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Nitro-tetrazole based high performing explosives:Recent overview of synthesis and energetic properties

Defence Technology（防务技术） 2021年第6期17卷 1995-2010页

作者： saira manzoor Qamar-un-nisa Tariq Xin Yin Jian-Guo Zhang State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China

Heterocyclic skeleton(Azoles)and different energetic groups containing high performing explosives are highly emerged in recent years to meet the challenging requirements of energetic materials in both military and civilian applications with improved *** this purpose tetrazole(Azole)is identified as an attractive heterocyclic backbone with energetic functional groups nitro(-NO_(2)),nitrato(-ONO_(2)),nitrimino(-NNO_(2)),and nitramino(eNHeNO_(2))to replace the traditionally used high performing *** tetrazole based compounds having these energetic functional groups demonstrated advanced energetic performance(detonation velocity and pressure),densities,and heat of formation(HOF)and became a potential replacement of traditional energetic compounds such as *** review presents a summary of the recently reported nitro-tetrazole energetic compounds containing poly-nitro,di/mono-nitro,nitrato/nitramino/nitrimino,bridged/bis/di tetrazole and nitro functional groups,describing their preparation methods,advance energetic properties,and further applications as highperforming explosives,especially those reported in the last *** review aims to provide a fresh concept for designing nitro-tetrazole based high performing explosives together with major challenges and perspectives.

关键词： Tetrazole Energetic materials Functional groups Heterocyclic High-performing explosives

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Recent advances in the synthesis and energetic properties of potassium-based potential green primary explosives

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Defence Technology（防务技术） 2022年第11期18卷 1945-1959页

作者： Qamar-un-Nisa Tariq saira manzoor Maher-un-Nisa Tariq Wen-Li Cao Jian-Guo Zhang State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China Department of Chemistry University of OkaraOkara56300Pakistan

Primary explosives are utilized as a reliable initiator for secondary explosives in an extensive range of military and civilian operations. Heavy-metal-based primary explosives are moderate performing, more sensitive, and environmentally hazardous, posing a direct and indirect threat to health and ***, heavy-metal-based primaries have been replaced by environment-friendly metal-based primary explosives, such as potassium complexes. This review presents not only a summary of the current progress of new-generation potassium-based primary explosives and their methods of preparation, energetic properties, and applications, but also a further comparison with traditional primary explosives. In addition, this work discusses the necessity of heavy metal-free primary explosives and the major challenges faced in replacing traditional primary explosives.

关键词： Green primary explosives Heterocyclic compounds Synthesis Energetic properties

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Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

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Computers, Materials & Continua 2021年第8期68卷 2159-2178页

作者： Bakhtawar Aslam Ziyad Awadh Alrowaili Bushra Khaliq Jaweria manzoor saira Raqeeb Fahad Ahmad Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan Physics Department College of ScienceJouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect *** study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted ***,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone *** permanent changes in climate are of serious *** leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV *** research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune *** have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through ***,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping *** is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered *** classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or *** proposed model presented 93%accuracy,and it outperforms the prevailing techniques.

关键词： Deep learning image processing classification climate variation ozone layer depleted region non-depleted region UV radiation faster region-based convolutional neural network

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