Construction of ternary hybrid layered reduced graphene oxide supported g-C3N4-TiO2 nanocomposite and its photocatalytic hydrogen production activity

Reduced graphene oxide (rGO) supported g-C₃N₄-TiO₂ ternary hybrid layered photocatalyst was prepared via ultrasound assisted simple wet impregnation method with different mass ratios of g-C₃N₄ to TiO₂. The synthesized composite was investigated by various characterization techniques, such as XRD, FTIR, Raman Spectra, FE-SEM, HR-TEM, UV vis DRS Spectra, XPS Spectra and PL Spectra. The optical band gap of g-C₃N₄-TiO₂/rGO nanocomposite was found to be red shifted to 2.56 eV from 2.70 eV for bare g-C₃N₄. It was found that g-C₃N₄ and TiO₂ in a mass ratio of 70:30 in the g-C₃N₄-TiO₂/rGO nanocomposite, exhibits the highest hydrogen production activity of 23,143 μmol g^?1h^?1 through photocatalytic water splitting. The observed hydrogen production rate from glycerol-water mixture using g-C₃N₄-TiO₂/rGO was found to be 78 and 2.5 times higher than g-C₃N₄ (296 μmol g^?1 h^?1) and TiO₂ (11,954 μmol g^?1 h^?1), respectively. A direct contact between TiO₂ and rGO in the g-C₃N₄-TiO₂/rGO nanocomposite produces an additional 10,500 μmol g^?1h^?1 of hydrogen in 4 h of photocatalytic reaction than the direct contact between g-C₃N₄ and rGO. The enhanced photocatalytic hydrogen production activity of the resultant nanocomposite can be ascribed to the increased visible light absorption and an effective separation of photogenerated electron-hole pairs at the interface of g-C₃N₄-TiO₂/rGO nanocomposite. The effective separation and transportation of photogenerated charge carriers in the presence of rGO sheet was further confirmed by a significant quenching of photoluminescence intensity of the g-C₃N₄-TiO₂/rGO nanocomposite. The photocatalytic hydrogen production rate reported in this work is significantly higher than the previously reported work on g-C₃N₄ and TiO₂ based photocatalysts.     

CO2 capture using membrane contactors: a systematic literature review

With fossil fuel being the major source of energy, CO₂ emission levels need to be reduced to a minimal amount namely from anthropogenic sources. Energy consumption is expected to rise by 48% in the next 30 years, and global warming is becoming an alarming issue which needs to be addressed on a thorough technical basis. Nonetheless, exploring CO₂ capture using membrane contactor technology has shown great potential to be applied and utilised by industry to deal with post- and pre-combustion of CO₂. A systematic review of the literature has been conducted to analyse and assess CO₂ removal using membrane contactors for capturing techniques in industrial processes. The review began with a total of 2650 papers, which were obtained from three major databases, and then were excluded down to a final number of 525 papers following a defined set of criteria. The results showed that the use of hollow fibre membranes have demonstrated popularity, as well as the use of amine solvents for CO₂ removal. This current systematic review in CO₂ removal and capture is an important milestone in the synthesis of up to date research with the potential to serve as a benchmark databank for further research in similar areas of work. This study provides the first systematic enquiry in the evidence to research further sustainable methods to capture and separate CO₂.     

A shock tube study of pyrolysis of tetrahydrothiophene at elevated temperatures

A single pulse shock tube has been used to study the pyrolysis of a hydrogenated sulphur compound, tetrahydrothiophene over the temperature range 1686–1885 K and pressures between 2.4 and 3.5 bars. Product yield and composition was determined using capillary column gas‐chromatography with flame ionization detection and flame photometric sulphur selective detection. The principal hydrocarbon products at all temperatures were C₂H₄ and C₂H₂. Other hydrocarbon reaction products were CH₄, C₂H₆, C₃H₄, C₃H₆, C₄H₃, C₄H₆, C₄H₁₀, C₄H₄, C₆H₆, C₄H₂ and some traces of C₅ and C₆H₅ species. The sulphur compounds identified were hydrogen sulphide, carbon disulphide, thiophene and traces of ethyl mercapton. The pyrolysis experiments indicated that at lower temperatures the hydrogenated thiophene molecule reacts in two unimolecular channels to form C₂H₄+(CH₂)2–S in the major faster channel which may be the route for other products. However, a second lower route may be the formation of C₃H₆+CH₂S. The rate constant obtained for tetrahydrothiophene pyrolysis calculated for this study was k_dis(C4H8S)=1.26×10¹³ exp (316.9 kJ mol^?1) s^?1. Copyright © 2004 John Wiley & Sons, Ltd.     

Core competence for sustainable competitive advantage: a structuredmethodology for identifying core competence

Core competencies are the crown jewels of a company and, therefore, should be carefully nurtured and developed. Companies can determine their future business directions based on the strengths of competencies. However, because generalized terms such as resource, asset, capability, and competence are not clearly explained in connection with competence theory, these posing difficulties in understanding many contemporary management concepts. In this paper, the authors provide a summary of the recent management theories by comparing their salient features. They then propose a linking mechanism between assets, resources, capabilities, competencies, and core competencies. They provide a methodology to identify core competencies by isolating unique and flexible capabilities of the firm. They use this framework to identify the core competencies of a UK manufacturing company. The results of their analyses are used to help the company to make more informed strategic management decisions regarding capability development, outsourcing, focusing, or diversification, with regards to new products, services, or markets. The framework is generic in nature and is applicable to benchmark a manufacturing, public, or service sector organization     

Image-Based Automatic Energy Meter Reading Using Deep Learning

We propose to perform an image-based framework for electrical energy meter reading. Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units. Combining the readings of serial numbers and energy meter units, an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup. However, such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display, orientation and scale variations caused by camera positioning, and non-uniform illumination caused by shades. To this end, our work evaluates and compares the state-of-the art deep learning algorithm You Only Look Once (YOLO ) along with traditional handcrafted features for text extraction and recognition. Our image dataset contains 10,000 images of electrical energy meters and is further expanded by data augmentation such as in-plane rotation and scaling to make the deep learning algorithms robust to these image variations. For training and evaluation, the image dataset is annotated to produce the ground truth of all the images. Consequently, YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98% for all the digits. It proves to be robust against the mentioned image variations compared with the traditional handcrafted features. Our proposed method can be highly instrumental in reducing the time and effort involved in the current meter reading, where workers visit door to door, take images of meters and manually extract readings from these images.     

Bioprinting Via a Dual-Gel Bioink Based on Poly(Vinyl Alcohol) and Solubilized Extracellular Matrix towards Cartilage Engineering

Various hydrogel systems have been developed as biomaterial inks for bioprinting, including natural and synthetic polymers. However, the available biomaterial inks, which allow printability, cell viability, and user-defined customization, remains limited. Incorporation of biological extracellular matrix materials into tunable synthetic polymers can merge the benefits of both systems towards versatile materials for biofabrication. The aim of this study was to develop novel, cell compatible dual-component biomaterial inks and bioinks based on poly(vinyl alcohol) (PVA) and solubilized decellularized cartilage matrix (SDCM) hydrogels that can be utilized for cartilage bioprinting. In a first approach, PVA was modified with amine groups (PVA-A), and mixed with SDCM. The printability of the PVA-A/SDCM formulations cross-linked by genipin was evaluated. On the second approach, the PVA was functionalized with cis-5-norbornene-endo-2,3-dicarboxylic anhydride (PVA-Nb) to allow an ultrafast light-curing thiol-ene cross-linking. Comprehensive experiments were conducted to evaluate the influence of the SDCM ratio in mechanical properties, water uptake, swelling, cell viability, and printability of the PVA-based formulations. The studies performed with the PVA-A/SDCM formulations cross-linked by genipin showed printability, but poor shape retention due to slow cross-linking kinetics. On the other hand, the PVA-Nb/SDCM showed good printability. The results showed that incorporation of SDCM into PVA-Nb reduces the compression modulus, enhance cell viability, and bioprintability and modulate the swelling ratio of the resulted hydrogels. Results indicated that PVA-Nb hydrogels containing SDCM could be considered as versatile bioinks for cartilage bioprinting.     

Ionic Conductive Organohydrogels with Dynamic Pattern Behavior and Multi-Environmental Stability

Multi-environmental tolerant hydrogels have received significant attention and are promising for application as smart materials in multiple environments (e.g., water, oil, freezing, and dry). However, the macroscopic change and anti-swelling mechanisms of organohydrogels in different solvents and their corresponding applications have not been adequately harnessed. Herein, an ionic organohydrogel with excellent mechanical properties and unique behaviors (information identification and encryption) and mechanical sensing in multiple environments is prepared. The prepared organohydrogel shows an obvious transparent change in different solvents owing to the microphase separation in poor solvents and swelling in suitable solvents, and can be treated as a dynamic information memory device for recording and encrypting information. Furthermore, owing to the interaction between water and dimethylsulfoxide (DMSO), the organohydrogel demonstrates a prominent freezing resistance (−90 to 20 °C) and moisturizing retention properties (76% after 15 days). In addition, the ionic conductive hydrogel exhibits outstanding human motion detection and physiological signal response and displays a stable mechanical sensing performance in freezing, dry conditions, and oil or water environments. It is envisioned that the design strategies and mechanistic investigation of organohydrogels may be promising for application as bio-sensors and information-recognition platforms in harsh environments.     

Designing for impact: the effect of rigor and case study design on citations of qualitative case studies in management

Scientometrics - One of the most highly cited papers in management is Eisenhardt’s (Acad Manag Rev 14(4):532–550, 1989) piece on the importance of case study research, in particular...     

Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.