Green light-driven acetone gas sensor based on electrospinned CdS nanospheres/Co3O4 nanofibers hybrid for the detection of exhaled diabetes biomarker

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.

Simultaneously Engineering Oxygen Defects and Heterojunction into Ho-Doped ZnO Nanoflowers for Enhancing n-Propanol Gas Detection

Lung cancer poses a serious threat to people's lives and health due to its high incidence rate and high mortality rate, making it necessary to effectively conduct early screening. As an important biomarker for lung cancer, the detection of n-propanol gas suffers from a low response value and a high detection limit. In this paper, flower-like Ho-doped ZnO was fabricated by the coprecipitation method for n-propanol detection at subppm concentrations. The gas sensor based on the 3% Ho-doped ZnO showed selectivity to n-propanol gas. Its response value to 100 ppm n-propanol was 341 at 140 °C, and its limit of detection (LOD) was about 25.6 ppb, which is lower than that of n-propanol in the breath of a healthy person (150 ppb). The calculation results show that the adsorption of n-propanol on a Ho-doped ZnO surface releases more energy than isopropanol, ethanol, formaldehyde, acetone, and ammonia. The enhanced gas-sensing properties of the Ho-doped ZnO material can be attributed to the fact that the Ho-doping distorts the crystal lattice of the ZnO, increases the specific surface area, and generates a large amount of oxygen defects. In addition, the doped Ho partially forms a Ho2O3/ZnO heterojunction in the material and improves the gas-sensing properties. The 3% Ho-doped ZnO material is expected to be a promising candidate for the trace detection of n-propanol gas.

Gas Sensors Based on Semiconductor Metal Oxides Fabricated by Electrospinning: A Review

Electrospinning has revolutionized the field of semiconductor metal oxide (SMO) gas sensors, which are pivotal for gas detection. SMOs are known for their high sensitivity, rapid responsiveness, and exceptional selectivity towards various types of gases. When synthesized via electrospinning, they gain unmatched advantages. These include high porosity, large specific surface areas, adjustable morphologies and compositions, and diverse structural designs, improving gas-sensing performance. This review explores the application of variously structured and composed SMOs prepared by electrospinning in gas sensors. It highlights strategies to augment gas-sensing performance, such as noble metal modification and doping with transition metals, rare earth elements, and metal cations, all contributing to heightened sensitivity and selectivity. We also look at the fabrication of composite SMOs with polymers or carbon nanofibers, which addresses the challenge of high operating temperatures. Furthermore, this review discusses the advantages of hierarchical and core-shell structures. The use of spinel and perovskite structures is also explored for their unique chemical compositions and crystal structure. These structures are useful for high sensitivity and selectivity towards specific gases. These methodologies emphasize the critical role of innovative material integration and structural design in achieving high-performance gas sensors, pointing toward future research directions in this rapidly evolving field.

MOF-derived xPd-NPs@ZnO porous nanocomposites for ultrasensitive ppb-level gas detection with photoexcitation: Design, diverse-scenario characterization, and mechanism

Metal-organic frameworks (MOFs) have been regarded as a potential candidate with great application prospects in the field of gas sensing. Although plenty of previous efforts have been made to improve the sensitivity of MOF-based nanocomposites, it is still a great challenge to realize ultrafast and high selectivity to typical flammable gases in a wide range. Herein, porous xPd-NPs@ZnO were prepared by optimized heat treatment, which maintained the controllable morphology and high specific surface area of 471.08 m2g-1. The coupling effects of photoexcitation and thermal excitation on the gas-sensing properties of nanocomposites were systematically studied. An ultrafast high response of 88.37 % towards 200 ppm H2 was realized within 1.2 s by 5.0Pd-NPs@ZnO under UV photoexcitation. All xPd-NPs@ZnO exhibited favorable linearity over an extremely wide range (0.2-4000 ppm H2) of experimental tests, indicating the great potential in quantitative detection. The photoexcited carriers enabled the nanocomposites a considerable response at lower operating temperatures, which made diverse applications of the sensors. The mechanisms of high sensing performances and the photoexcitation enhancement were systematically explained by DFT calculations. This work provides a solid experimental foundation and theoretical basis for the design of controllable porous materials and novel photoexcited gas detection.

Exhaled breath signal analysis for diabetes detection: an optimized deep learning approach

In this study, a flexible deep learning system for breath analysis is created using an optimal hybrid deep learning model. To improve the quality of the gathered breath signals, the raw data are first pre-processed. Then, the most relevant features like Improved IMFCC, BFCC (bark frequency), DWT, peak detection, QT intervals, and PR intervals are extracted. Then, using these features the hybrid classifiers built into the diabetic's detection phase is trained. The diabetic detection phase is modeled with an optimized DBN and BI-GRU model. To enhance the detection accuracy of the proposed model, the weight function of DBN is fine-tuned with the newly projected Sine Customized by Marine Predators (SCMP) model that is modeled by conceptually blending the standard MPA and SCA models, respectively. The final outcome from optimized DBN and Bi-GRU is combined to acquire the ultimate detected outcome. Further, to validate the efficiency of the projected model, a comparative evaluation has been undergone. Accordingly, the accuracy of the proposed model is above 98%. The accuracy of the proposed model is 54.6%, 56.9%, 56.95, 44.55, 57%, 56.95, 18.2%, and 56.9% improved over the traditional models like CNN + LSTM, CNN + LSTM, CNN, LSTM, RNN, SVM, RF, and DBN, at 60th learning percentage.

Real-Time Monitoring of Air Discharge in a Switchgear by an Intelligent NO2 Sensor Module

An air-insulated power equipment adopts air as the insulating medium and is widely implemented in power systems. When discharge faults occur, the air produces decomposition products represented by NO2. The efficient NO2 sensor enables the identification of electrical equipment faults. However, single-sensor-dependent NO2 detection is vulnerable to interfering gases. Implementing the sensor array could reduce the interference and improve detection efficiency. In the field of NO2 detection, In2O3 sensors have exhibited tremendous advantages. In our work, four composites based on In2O3 are integrated into sensor arrays, which could detect 250 ppb of NO2 and exhibit excellent selectivity when simultaneously exposed to CO. To further reduce the impact of humidity on gas-sensing performance, a convolutional neural network and a long short-term memory model equipped with an attention mechanism are proposed to evaluate NO2 concentration within 1 ppm, and the detection error is 63.69 ppb. In addition, the NO2 concentration estimation platform based on a microgas sensor is established to detect air discharge faults. The average concentration of NO2 generated by 10 consecutive discharge faults at 15 kV is 726.58 ppb, which indicates severe discharge in the switchgear. Our NO2 estimation method has great potential for large-scale deployment in low- and medium-voltage switchgears.

Electronic structure analysis of NiO quantum dot-modified jackfruit-shaped ZnO sensors and sensing properties investigation of their highly sensitive and selective for butyl acetate

Detection of flammable, explosive and toxic butyl acetate helps to avoid accidents and protect health in industrial production. However, there are few reports on butyl acetate sensors, especially highly sensitive, low detection limit and highly selective ones. In this work, density functional theory (DFT) analyzes the electronic structure of sensing materials and the adsorption energy of butyl acetate. The effects of Ni element doping, oxygen vacancy constructions, and NiO quantum dot modifications on the modulation of the electronic structure of ZnO and on the adsorption energy of butyl acetate are investigated in detail. Based on the DFT analysis, the NiO quantum dot modified jackfruit-shaped ZnO is synthesized via thermal solvent method reduction. The NiO/ZnO sensor has a response 502.5 for 100 ppm butyl acetate with 100 ppb detection limit, and the response for 100 ppm butyl acetate is at least 6.2 times higher than 100 ppm methanol, 100 ppm benzene, 100 ppm triethylamine, 100 ppm isopropanol, 100 ppm ethyl acetate and 100 ppm formic acid. X-ray photoelectron spectroscopy (XPS) explores the change of oxygen vacancies in sensor accompanied with the addition of Ni element and reveales the reason for the change of oxygen vacancies.

Growth of diazonium-functionalized ZnO nanoflakes on flexible carbon cloth for electrochemical sensing of acetone in the liquid phase

Simple detection of acetone is indispensable due to its health and environmental concerns. Surface-modified electrodes are promising for the detection of acetone. In the present study, the facile fabrication of ZnO nanoflakes on carbon cloth (CC) is reported. The electrode was fabricated by decorating the CC with ZnO nanoparticles (ZnO NPs), followed by the hydrothermal treatment and modification with diazonium salt using linear sweep voltammetry (LSV) forming ZnO nanoflakes (ZnO NFs) on ZnO NPs/CC. The as-prepared ZnO/CC electrode was used for the detection of acetone at room temperature using cyclic voltammetry. Fourier transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) analyses were used for the chemical and physical characterization of the CC before and after each modification step. The obtained data manifested that ZnO NFs functionalized with diazonium salt increased the roughness of the CC surface, which was advantageous to promote the interaction between CC and acetone target. The modified sensing platform showed excellent performance in terms of the wide working range (0.1-2000 ppm) and low detection limit (0.03 ppm), making it a promising and cost-effective sensor of acetone in the liquid phase.

A humidity-resistant and room temperature carbon soot@ZIF-67 composite sensor for acetone vapour detection

Zeolitic imidazolate framework-67 (ZIF-67), carbon nanoparticles (CNPs), and the CNPs@ZIF-67 composite were prepared and used to fabricate sensors for the detection of acetone vapour. The prepared materials were characterized using transmission electron microscopy, powder X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy and Fourier-transform infrared spectroscopy. The sensors were tested using an LCR meter under the resistance parameter. It was found that the ZIF-67 sensor did not respond at room temperature, the CNP sensor had a non-linear response to all analytes, and the CNPs/ZIF-67 sensor had an excellent linear response to acetone vapour and was less sensitive to 3-pentanone, 4-methyl-1-hexene, toluene and cyclohexane vapours. However, it was found that ZIF-67 improves carbon soot sensor sensitivity by 155 times, wherein the sensitivity of the carbon soot sensor and carbon soot@ZIF-67 sensor on acetone vapour was found to be 0.0004 and 0.062 respectively. In addition, the sensor was found to be insensitive to humidity and the limit of detection was 484 ppb at room temperature.

Experimental and density functional study of the light-assisted gas-sensing performance of a TiO2-CoFe2O4 heterojunction

Toluene gas as a solvent is widely present in industrial production and indoor decoration, and can seriously harm human health even at low concentrations. Furthermore, toluene can be used as a typical biomarker for disease diagnosis. Therefore, the detection of toluene gas is very important. Herein, a hydrothermal method was used to successfully prepare a TiO₂-CoFe₂O₄ heterostructure for detecting toluene gas. The ultraviolet (UV)-visible diffuse reflectance spectra and photoluminescence spectra showed that the bandgap of the heterojunction was considerably shorter than those of pure TiO₂ and CoFe₂O₄, and the recombination of electron-hole pairs was inhibited. At the same time, the response value of the TiO₂-CoFe₂O₄ heterojunction was 10.5 for 20 ppm toluene at 219 °C, which was much better than those of pure TiO₂ and CoFe₂O₄. Moreover, its response value further increased under UV irradiation. In addition, density functional theory (DFT) was innovatively employed in this study to explain in detail how the heterojunction and UV irradiation can improve gas sensitivity through the calculation of the material energy band, adsorption energy, etc. This work provides a good reference for the preparation of high-efficiency and high-sensitivity gas sensors.

Metal Oxide Gas Sensors to Study Acetone Detection Considering Their Potential in the Diagnosis of Diabetes: A Review

Metal oxide (MOx) gas sensors have attracted considerable attention from both scientific and practical standpoints. Due to their promising characteristics for detecting toxic gases and volatile organic compounds (VOCs) compared with conventional techniques, these devices are expected to play a key role in home and public security, environmental monitoring, chemical quality control, and medicine in the near future. VOCs (e.g., acetone) are blood-borne and found in exhaled human breath as a result of certain diseases or metabolic disorders. Their measurement is considered a promising tool for noninvasive medical diagnosis, for example in diabetic patients. The conventional method for the detection of acetone vapors as a potential biomarker is based on spectrometry. However, the development of MOx-type sensors has made them increasingly attractive from a medical point of view. The objectives of this review are to assess the state of the art of the main MOx-type sensors in the detection of acetone vapors to propose future perspectives and directions that should be carried out to implement this type of sensor in the field of medicine.

Conductometric ppb-Level CO Sensors Based on In2O3 Nanofibers Co-Modified with Au and Pd Species

Carbon monoxide (CO) is one of the most toxic gases to human life. Therefore, the effective monitoring of it down to ppb level is of great significance. Herein, a series of In₂O₃ nanofibers modified with Au or Pd species or simultaneous Au and Pd species have been prepared by electrospinning combined with a calcination process. The as-obtained samples are applied for the detection of CO. Gas-sensing investigations indicate that 2 at% Au and 2 at% Pd-co-modified In₂O₃ nanofibers exhibit the highest response (21.7) to 100 ppm CO at 180 °C, and the response value is ~8.5 times higher than that of pure In₂O₃ nanofibers. More importantly, the detection limit to CO is about 200 ppb with a response value of 1.23, and is obviously lower than that (6 ppm) of pure In₂O₃ nanofibers. In addition, the sensor also shows good stability within 19 days. These demonstrate that co-modifying In₂O₃ nanofibers with suitable amounts of Pd and Au species might be a meaningful strategy for the development of high-performance carbon monoxide gas sensors.

Application of Metal–Organic Framework in Diagnosis and Treatment of Diabetes

Diabetes-related chronic wounds are often accompanied by a poor wound-healing environment such as high glucose, recurrent infections, and inflammation, and standard wound treatments are fairly limited in their ability to heal these wounds. Metal–organic frameworks (MOFs) have been developed to improve therapeutic outcomes due to their ease of engineering, surface functionalization, and therapeutic properties. In this review, we summarize the different synthesis methods of MOFs and conduct a comprehensive review of the latest research progress of MOFs in the treatment of diabetes and its wounds. State-of-the-art in vivo oral hypoglycemic strategies and the in vitro diagnosis of diabetes are enumerated and different antimicrobial strategies (including physical contact, oxidative stress, photothermal, and related ions or ligands) and provascular strategies for the treatment of diabetic wounds are compared. It focuses on the connections and differences between different applications of MOFs as well as possible directions for improvement. Finally, the potential toxicity of MOFs is also an issue that we cannot ignore.