A Bulk Oxygen Vacancy Dominating WO3-x Photocatalyst for Carbamazepine Degradation.

Creating oxygen vacancy in tungsten trioxide (WO3) has been considered as an effective strategy to improve the photocatalytic performance for degrading organic pollutants. In this study, oxygen vacancies were introduced into WO3 by thermal treatment under Ar atmosphere and their proportion was changed by setting different treatment times. WO3-x samples show better photoelectric properties and photocatalytic degradation performance for carbamazepine (CBZ) than an oxygen-vacancy-free sample, and WO3-x with the optimal proportion of oxygen vacancies is obtained by thermal treatment for 3 h in 550 °C. Furthermore, it discovers that the surface oxygen vacancies on WO3-x would be recovered when it is exposed to air, resulting in a bulk oxygen vacancy dominating WO3-x (bulk-WO3-x). The bulk-WO3-x exhibited much higher degradation efficiency for CBZ than WO3-x with both surface and bulk oxygen vacancies. The mechanism study shows bulk-WO3-x mainly degrades the CBZ by producing OH radicals and superoxide radicals, while oxygen-vacancy-free sample mainly oxidizes the CBZ by the photoexcited hole, which requires the CBZ to be adsorbed on the surface for degradation. The radical generated by bulk-WO3-x exhibits stronger oxidizing capacity by migrating to the solution for CBZ degradation. In summary, the influence of oxygen vacancy on photocatalytic degradation performance depends on both the proportion and location distribution and could lie in the optimization of the photodegradation mechanism. The results of this study could potentially broaden our understanding of the role of oxygen vacancies and provide optimal directions and methods for oxygen vacancy regulation for photocatalysts.

Explainable machine-learning predictions for catalysts in CO2-assisted propane oxidative dehydrogenation.

Propylene is an important raw material in the chemical industry that needs new routes for its production to meet the demand. The CO2-assisted oxidative dehydrogenation of propane (CO2-ODHP) represents an ideal way to produce propylene and uses the greenhouse gas CO2. The design of catalysts with high efficiency is crucial in CO2-ODHP research. Data-driven machine learning is currently of great interest and gaining popularity in the heterogeneous catalysis field for guiding catalyst development. In this study, the reaction results of CO2-ODHP reported in the literature are combined and analyzed with varied machine learning algorithms such as artificial neural network (ANN), k-nearest neighbors (KNN), support vector regression (SVR) and random forest regression (RF)and were used to predict the propylene space-time yield. Specifically, the RF method serves as a superior performing algorithm for propane conversion and propylene selectivity prediction, and SHapley Additive exPlanations (SHAP) based on the Shapley value performs fine model interpretation. Reaction conditions and chemical components show different impacts on catalytic performance. The work provides a valuable perspective for the machine learning in light alkane conversion, and helps us to design catalyst by catalytic performance hidden in the data of literatures.

Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion.

For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.

Detection of the Core Bacteria in Colostrum and Their Association with the Rectal Microbiota and with Milk Composition in Two Dairy Cow Farms.

As one of the pioneer bacterial sources of intestinal microbiota, the information of bacterial composition in colostrum might provide a reference for developing specific probiotics for newborn calves, especially calves fed with pasteurized milk. The present study aimed to detect the core bacteria at different taxonomic levels and the common beneficial ones in colostrum by analyzing the bacterial composition in 34 colostrum samples of healthy cows selected from two dairy farms. The results of the further analysis showed that the bacterial composition in the colostrum of the two dairy farms was different, but their four most dominant phyla were the same including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. The microbiome of all colostrum samples shared ten core operational taxonomic units (OTUs), 21 core genera, and 34 core families, and most of them had no difference in relative abundance between the two farms. The ten core OTUs did not belong to the identified commensal bacteria and have not been detected by previous study. However, several core genera found in our study were also identified as core genus in a previous study. Some well-known beneficial and pathogenic bacteria including Lactobacillus plantarum, Bacillus subtilis, Acinetobacter lwoffii, and Streptococcus pneumoniae were present in the colostrum of healthy cows. However, none had a correlation with the number of somatic cell count (SCC), but the core genera Nubella and Brevundinimas and the core families Methylobacteriaceae and Caulobacteraceae positively correlated with the number of SCC. The genus Staphylococcus, Pseudomonas, and Chryseobacterium in colostrum had a positive correlation with each other, while the probiotics unidentified-Bacteroidales-S24-7-group had a negative correlation with Pseudomonas and Chryseobacterium. In addition, more than 50% bacterial OTUs in colostrum were detected in the rectal content including some strictly anaerobic bacteria that are generally present in the intestine and rumen. However, of the top 30 commonly shared bacterial genera in the colostrum and rectal feces, no genus in colostrum was positively correlated with that same genus in rectal feces. In conclusion, the bacterial composition of colostrum microbiota is greatly influenced by external factors and individuals. There were several core OTUs, and some core genus and families in the colostrum samples. Colostrum from healthy cows contained both beneficial and pathogenic bacteria and shared many common bacteria with rectal content including some gastrointestinal anaerobes.

Fibrous Phase Red Phosphorene as a New Photocatalyst for Carbon Dioxide Reduction and Hydrogen Evolution.

2D photocatalysts are one of the hottest issues in energy and material science. In the field of photocatalysis, a 2D material with an appropriate bandgap of 1.3 to 2.0 eV is desirable. Herein, a new kind of fibrous phase red phosphorene with a bandgap between 1.43 to 1.54 eV is obtained. This is much better than black phosphorus because the bandgap of black P depends of its layer number. The black P needs to be as thin as 1-2 layers for suitable band diagram, which is difficult to control. The fibrous red phosphorene is first used for photocatalytic CO2 reduction, and its activity is superior to the majority of mainstream photocatalysts and reaches a record-high value among phosphorus. Besides, its activity in hydrogen evolution is higher than most of the phosphorus photocatalysts. The intralayer charge transfer is much easier than interlayer transfer. The mobility of electron and hole along the phosphorene plane is about 20 times higher than that perpendicular to different layers. The activity sites is at region between the two P[21] chains. These regions are easy to be exposed for fibrous phase phosphorene, making it to exhibit high activity.

Non-targeted Metabolomic Analysis Based on Ultra-High-Performance Liquid Chromatography Quadrupole Time-of-Flight Tandem Mass Spectrometry Reveals the Effects of Grafting on Non-volatile Metabolites in Fresh Tea Leaves ( Camellia sinensis L.).

To investigate the effects of grafting on non-volatile metabolites in tea, non-targeted metabolomic analyses of fresh leaves were performed on the basis of ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF/MS). One non-grafted YingHong No. 9 and four grafted tea [grafting scion YingHong No. 9 on four different rootstocks, BaiMao No. 2 (BM2), BaiYeDanCong (BY), HeiYeShuiXian (HY), and WuLingHong (WLH)] were chosen as materials. In total, 32 differential metabolites were identified, including phenolic acids, flavan-3-ols, dimeric catechins, flavonol and flavonol/flavone glycosides, etc. Partial least squares discrimination analysis and hierarchical cluster analysis showed various effects of different rootstocks on metabolites. Thereinto, rootstocks of WLH and BY showed extremely outstanding performance in up- and downregulating these metabolites, respectively. Differential metabolites were enriched into three crucial pathways, including biosynthesis of phenylpropanoids, flavonoid biosynthesis, and flavone and flavonol biosynthesis, which might influence the quality of tea. This study provides a theoretical basis for grafting-related variations of non-volatile metabolites in fresh tea leaves.

Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses.

The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.