination performance of the sensor array. Three resultant MISG/KB sensors exhibited high sensitivity and selectivity to VOA vapors. The limit of detection and imprinting factor were 0.018 ppm and 7.82, respectively, for HA-MISG/KB sensors to the corresponding target. With linear discriminant analysis of the gas responses, the MISG/KB sensor array can realize high discrimination to VOAs in single and binary mixtures. Furthermore, the proposed sensor array showed strong sensor robustness with excellent consistency, durability, bending, and humidity resistance. This work developed a fully inkjet-printed chemiresistive sensor array, enabling the realization of high cross-selectivity detection, achieving low-cost, scalable, and highly reproducible sensor fabrication, moving it closer to reliable, commercial, and wearable multi-analyte human body odor analysis potential.

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.

Metal-Nanoparticle-Supported Nanozyme-Based Colorimetric Sensor Array for Precise Identification of Proteins and Oral Bacteria

Convenient, precise, and high-throughput discrimination of multiple bioanalytes is of great significance for an early diagnosis of diseases. Array-based pattern recognition has proven to be a powerful tool to detect diverse analytes, but developing sensing elements featuring favorable surface diversity still remains a challenge. In this work, we presented a simple and facile method to prepare programmable metal-nanoparticle (NP)-supported nanozymes (MNNs) as artificial receptors for the accurate identification of multiple proteins and oral bacteria. The in situ reduction of metal NPs on hierarchical MoS₂ on polypyrrole (PPy), which generated differential nonspecific interactions with bioanalytes, was envisaged as the encoder to break through the limited supply of the receptor's quantity. As a proof of concept, three metal NPs, i.e., Au, Ag, and Pd NPs, were taken as examples to deposit on PPy@MoS₂ as colorimetric probes to construct a cross-reactive sensor array. Based on the principal component analysis (PCA), the proposed MNN sensor array could well discriminate 11 proteins with unique fingerprint-like patterns at a concentration of 250 nM and was sufficiently sensitive to determine individual proteins with a detection limit down to the nanomolar level. Remarkably, two highly similar hemoglobins from different species (hemoglobin and bovine hemoglobin) have been precisely identified. Additionally, five oral bacteria were also well separated from each other without cross-classification at the level of 10⁷ CFU mL^-1. Furthermore, the sensor array allowed effective discrimination of complex protein mixtures either at different molar ratios or with minor varying components. Most importantly, the blind samples, proteins in human serums, proteins in simulated body fluid environment, the heat-denatured proteins, and even clinical cancer samples all could be well distinguished by the sensor array, demonstrating the real-world applications in clinical diagnosis.

Electronic Nose and Tongue for Assessing Human Microbiota

The technological developments of recent times have allowed the use of innovative approaches to support the diagnosis of various diseases. Many of such clinical conditions are often associated with metabolic unbalance, in turn producing an alteration of the gut microbiota even during asymptomatic stages. As such, studies regarding the microbiota composition in biological fluids obtained by humans are continuously growing, and the methodologies for their investigation are rapidly changing, making it less invasive and more affordable. To this extent, Electronic Nose and Electronic Tongue tools are gaining importance in the relevant field, making them a useful alternative—or support—to traditional analytical methods. In light of this, the present manuscript seeks to investigate the development and use of such tools in the gut microbiota assessment according to the current literature. Significant gaps are still present, particularly concerning the Electronic Tongue systems, however the current evidence highlights the strong potential such tools own to enter the daily clinical practice, with significant advancement concerning the patients’ acceptability and cost saving for healthcare providers.

Breath Sensor Technology for the Use in Mechanical Lung Ventilation Equipment for Monitoring Critically Ill Patients

Background: The need for mechanical lung ventilation is common in critically ill patients, either with COVID-19 infection or due to other causes. Monitoring of patients being ventilated is essential for timely and improved management. We here propose the use of a novel breath volatile organic compound sensor technology to be used in a mechanical lung ventilation machine for this purpose; the technology was evaluated in critically ill COVID-19 patients on mechanical lung ventilation. Methods: Based on the consistency results of our study data, the breath sensor device with metal oxide gas sensors and environment-controlling sensors was mounted on the ventilation exhaust port of the ventilation machine; this allowed to ensure additional safety since the device was placed outside the contour between the patient and equipment. Results: The sensors allowed stable registration of the signals for up to several weeks for 10 patients in total, depending on the storage amount; a proportion of patients were intubated or received tracheostoma during the evaluation period. Future studies are on the way to correlate sensor readings to other parameters characterizing the severity of the patient condition and outcome. Conclusions: We suppose that such technology will allow patient monitoring in real-time for timely identification of deterioration, potentially requiring some change of management. The obtained results are preliminary and further studies are needed to examine their clinical significance.

Simple, efficient and accurate method toward the monitoring of ethyl butanoate traces

We introduce in this research a simple, accurate, safe, and efficient design for the detection of ethyl butanoate that be present in the dry exhaled breath. In particular, the presence of ethyl butanoate in the dry exhaled breath could be utilized as a platform for the diagnosing of COVID 19. The main idea of this theoretical investigation is based on the inclusion of a cavity layer between a thin layer of Au and the well-known one-dimension photonic crystals. Accordingly, the cavity layer is filled with dry exhaled breath. The numerical results are investigated in the vicinity of the Drude model and transfer matrix method. The investigated results show the appearance of Tamm plasmon resonance in the reflectance spectrum of our design through the IR region. Such resonant mode provides very high sensitivity with the change in the concentration of ethyl butanoate. We have examined the performance of the proposed sensor by calculating its sensitivity, detection limit, detection accuracy, quality factor and figure of merit. The designed sensor could receive sensitivity of 0.3 nm/ppm or 260,486 nm/RIU, resolution of 7 ppm and quality factor of 969.

Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs)

The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors.

Cr-Doped Pd Metallene Endows a Practical Formaldehyde Sensor New Limit and High Selectivity

Electrochemical sensors for detecting micromolecule organics are desirable for improving the perception of environmental quality and human health. However, currently, the electrochemical sensors for formaldehyde are substantially limited on the market due to the long-term unsolved problems of the low electrooxidation efficiency and CO poisoning issue of commercial Pd catalysts. Here, a 2D Cr-doped Pd metallene (Cr-Pdene) with few atomic layers is shown as an advanced catalyst for ultrasensitive and selective sensing of formaldehyde via a highly efficient formaldehyde electrooxidation. It is found that the doping of Cr into Pd metallene can efficiently optimize the electronic structure of Pd and weaken the interaction between Pd and CO, providing an anti-poisoning means to favor CO₂ production and suppress CO adsorption. The Cr-Pdene-based electrochemical sensor exhibits one order of magnitude higher detection range and, especially, much higher anti-interference for formaldehyde than that of the conventional sensors. Most importantly, it is demonstrated that the Cr-Pdene can be integrated into commercializable wireless sensor networks or handheld instruments for promising applications relating to the environment, health, and food.

Electrocatalytic detection of ethanol and acetaldehyde by aminoxyl radicals: utilizing molecular catalysis for breath analysis

Using molecular catalysis and functional group dependence reactivities of catalysts for breath analysis.