Ultrasensitive Bio-H2S Gas Sensor Based on Cu2O-MWCNT Heterostructures.

Developing a respiratory analysis disease diagnosis platform for the H2S biomarker has great significance for the real-time detection of various diseases. However, achieving highly sensitive and rapid detection of H2S gas at the parts per billion level at low temperatures is one of the most critical challenges for developing portable exhaled gas sensors. Herein, Cu2O-multiwalled carbon nanotube (MWCNT) heterostructures with excellent gas sensitivity to H2S at room temperature and a lower temperature were successfully synthesized by a facile two-dimensional (2D) electrodeposition in situ assembly method. The combination of Cu2O and MWCNTs via the principle of optimal conductance growth not only reduced the initial resistance of the material but also provided an ideal interfacial barrier structure. Compared to the response of the pure Cu2O sensor, that of the Cu2O-MWCNT sensor to 1 ppm of H2S increased nearly 800 times at room temperature, and the response time decreased by more than 500 s. In addition to the excellent sensitivity with detection limits as low as 1 ppb, the Cu2O-MWCNT sensor was extremely selective with low-temperature adaptability. The sensor had a response value of 80.6 to 0.1 ppm of H2S at -10 °C, which is difficult to achieve with sensors based on oxygen adsorption/desorption mechanisms. The sensor was used for the detection of real oral exhaled breath, confirming its feasibility as a real-time disease monitoring sensor. The Cu2O-MWCNT heterostructures maximized the advantages of the individual components and laid the experimental foundation for future applications of highly sensitive portable breath analysis platforms for monitoring H2S.

Cu2O/Co3O4 nanoarrays for rapid quantitative analysis of hydrogen sulfide in blood.

2D heterostructure nanoarrays have emerged as a promising sensing material for rapid disease detection applications. In this study, a bio-H2S sensor based on Cu2O/Co3O4 nanoarrays was proposed, the controllable preparation of the nanoarrays being achieved by exploring the experimental parameters of the 2D electrodeposition in situ assembly process. The nanoarrays were designed as a multi-barrier system with strict periodicity and long-range order. Based on the interfacial conductance modulation and vulcanization reaction of Cu2O and Co3O4, the sensor exhibited superior sensitivity, selectivity, and stability to H2S in human blood. In addition, the sensor exhibited a reasonable response to 0.1 µmol L-1 Na2S solution, indicating that it had a low detection limit for practical applications. Moreover, first-principles calculations were performed to study changes in the heterointerface during the sensing process and the mechanism of rapid response of the sensor. This work demonstrated the reliability of Cu2O/Co3O4 nanoarrays applied in portable sensors for the rapid detection of bio-H2S.

Magnetic-Field-Enhanced H2S Sensitivity of Cu2O/NiO Heterostructure Ordered Nanoarrays.

Magnetism is a promising external intervention for gas sensitivity based on a heterogeneous interfacial structure caused by the regulation of the heterogeneous interface conductivity and the surface oxygen adsorption. In this study, Cu2O/NiO heterostructure-ordered nanoarrays were prepared with a two-dimensional (2D) electrodeposition in situ assembly method for H2S gas detection at room temperature under the action of a magnetic field. The nanoarrays were multibarrier structures with a strictly periodic structure that was greater than hundreds of microns in size. The experimental data confirmed that the response of 50 ppm of H2S based on the nanoarrays was improved by nearly 61% with a relatively weak magnetic field. Particularly at a low concentration (≤20 ppm), the effect of the magnetic field enhancement on the sensitivity was more obvious. We attributed the enhancement of the gas sensitivity with the magnetic field to the regulation of the Cu2O-NiO interface conductance and the surface oxygen adsorption. This study demonstrated that a magnetic field could significantly enhance the gas sensitivity based on heterostructures. Results of this study provide an important reference for the application of magnetism in gas detection and the design of new gas-sensitive materials.

H2S detection at low temperatures by Cu2O/Fe2O3 heterostructure ordered array sensors.

2D heterostructures are promising gas sensor materials due to their surface/interface effects and hybrid properties. In this research, Cu2O/Fe2O3 heterostructure ordered arrays were synthesized using an in situ electrodeposition method for H2S detection at low temperatures. These arrays possess a periodic long range ordered structure with horizontal multi-heterointerfaces, leading to superior gas sensitivity for synergistic effects at the heterointerfaces. The sensor based on the Cu2O/Fe2O3 heterostructure ordered arrays exhibits a dramatic improvement in H2S detection at low temperatures (even as low as -15 °C). The response is particularly significant at room and human body temperatures since the conductivity of the arrays can change by up to three orders of magnitude in a 10 ppm H2S atmosphere. These good performances are also attributed to the formation of metallic Cu2S conducting channels. Our results imply that the Cu2O/Fe2O3 heterostructure ordered arrays are promising candidates for high-performance H2S gas sensors that function at low temperatures as well as breath analysis systems for disease diagnosis.

MRI and PET image fusion using the nonparametric density model and the theory of variable-weight.

Medical image fusion is important in the field of clinical diagnosis because it can improve the availability of information contained in images. Magnetic Resonance Imaging (MRI) provides excellent anatomical details as well as functional information on regional changes in physiology, hemodynamics, and tissue composition. In contrast, although the spatial resolution of Positron Emission Tomography (PET) provides is lower than that an MRI, PET is capable of depicting the tissue's molecular and pathological activities that are not available from MRI. Fusion of MRI and PET may allow us to combine the advantages of both imaging modalities and achieve more precise localization and characterization of abnormalities. Previous image fusion algorithms, based on the estimation theory, assume that all distortions follow Gaussian distribution and are therefore susceptible to the model mismatch problem. To overcome this mismatch problem, we propose a new image fusion method with multi-resolution and nonparametric density models (MRNDM). The RGB space registered from the source multi-modal medical images is first transformed into a generalized intensity-hue-saturation space (GIHS), and then is decomposed into the low- and high-frequency components using the non-subsampled contourlet transform (NSCT). Two different fusion rules, which are based on the nonparametric density model and the theory of variable-weight, are developed and used to fuse low- and high-frequency coefficients. The fused images are constructed by performing the inverse of the NSCT operation with all composite coefficients. Our experimental results demonstrate that the quality of images fused from PET and MRI brain images using our proposed method MRNDM is higher than that of those fused using six previous fusion methods.