Colorimetric Multigas Sensor Arrays and an Artificial Olfactory Platform for Volatile Organic Compounds

Pd nanoparticles-functionalized In2O3 based gas sensor for highly selective detection of toluene

Given the threat posed by toluene to human health and environmental safety, real-time and efficient detection of toluene assumes paramount importance. However, the low chemical reactivity and structural similarity of benzene, toluene, and xylene (BTX) gases impede the attainment of highly selective toluene detection. Herein, palladium-loaded indium oxide nanospheres were successfully synthesized through a combination of solvothermal and post-reduction methods. And the sensor based on 0.75 wt% Pd-In2O3 exhibits the response to the concentration of 100 ppm toluene (Ra/Rg = 21) that is approximately four times better compared to pure indium oxide (Ra/Rg = 4) at their respective optimum operating temperatures. Moreover, this sensor exhibited enhanced sensing performance towards toluene, including a low operating temperature of 160 °C, exceptional selectivity, and good stability. Furthermore, an investigation into the sensing mechanism of toluene by the Pd-In2O3-based sensor was conducted. The chemical and electron sensitization effects of palladium result in the more chemisorbed oxygen of the sensing material, which improves the toluene sensing performance by enhancing the reaction with more toluene molecules. Additionally, the moderate catalytic activation of toluene by palladium plays a crucial role in improving the selectivity. Overall, this work provides a basis for the rational design of metal oxide semiconductor sensors with catalytic properties for the highly selective detection of toluene.

Identification of Gas Mixture Components with Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data

Chemical vapor sensors are essential for various fields, including medical diagnostics and environmental monitoring. Notably, the identification of components in unknown gas mixtures has great potential for noninvasive diagnosis of diseases such as lung cancer. However, current gas identification techniques, despite the development of electronic nose-based sensor platforms, still lack sufficient classification accuracy for mixed gases. In our previous study, we introduced multichannel hierarchical analysis using a time-resolved hyperspectral system to address the spectral ambiguity of conventional RGB sensor-based colorimetric e-noses. Here, we demonstrate the identification of mixed gas components through time-resolved line hyperspectral measurements with an eight-colorimetric sensor array that uses genetically engineered M13 bacteriophages as gas-selective colorimetric sensors. The time-dependent spectral variations induced by mixed gas in the different colorimetric sensors are converted into a hyperspectral three-dimensional (3D) data cube. For efficient machine learning classification, the data cube was converted into a multichannel spectrogram by applying a novel data processing method, including dimensionality reduction and a block average filter to reduce high-dimensional complexity and improve the signal-to-noise ratio. A convolution filter was then used for hierarchical analysis of the multichannel spectrogram, effectively capturing the complex gas-induced spectral patterns and temporal dynamics. Our study demonstrates a classification accuracy of 93.9% for pure and mixed gases of acetone, ethanol, and xylene at a low concentration of 2 ppm.

Integrating Humidity-Resistant and Colorimetric COF-on-MOF Sensors with Artificial Intelligence Assisted Data Analysis for Visualization of Volatile Organic Compounds Sensing

Direct visualization and monitoring of volatile organic compounds (VOCs) sensing processes via portable colorimetric sensors are highly desired but challenging targets. The key challenge resides in the development of efficient sensing systems with high sensitivity, selectivity, humidity resistance, and profuse color change. Herein, a strategy is reported for the direct visualization of VOCs sensing by mimicking human olfactory function and integrating colorimetric COF-on-MOF sensors with artificial intelligence (AI)-assisted data analysis techniques. The Dye@Zeolitic Imidazolate Framework@Covalent Organic Framework (Dye@ZIF-8@COF) sensor takes advantage of the highly porous structure of MOF core and hydrophobic nature of the COF shell, enabling highly sensitive colorimetric sensing of trace number of VOCs. The Dye@ZIF-8@COF sensor exhibits exceptional sensitivity to VOCs at sub-parts per million levels and demonstrates excellent humidity resistance (under 20-90% relative humidity), showing great promise for practical applications. Importantly, AI-assisted information fusion and perceptual analysis greatly promote the accuracy of the VOCs sensing processes, enabling direct visualization and classification of seven stages of matcha drying processes with a superior accuracy of 95.74%. This work paves the way for the direct visualization of sensing processes of VOCs via the integration of advanced humidity-resistant sensing materials and AI-assisted data analyzing techniques.

Rational Design and Application of Breath Sensors for Healthcare Monitoring

Biomarkers contained in human exhaled breath are closely related to certain diseases. As a noninvasive, portable, and efficient health diagnosis method, the breath sensor has received considerable attention in recent years for early disease screening and prevention due to its user-friendly and easy-accessible features. Although some key challenges have been addressed, its capability to precisely monitor specific biomarkers of interest and its physiological relevance to health metrics is still to be ascertained. In this context, we analyzed the rational design and recent advance of breath sensors for healthcare monitoring. This review begins with an introduction to exhaled breath biomarkers and their sensing technologies, such as chemoresistive, humidity-sensitive, electrochemical, and colorimetric principles. Then, a systematic overview of their emerging applications in early disease screening, drunk driving inspection, apnea monitoring, and exhaled breath condensate analysis are demonstrated. Finally, we discuss the challenges and opportunities of breath sensors for noninvasive healthcare monitoring. With the ongoing research efforts, the continuous breakthrough in breath sensors and their attractive applications is foreseeable in the future.

Multiwalled Carbon Nanotube-Templated Nickel Porphyrin Covalent Organic Framework for Pencil-Drawn Noninvasive Respiration Sensors

Paper-integrated configuration with miniaturized functionality represents one of the future main green electronics. In this study, a paper-based respiration sensor was prepared using a multiwalled carbon nanotube-templated nickel porphyrin covalent organic framework (MWCNTs@COFNiP-Ph) as an electrical identification component and pencil-drawn graphite electric circuits as interdigitated electrodes (IDEs). The MWCNTs@COFNiP-Ph not only inherited the high gas sensing performance of porphyrin and the aperture induction effect of COFs but also overcame the shielding effect between phases through the MWCNT template. Furthermore, it possessed highly exposed M-N4 metallic active sites and unique periodic porosity, thereby effectively addressing the key technical issue of room-temperature sensing for the respiration sensor. Meanwhile, the introduction of a pencil-drawing approach on common printing papers facilitates the inexpensive and simple manufacturing of the as-fabricated graphite IDE. Based on the above advantages, the MWCNTs@COFNiP-Ph respiration sensor had the characteristics of wide detection range (1-500 ppm), low detection limit (30 ppb), acceptable flexibility for toluene, and rapid response/recovery time (32 s/116 s). These advancements facilitated the integration of the respiration sensor into surgical masks and clothes with maximum functionality at a minimized size and weight. Moreover, the primary internal mechanism of COFNiP-Ph for this efficient toluene detection was investigated through in situ FTIR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers, resulting in alterations in sensor resistance upon exposure to the target gas. The encouraging results revealed the feasibility of employing a paper-sensing system as a wearable platform in green electronics.

Plasmon-Stimulated Colorimetry Biosensor Array for the Identification of Multiple Metabolites

Rationally designing highly catalytic and stable nanozymes for metabolite monitoring is of great importance because of their huge potential in early disease diagnosis. Herein, a novel nanozyme based on hierarchically structured CuS/ZnS with a highly efficient peroxidase (POD)-mimic capability was developed and synthesized for multiple metabolite determination and recognition via the plasmon-stimulated biosensor array strategy. The designed nanozyme can simultaneously harvest plasmon triggered hot electron-hole pairs and generate photothermal properties, leading to a sharply boosted POD-mimic capability under 808 nm laser irradiation. Interestingly, because of the interaction diversity of the metabolite with POD-like nanomaterials, the unique inhibitory effect of metabolites on the POD-mimic activity could be the signal response as the differentiation. Thus, utilizing TMB as a typical chromogenic substrate in the addition of H2O2, the designed colorimetric biosensor array can produce diverse fingerprints for the three vital metabolisms (cysteine (Cys), ascorbic acid (AA), and glutathione (GSH)), which can be precisely identified by principal component analysis (PCA). Notably, a distinct fingerprint of a single metabolite with different levels and metabolite mixtures is also achieved with a detection limit of 1 μM. Most importantly, cell lysis could be effectively discriminated by the biosensor assay, implying its great potential in clinical diagnosis.