A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms.

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

Preparation and Gas Sensing Properties of Hair-Based Carbon Sheets.

Waste human hair was carbonized into carbon sheets by a simple carbonization method, which was studied as gas sensing materials for the first time. The effect of carbonization temperature on the structure and gas sensing properties of hair-based carbon sheet was studied by scanning electron microscope, X-ray diffraction, infrared spectrum, Raman spectrum, and gas-sensitive tester. The results showed that the carbonization temperature had a significant effect on the structure and gas sensing performance of carbon sheets, which were doped with K, N, P, and S elements during carbonization. However, the sensor of the carbon sheet does not show good selectivity among six target gases. Fortunately, the carbon sheets prepared at different temperatures have different responses to the target gases. The sensor array constructed by the carbon sheets prepared at different temperatures can realize the discriminative detection of a variety of target gases. For the optimized carbon sheet, the theoretical limit of detection of hydrogen peroxide is 0.83 ppm. This work provides a reference for the resource utilization of waste protein and the development of gas sensors.

Non-invasive SERS serum detection technology combined with multivariate statistical algorithm for simultaneous screening of cervical cancer and breast cancer.

Surface-enhanced Raman scattering (SERS), as a rapid, reliable and non-destructive spectral detection technology, has made a series of breakthrough achievements in screening and pre-diagnosis of various cancerous tumors. In this paper, high-performance gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) active SERS substrates were specially designed for serum testing, and realized highly sensitive detection of serum from healthy people, patients with cervical cancer and breast cancer. Based on the SERS spectra of the three groups of serum, the significant differences between the healthy group and cancer group at 1030 cm-1 and 1051 cm-1 were analyzed, and the similar but different serum SERS spectra of cervical cancer and breast cancer patients were compared. In addition, the spectral difference detected by SERS technology combined with a multivariate statistical algorithm was used to distinguish three kinds of serum. The serum SERS spectral sensitive bands were extracted by recursive weighted partial least squares (rPLS), and the three classification diagnosis models were established by combining orthogonal partial least squares discriminant analysis (OPLS-DA), linear discriminant analysis (LDA) and principal component analysis support vector machine (PCA-SVM) for synchronous classification and discrimination of the three groups of serum. The diagnostic results showed that the overall screening accuracy of three models were 93.28%, 97.77% and 94.78%, respectively. These above results confirmed that the Au NPs/785 PSi PhCs can realize super-sensitive detection of serum, and the established diagnostic model has great potential for pre-diagnosis and simultaneous screening of cervical cancer and breast cancer.

Establishment of a reliable scheme for obtaining highly stable SERS signal of biological serum.

As a rapid and non-destructive biological serum detection method, SERS technology was widely used in the screening and medical diagnosis of various diseases by combining the analysis of serum SERS spectrum and multivariate statistical algorithm. Because of the high complexity of serum components and the variability of SERS spectra, which often resulted in the phenomenon that the SERS spectrum of the same biological serum was significantly different due to the different test conditions. In this experiment, through the dilution treatment of the serum and the systematic test of the serum of all concentration gradients with lasers of wavelength of 785, 633 and 532 nm, the most suitable conditions for detecting the serum were investigated. The experimental results showed that only when the serum is diluted to low concentration (10 ppm), the SERS spectrum with high reproducibility and stability could be obtained, furthermore, the low concentration serum had weak tolerance to laser, and 532 nm laser was not suitable for serum detection. In this paper, a set of test scheme for obtaining highly stable serum SERS spectra was established by using high-performance gold nanoparticles (Au NPs) as the active substrate of SERS. Through comparative analysis of SERS spectrum of serum of normal people and cervical cancer, the reliability of the established low-concentration serum test program was verified, as well as its great potential advantages in disease screening and diagnosis.

Enhanced gas sensing performance of perovskite YFe1-x Mn x O3 by doping manganese ions.

Perovskite YFe1-x Mn x O3 with a hierarchical structure were prepared by a simple hydrothermal method and used as gas sensing materials. The structure, morphology and composition of YFe1-x Mn x O3 were investigated using X-ray diffraction, transmission electron microscopy, scanning electron microscopy and X-ray photoelectron spectroscopy. The gas sensing test showed that all YFe1-x Mn x O3 perovskites with different Mn doping concentrations displayed fast response and recovery characteristics to multiple analytes as well as good stability and recoverability. With the increase of Mn doping concentration, the response of YFe1-x Mn x O3 to four kinds of target atmospheres first increases, then decreases. The sensing performance of YFe1-x Mn x O3 is best when x = 0.05. Compared with pure YFeO3, the responses of YFe0.95Mn0.05O3 to 1000 ppm of CH2O, C2H6O, H2O2 and 100% relative humidity were increased by 835%, 1462%, 812% and 801%, respectively. The theoretical detection limit of YFe0.95Mn0.05O3 for H2O2 and CH2O is 1.75 and 2.55 ppb, respectively. Furthermore, the possibility of buildings a sensor array based on YFe1-x Mn x O3 with different doping concentrations was evaluated by principal component analysis and radar chart analysis. It is feasible to realize the visual and discriminative detection of the target analyte by constructing sensor arrays through radar chart analysis and database construction.

High-Performance Gas Sensor of Polyaniline/Carbon Nanotube Composites Promoted by Interface Engineering.

Inspired by the enhanced gas-sensing performance by the one-dimensional hierarchical structure, one-dimensional hierarchical polyaniline/multi-walled carbon nanotubes (PANI/CNT) fibers were prepared. Interestingly, the simple heating changed the sensing characteristics of PANI from p-type to n-type and n-type PANI and p-type CNTs form p-n hetero junctions at the core-shell interface of hierarchical PANI/CNT composites. The p-type PANI/CNT (p-PANI/CNT) and n-type PANI/CNT (n-PANI/CNT) performed the higher sensitivity to NO2 and NH3, respectively. The response times of p-PANI/CNT and n-PANI/CNT to 50 ppm of NO2 and NH3 are only 5.2 and 1.8 s, respectively, showing the real-time response. The estimated limit of detection for NO2 and NH3 is as low as to 16.7 and 6.4 ppb, respectively. After three months, the responses of p-PANI/CNT and n-PANI/CNT decreased by 19.1% and 11.3%, respectively. It was found that one-dimensional hierarchical structures and the deeper charge depletion layer enhanced by structural changes of PANI contributed to the sensitive and fast responses to NH3 and NO2. The formation process of the hierarchical PANI/CNT fibers, p-n transition, and the enhanced gas-sensing performance were systematically analyzed. This work also predicts the development prospects of cost-effective, high-performance PANI/CNT-based sensors.

Porous Silicon Photonic Crystals Coated with Ag Nanoparticles as Efficient Substrates for Detecting Trace Explosives Using SERS.

Picric acid (PA) is an organic substance widely used in industry and military, which poses a great threat to the environment and security due to its unstable, toxic, and explosive properties. Trace detection of PA is also a challenging task because of its highly acidic and anionic character. In this work, silver nanoparticles (AgNPs)-decorated porous silicon photonic crystals (PS PCs) were controllably prepared as surface-enhanced Raman scattering (SERS) substrates using the immersion plating solution. PA and Rhodamine 6G dye (R6G) were used as the analyte to explore the detection performance. As compared with single layer porous silicon, the enhancement factor of PS PCs substrates is increased to 3.58 times at the concentration of 10-6 mol/L (R6G). This additional enhancement was greatly beneficial to the trace-amount-detection of target molecules. Under the optimized assay condition, the platform shows a distinguished sensitivity with the limit of detection of PA as low as 10-8 mol/L, the linear range from 10-4 to 10-7 mol/L, and a decent reproducibility with a relative standard deviation (RSD) of ca. 8%. These results show that the AgNPs-modified PS PCs substrates could also find further applications in biomedical and environmental sensing.

Ni-Doped ZnS Nanospheres Decorated with Au Nanoparticles for Highly Improved Gas Sensor Performance.

Novel Ni-doped wurtzite ZnS nanospheres decorated with Au nanoparticles (Au NPs⁻ZnS NSs) have been successfully fabricated using a simple method involving vacuum evaporation followed by an annealing process. This transition metal-doped gas sensor had high responsivity, extremely fast response and recovery time, and excellent selectivity to formaldehyde at room temperature. The response and recovery time are only 29 s and 2 s, respectively. Since ZnS is transformed into ZnO at a high temperature, superior room temperature-sensing performance can improve the stability and service life of the sensor. The improvement in sensing performance could be attributed to the reduced charge-transfer distance resulting from the creation of a local charge reservoir layer, and the catalytic and spillover effect of Au nanoparticles. The rough and porous spherical structure can also facilitate the detection and diffusion of gases. The as-prepared Au NPs⁻ZnS NSs are considered to be an extremely promising candidate material for gas sensors, and are expected to have other potential applications in the future.

Multichannel Discriminative Detection of Explosive Vapors with an Array of Nanofibrous Membranes Loaded with Quantum Dots.

The multichannel fluorescent sensor array based on nanofibrous membranes loaded with ZnS quantum dots (QDs) was created and demonstrated for the discriminative detection of explosives. The synergistic effect of the high surface-to-volume ratio of QDs, the good permeability of nanofibrous membranes and the differential response introduced by surface ligands was played by constructing the sensing array using nanofibrous membranes loaded with ZnS QDs featuring several surface ligands. Interestingly, although the fluorescence quenching of the nanofibrous membranes is not linearly related to the exposure time, the fingerprint of each explosive at different times is very similar in shape, and the fingerprints of the three explosives show different shapes. Three saturated vapors of nitroaromatic explosives could be reliably detected and discriminated by the array at room temperature. This work is the first step toward devising a monitoring system for explosives in the field of public security and defense. It could, for example, be coupled with the technology of image recognition and large data analysis for a rapid diagnostic test of explosives. This work further highlights the power of differential, multichannel arrays for the rapid and discriminative detection of a wide range of chemicals.

Hybridization assay of insect antifreezing protein gene by novel multilayered porous silicon nucleic acid biosensor.

A fabrication of a novel simple porous silicon polybasic photonic crystal with symmetrical structure has been reported as a nucleic acid biosensor for detecting antifreeze protein gene in insects (Microdera puntipennis dzhungarica), which would be helpful in the development of some new transgenic plants with tolerance of freezing stress. Compared to various porous silicon-based photonic configurations, porous silicon polytype layered structure is quite easy to prepare and shows more stability; moreover, polybasic photonic crystals with symmetrical structure exhibit interesting optical properties with a sharp resonance in the reflectance spectrum, giving a higher Q factor which causes higher sensitivity for sensing performance. In this experiment, DNA oligonucleotides were immobilized into the porous silicon pores using a standard crosslink chemistry method. The porous silicon polybasic symmetrical structure sensor possesses high specificity in performing controlled experiments with non-complementary DNA. The detection limit was found to be 21.3nM for DNA oligonucleotides. The fabricated multilayered porous silicon-based DNA biosensor has potential commercial applications in clinical chemistry for determination of an antifreeze protein gene or other genes.