Enhanced electrocatalytic oxygen reduction reaction from organic-inorganic heterostructure

Herein, molybdenum disulfide nanoflakes decorated copper phthalocyanine microrods (CuPc-MoS₂) are synthesized via two step simple hydrothermal method. The as synthesized hybrid along with pure molybdenum disulfide (MoS₂) nanoflower and pure copper phthalocyanine (CuPc) microrods are well characterized by various techniques that confirm phase, morphology, elemental compositions etc. Next, electrocatalytic oxygen reduction reaction towards fuel cell is investigated in alkaline medium and obtained results proclaim that our CuPc-MoS₂ heterostructure outperforms the other two constituent materials. Efficient oxygen reduction is achieved following four electron pathway by CuPc-MoS₂ whereas partial reduction is done through two electron process by CuPc and MoS₂ separately. Long-time durability test reveals almost 97.6% retention after 8000s that eventually dictate us that CuPc-MoS₂ heterostructure can be the efficient cathode electrocatalyst for future generation fuel cell.     

Preparation,structural and photocatalytic activity of Sn/Fe co-doped TiO2 nanoparticles by sol-gel method

Pure and Sn/Fe co-doped (0.2 at.% Sn and 0.6 at.% Fe, 0.6 at.% Sn and 0.2 at.% Fe, 1.0 at.% Sn and 1.0 at.% Fe) TiO₂ nanoparticles were synthesized via a sol-gel method and subsequently calcined at different temperatures. Furthermore, the particles were analyzed by TG-DSC, XRD, TEM, HRTEM, EDS, SAED and UV–Vis for investigating the influences of dopant and calcination temperature on the thermal effect, composition, morphology, energy band gap (E_g) and the degradation efficiency of methyl orange (MO) under various light irradiations respectively. Results indicated that Sn/Fe co-doping inhibited the crystallization transformation from anatase to rutile phase of TiO₂ and decreased the E_g. The increased calcination temperature and Sn/Fe co-doped effect brought about the abnormal grain growth of TiO₂ nanoparticles. 0.6 at.% Sn/0.2 at.% Fe and 1.0 at.% Sn/1.0 at.% Fe co-doped TiO₂ nanoparticles presented better photocatalytic performance than pure and 0.2 at.% Sn/0.6 at.% Fe co-doped TiO₂ nanoparticles under visible light irradiation mainly due to the decreased E_g. On the contrary, 0.2 at.% Sn and 0.6 at.% Fe co-doped TiO₂ nanoparticles calcined at 650 °C showed the most excellent photocatalytic performance under UV light irradiation, which was about twice as large as that of pure TiO₂ possibly due to the formed hybrid structure of anatase and rutile phase as well as the h⁺-mediated decomposition pathway.     

Antibacterial activity and action target of phenyllactic acid against Staphylococcus aureus and its application in skim milk and cheese

Phenyllactic acid (PLA) has been demonstrated to possess antibacterial activity and capacity to prolong food shelf life. However, studies on the performance of PLA in inhibiting Staphylococcus aureus and its effectiveness when applied to dairy products are largely lacking. Here, antibacterial activity (planktonic and biofilm states) of PLA against S. aureus CICC10145 (S. aureus_45) were investigated. The results showed that PLA inhibited growth of S. aureus_45 and formation of S. aureus_45 biofilm. Next, the antibacterial action target of PLA was uncovered from both physiological and phenotypic perspectives. The results showed that PLA decreased cell metabolic activity and cell viability, damaged cell membrane integrity, triggered leakage of intracellular contents (DNA, proteins, and ATP), and caused oxidative stress damage and morphological deformation of S. aureus_45. In practical application, the antibacterial activity of PLA against S. aureus_45 cells was further confirmed in skim milk and cheese as dairy food models, and the antibacterial effects can be adequately maintained during storage for 21 d, at least at 4°C. These findings suggested that PLA could be a potential candidate for controlling S. aureus outgrowth in dairy foods.     

Combinations of Piperine with Hydroxypropyl-β-Cyclodextrin as a Multifunctional System

Piperine is an alkaloid that has extensive pharmacological activity and impacts other active substances bioavailability due to inhibition of CYP450 enzymes, stimulation of amino acid transporters and P-glycoprotein inhibition. Low solubility and the associated low bioavailability of piperine limit its potential. The combination of piperine with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) causes a significant increase in its solubility and, consequently, an increase in permeability through gastrointestinal tract membranes and the blood–brain barrier. X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR) were used to characterize interactions between piperine and HP-β-CD. The observed physicochemical changes should be combined with the process of piperine and CD system formation. Importantly, with an increase in solubility and permeability of piperine as a result of interaction with CD, it was proven to maintain its biological activity concerning the antioxidant potential (2,2-diphenyl-1-picryl-hydrazyl-hydrate assay), inhibition of enzymes essential for the inflammatory process and for neurodegenerative changes (hyaluronidase, acetylcholinesterase, butyrylcholinesterase).     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Effects of non-thermal plasma treating wheat kernel on the physicochemical properties of wheat flour and the quality of fresh wet noodles

The effects of non-thermal plasma (NTP) on the physicochemical properties of wheat flour and the quality of fresh wet noodles ( FWN) were investigated. The results showed that NTP effectively decreased the total plate count (TPC), yeast and mould count (YMC) and Bacillus spp. in wheat flour. Wet gluten contents and the stability time reached the maximum when treated for 20 s. The viscosity of starch increased significantly after treatment due to the increased of damaged starch. The contents of secondary structure were altered to some extent, which was because that the ordered network structure of gluten protein broken. Furthermore, compared with the control, texture properties of FWN were enhanced significantly at 20 s, and the darkening rate of FWN was greatly inhibited due to the low polyphenol oxidase (PPO) activity. Consequently, the most suitable treatment was 500 W for 20 s, providing a basis for the application of NTP in flour products.     

Fabrication of reduced Graphene Oxide supported Gd3+ doped V2O5 nanorod arrays for superior photocatalytic and antibacterial activities

In the current report, pure V₂O₅, a series of Gd doped V₂O₅ (1 wt%, 3 wt%, 5 wt% and 10 wt%) and graphene integrated Gd–V₂O₅ photocatalysts have been prepared using a facile wet chemical approach. The effect of Gd⁺³ ions substitution and RGO support on V₂O₅ was studied by the different analytical techniques. X-ray diffraction (XRD) results showed the orthorhombic crystal structure of synthesized samples with crystallize size in range of 22–35 nm. Morphological analysis showed nanorods and nanorod arrays like appearance of V₂O₅, Gd–V₂O₅ and GdV-₂O₅/RGO, respectively. Gd–V₂O₅ and Gd–V₂O₅/RGO exhibited enhanced optical response in the visible region along with decrease in the band gap values for Gd doped V₂O₅ samples. BET surface area of Gd–V₂O₅ and Gd- V₂O₅/RGO was calculated as 12.39 g/m² and 15.35 g/m² that was found to be higher than pristine V₂O₅. To study the photocatalytic activity of synthesized photocatalysts, methylene blue (MB) was chosen as model pollutant. Among the Gd doped V₂O₅ samples, highest photocatalytic activity (45.62%) was achieved by optimal concentration of 5 wt% Gd–V₂O₅ that is accredited to effective separation of electron-hole pairs. While Gd–V₂O₅/RGO showed 2.1 times higher dye removal (97.12%) than unsupported Gd–V₂O₅, under the visible light irradiation. The significantly high photocatalytic activity of Gd–V₂O₅/RGO is due to the synergistic effect aroused by combined action of Gd⁺³ ions doping and advantageous properties of highly conductive and large surfaced graphene. Recycling experiments for V₂O₅ derivatives showed good stability and recyclability of photocatalysts. Additionally, Gd–V₂O₅/RGO was found to be more potential anti-bacterial agent than V₂O₅ and Gd–V₂O₅.