Gold particles decorated reduced graphene oxide for low level mercury vapor detection with rapid response at room temperature.

Rapid and sensitive detection of mercury vapor is of great significance for environmental protection and human health. But the detection method enabling low detection limitation and rapid response at room temperature simultaneously has rarely been reported. In this work, we propose a gold particles decorated reduced graphene oxide sensor for mercury vapor detection. After adding the gold particles, the reduced graphene oxide sensors' response sensitivity increase by more than 16 times and the response time significantly decreases, which is far less below the results reported by others. The sensor performance improvement should attribute to the distribution of the decorated gold particles, which insert into the layered graphene sheets, as demonstrated by the SEM and XRD results. The increased layer spacing of graphene sheets is conductive to the faster entry/exit of mercury vapor and increases the effective sensing area of graphene. What's more, the first-principles calculation results confirm the mercury-philicity of gold particles, which also contributes to the increased sensitivity. We further test more performance of the gold particles decorated reduced graphene oxide sensor to mercury vapor, which shows a linear response, low detection limit and good repeatability. The proposed sensor shows rapid response/recovery (6/8 s), low detection limit (0.01 ng/mL), linear response, good repeatability and room temperature detection simultaneously, which shows great application potential for mercury vapor detection.

Green Fruit Detection with a Small Dataset under a Similar Color Background Based on the Improved YOLOv5-AT.

Green fruit detection is of great significance for estimating orchard yield and the allocation of water and fertilizer. However, due to the similar colors of green fruit and the background of images, the complexity of backgrounds and the difficulty in collecting green fruit datasets, there is currently no accurate and convenient green fruit detection method available for small datasets. The YOLO object detection model, a representative of the single-stage detection framework, has the advantages of a flexible structure, fast inference speed and excellent versatility. In this study, we proposed a model based on the improved YOLOv5 model that combined data augmentation methods to detect green fruit in a small dataset with a background of similar color. In the improved YOLOv5 model (YOLOv5-AT), a Conv-AT block and SA and CA blocks were designed to construct feature information from different perspectives and improve the accuracy by conveying local key information to the deeper layer. The proposed method was applied to green oranges, green tomatoes and green persimmons, and the mAPs were higher than those of other YOLO object detection models, reaching 84.6%, 98.0% and 85.1%, respectively. Furthermore, taking green oranges as an example, a mAP of 82.2% was obtained on the basis of retaining 50% of the original dataset (163 images), which was only 2.4% lower than that obtained when using 100% of the dataset (326 images) for training. Thus, the YOLOv5-AT model combined with data augmentation methods can effectively achieve accurate detection in small green fruit datasets under a similar color background. These research results could provide supportive data for improving the efficiency of agricultural production.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation.

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Robustness of calibration model for prediction of lignin content in different batches of snow pears based on NIR spectroscopy.

Snow pear is very popular in southwest China thanks to its fruit texture and potential medicinal value. Lignin content (LC) plays a direct and negative role (higher concentration and larger size of stone cells lead to thicker pulp and deterioration of the taste) in determining the fruit texture of snow pears as well as consumer purchasing decisions of fresh pears. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using a portable near-infrared (NIR) spectrometer, with the range of 1033-2300 nm. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods (batch A, B, C and D) were collected. We developed a standard normal variate transformation (SNV)-genetic algorithm (GA) -the partial least square regression (PLSR) model (master model A) - to predict LC in batch A of snow pear samples based on 80 selected effective wavelengths, with a higher correlation coefficient of prediction set (Rp) of 0.854 and a lower root mean square error of prediction set (RMSEP) of 0.624, which we used as the prediction model to detect LC in three other batches of snow pear samples. The performance of detecting the LC of batch B, C, and D samples by the master model A directly was poor, with lower Rp and higher RMSEP. The independent semi-supervision free parameter model enhancement (SS-FPME) method and the sequential SS-FPME method were used and compared to update master model A to predict the LC of snow pears. For the batch B samples, the predictive ability of the updated model (Ind-model AB) was improved, with an Rp of 0.837 and an RMSEP of 0.614. For the batch C samples, the performance of the Seq-model ABC was improved greatly, with an Rp of 0.952 and an RMSEP of 0.383. For the batch D samples, the performance of the Seq-model ABCD was also improved, with an Rp of 0.831 and an RMSEP of 0.309. Therefore, the updated model based on supervision and learning of new batch samples by the sequential SS-FPME method could improve the robustness and migration ability of the model used to detect the LC of snow pears and provide technical support for the development and practical application of portable detection device.

Detection of heavy metal ions using laser-induced breakdown spectroscopy combined with filter paper modified with PtAg bimetallic nanoparticles.

The rapid and sensitive detection of heavy metal ions is important for environment and human health. Hence, the rapid and sensitive detection of multiple heavy metals simultaneously has become a critical issue. Here, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with PtAg bimetallic nanoparticles (BNPs) (LIBS-FP-PtAgBNPs) for the ultrasensitive detection of Hg2+, Cr3+, and Pb2+. The PtAgBNPs-modified filter paper was used to efficiently and specifically adsorb Hg, Cr, and Pb, and LIBS was used to detect the Hg, Cr, and Pb simultaneously. The limits of detection for Hg, Cr, and Pb were 0.5 µg/L (2.5 nM), 8 µg/L (0.15 µM), and 2 µg/L (9 nM), respectively. Furthermore, this method was successfully applied to determine the concentrations of Hg, Cr, and Pb in real spiked water samples. Compared with other methods based on nanoparticle sensing, LIBS-FP-PtAgBNPs is simpler to use and can achieve highly efficient enrichment, rapid separation, and sensitive detection of heavy metal ions. The optimal detections of Hg, Cr, and Pb were achieved in the pH range of 1-6. The developed method provides a new avenue to realize the rapid and sensitive detection of trace heavy metals in the environment.

Detection of cadmium in soils using laser-induced breakdown spectroscopy combined with spatial confinement and resin enrichment.

The determination of heavy metals in soils is of great significance for the monitoring and control of environmental pollution. However, it is hard to realize fast and in situ measurements. Laser-induced breakdown spectroscopy (LIBS) is an effective method for element detection in soils, but its detection limit cannot meet the requirements of the control of soil pollution. In addition, it usually suffers splash problems and needs complex pretreatment processes before measurement. In this study, we developed a new method for the determination of cadmium in soils using LIBS. We improved the sensitivity of common LIBS, while avoiding splash problems and without complex pretreatment processes. The LIBS signal is enhanced in two ways. Firstly, the heavy metals were enriched by the cation exchange resins. And then, the LIBS signal levels were further enhanced by a sample container with spatial confinement. During this process, the soil only needs to be treated with water to achieve slurry status, rather than any complex pretreatments. We demonstrated that the detection limit for cadmium in soils is 0.132 mg kg-1 using this method.