NOx Sensor Constructed from Conductive Metal-Organic Framework and Graphene for Airway Inflammation Screening

A Prototype of Graphene E-Nose for Exhaled Breath Detection and Label-Free Diagnosis of Helicobacter Pylori Infection

Helicobacter pylori (HP), a common microanaerobic bacteria that lives in the human mouth and stomach, is reported to infect ≈50% of the global population. The current diagnostic methods for HP are either invasive, time-consuming, or harmful. Therefore, a noninvasive and label-free HP diagnostic method needs to be developed urgently. Herein, reduced graphene oxide (rGO) is composited with different metal-based materials to construct a graphene-based electronic nose (e-nose), which exhibits excellent sensitivity and cross-reactive response to several gases in exhaled breath (EB). Principal component analysis (PCA) shows that four typical types of gases in EB can be well discriminated. Additionally, the potential of the e-nose in label-free detection of HP infection is demonstrated through the measurement and analysis of EB samples. Furthermore, a prototype of an e-nose device is designed and constructed for automatic EB detection and HP diagnosis. The accuracy of the prototype machine integrated with the graphene-based e-nose can reach 92% and 91% in the training and validation sets, respectively. These results demonstrate that the highly sensitive graphene-based e-nose has great potential for the label-free diagnosis of HP and may become a novel tool for non-invasive disease screening and diagnosis.

High-precision Helicobacter pylori infection diagnosis using a dual-element multimodal gas sensor array

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope 13C or 14C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH2-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

Porous materials as effective chemiresistive gas sensors

Chemiresistive gas sensors (CGSs) have revolutionized the field of gas sensing by providing a low-power, low-cost, and highly sensitive means of detecting harmful gases. This technology works by measuring changes in the conductivity of materials when they interact with a testing gas. While semiconducting metal oxides and two-dimensional (2D) materials have been used for CGSs, they suffer from poor selectivity to specific analytes in the presence of interfering gases and require high operating temperatures, resulting in high signal-to-noise ratios. However, nanoporous materials have emerged as a promising alternative for CGSs due to their high specific surface area, unsaturated metal actives, and density of three-dimensional inter-connected conductive and pendant functional groups. Porous materials have demonstrated excellent response and recovery times, remarkable selectivity, and the ability to detect gases at extremely low concentrations. Herein, our central emphasis is on all aspects of CGSs, with a primary focus on the use of porous materials. Further, we discuss the basic sensing mechanisms and parameters, different types of popular sensing materials, and the critical explanations of various mechanisms involved throughout the sensing process. We have provided examples of remarkable performance demonstrated by sensors using these materials. In addition to this, we compare the performance of porous materials with traditional metal-oxide semiconductors (MOSs) and 2D materials. Finally, we discussed future aspects, shortcomings, and scope for improvement in sensing performance, including the use of metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and porous organic polymers (POPs), as well as their hybrid counterparts. Overall, CGSs using porous materials have the potential to address a wide range of applications, including monitoring water quality, detecting harmful chemicals, improving surveillance, preventing natural disasters, and improving healthcare.