Ambient Protection of Few‐Layer Black Phosphorus via Sequestration of Reactive Oxygen Species

Few‐layer black phosphorous (BP) has emerged as a promising candidate for next‐generation nanophotonic and nanoelectronic devices. However, rapid ambient degradation of mechanically exfoliated BP poses challenges in its practical deployment in scalable devices. To date, the strategies employed to protect BP have relied upon preventing its exposure to atmospheric conditions. Here, an approach that allows this sensitive material to remain stable without requiring its isolation from the ambient environment is reported. The method draws inspiration from the unique ability of biological systems to avoid photo‐oxidative damage caused by reactive oxygen species. Since BP undergoes similar photo‐oxidative degradation, imidazolium‐based ionic liquids are employed as quenchers of these damaging species on the BP surface. This chemical sequestration strategy allows BP to remain stable for over 13 weeks, while retaining its key electronic characteristics. This study opens opportunities to practically implement BP and other environmentally sensitive 2D materials for electronic applications.     

An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features

This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.     

Virtualized controller placement for multi-domain optical transport networks using machine learning

Photonic Network Communications - Optical multi-domain transport networks are often controlled by a hierarchical distributed architecture of controllers. Optimal placement of these controllers is...     

Heterogeneous catalysts for production of chemicals using carbon dioxide as raw material: A review

The utilization of CO₂ for the production of useful chemicals using heterogeneous catalysts is one of the ways to reduce the anthropogenic greenhouse gases in the atmosphere. In many cases, the CO₂ conversion and products yield are still considered very low and need to be operated at high pressure and temperature. The critical point in CO₂ conversion is to activate the CO₂ molecules either by adding a co-reactant or by using effective catalysts. This paper presents the current development on the effect of several precursors like metals, metal oxides, ionic liquids, and acid–base loaded on a suitable support in creating magical properties of catalysts on the performance of CO₂ conversion. Cu/ZnO-based catalysts, ionic liquid-based catalysts, and metal oxides-based catalysts are reported to be the most effective catalysts in the formation of methanol, cyclic carbonates and dimethyl carbonate. This review also focuses on various strategies and developments in altering heterogeneous catalysts, followed by critical factors of CO₂ molecule activation, and the optimization of the catalytic activity or catalysts reusability.     

Mitigation measures to reduce greenhouse gas emissions and enhance carbon capture and storage in Saudi Arabia

The main causes of global warming are now attributed to the burning of fossil fuels. Saudi Arabia is the world's largest producer and exporter of total petroleum liquids, and one of the largest consumers of total primary energy. The activities which are mainly responsible for significant greenhouse gas emissions are consistently in the upslope. The electricity generation, the solid waste management, and the agricultural sectors are responsible for the highest share of emissions of CO₂, CH₄, and N₂O, respectively. The results of current research provided the initial justifications for the renewable energy sources such as solar and wind energy conversion, and hybrid systems. The Master Gas Collection System of Saudi Aramco can be considered as a remarkable step forward in lowering CH₄ emissions from the oil and gas fields. The integrated efforts of the public and private sectors are essential for development and implementation of appropriate strategies to reduce greenhouse gas emissions. The study provides an overview of Saudi initiatives related to policy, plan, program, and/or project towards the reduction of greenhouse gases and enhancement of carbon capture and storage.     

Engine's behaviour on magnetite nanoparticles as additive and hydrogen addition of chicken fat methyl ester fuelled DICI engine: A dual fuel approach

The present work evaluated the influence of magnetite nanoparticles in chicken fat methyl-ester blend (CFME20) and hydrogen induction in CFME20 nano-fuels combustion performance and exhaust emissions using a 1-cylinder dual DICI engine. Results revealed that CFME20 blend exhibits a significant reduction in smoke, hydrocarbon, and carbon-monoxide emissions; however, increment in oxides of nitrogen and insignificant reduction in BTE also noted. Magnetite nanoparticles mixed with CFME20 through ultrasonication to make nano-additive fuel which noticeably decreased the exhaust emissions including oxides of nitrogen and slightly enhanced the BTE as well. Performance parameters gradually enhanced with escalating nanoparticles dosage up to 100 ppm. Hydrogen induction during nano-additive pilot fuel combustion further decreased the smoke, carbon-monoxide, and hydrocarbon emissions and enhanced the BTE to an appreciable level. However, oxides of nitrogen also enhanced with increasing hydrogen flow rate, but for 10 lpm trivial increased. Hence, the results confirmed that nano-fuel (100 ppm) with hydrogen (10 lpm) addition provides optimum outcomes.     

Multi-layered attentional peephole convolutional LSTM for abstractive text summarization

Abstractive text summarization is a process of making a summary of a given text by paraphrasing the facts of the text while keeping the meaning intact. The manmade summary generation process is laborious and time-consuming. We present here a summary generation model that is based on multilayered attentional peephole convolutional long short-term memory (MAPCoL; LSTM) in order to extract abstractive summaries of large text in an automated manner. We added the concept of attention in a peephole convolutional LSTM to improve the overall quality of a summary by giving weights to important parts of the source text during training. We evaluated the performance with regard to semantic coherence of our MAPCoL model over a popular dataset named CNN/Daily Mail, and found that MAPCoL outperformed other traditional LSTM-based models. We found improvements in the performance of MAPCoL in different internal settings when compared to state-of-the-art models of abstractive text summarization.