The waste and scrap industry

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Recent synthetic efforts towards high energy density materials: How to design high-performance energetic structures?

Energetic material is a very essential company of compounds, widely used in various fields, primarily in the military industry and space technologies. These compounds are unique in that they are capable of instantly decomposing to release enormous energy. The chemical diversity of energetic organic compounds is constantly increasing through the combination of various high-nitrogen frameworks and explosophoric groups, which follows the overall trend of increasing interest in their chemistry and applications. The scientific community is constantly looking for more powerful and less sensitive to external impulses energetic compounds for both military and civilian use. Herein, a detailed overview regarding classic and novel explosophoric groups and various frameworks, as well as a description of the selected synthesis for the target compounds, has been given for the time period since 2010. The physical properties and performances of benchmark and prospective compounds are also collected

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images.

Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images.

Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data

Large-eddy simulation of starting buoyant jets

A series of Large Eddy Simulations (LES) are performed to investigate the penetration of starting buoyant jets. The LES code is first validated by comparing simulation results with existing experimental data for both steady and starting pure jets and lazy plumes. The centerline decay and the growth rate of the velocity and concentration fields for steady jets and plumes, as well as the simulated transient penetration rate of a starting pure jet and a starting lazy plume, are found to compare well with the experiments. After validation, the LES code is used to study the penetration of starting buoyant jets with three different Reynolds numbers from 2000 to 3000, and with a wide range of buoyancy fluxes from pure jets to lazy plumes. The penetration rate is found to increase with an increasing buoyancy flux. It is also observed that, in the initial Period of Flow Development, the two penetrative mechanisms driven by the initial buoyancy and momentum fluxes are uncoupled; therefore the total penetration rate can be resolved as the linear addition of these two effects. A fitting equation is proposed to predict the penetration rate by combining the two independent mechanisms.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitorin

Catalytic Decomposition of Oleic Acid to Fuels and Chemicals: Roles of Catalyst Acidity and Basicity on Product Distribution and Reaction Pathways

The roles of catalyst acidity and basicity playing in catalytic conversion of oleic acid were studied in a fixed-bed micro-reactor at atmospheric pressure. The chemical compositions of the petroleum-like products were obtained and the reaction pathways of different catalysts are discussed. The metal oxides are suitable for upgrading oleic acid into organic liquid products (OLPs). Over 98% oxygen was removed when CaO, MgO, and TiO2 were implemented, whereas a minimum oxygen removal lower than 20% was obtained by using quartz. The oxygen removal was 73% by alumina; however, the light oil yield (to feed) and the valuable product yield received were the highest in all investigated catalysts. The hydrocarbons in OLPs, overwhelmingly presenting in the product, were found to be alkenes and cycloalkenes, followed by saturated hydrocarbons, and then aromatics lower than 4%. For Lewis acidic catalysts, higher acidity of the catalyst is beneficial to deoxygenation but also secondary cracking. CaO has higher dehydrogenation capability than MgO does

An Integrative Analysis of Transcriptome, Proteome and Hormones Reveals Key Differentially Expressed Genes and Metabolic Pathways Involved in Flower Development in Loquat

Flower development is a vital developmental process in the life cycle of woody perennials, especially fruit trees. Herein, we used transcriptomic, proteomic, and hormone analyses to investigate the key candidate genes/proteins in loquat (Eriobotrya japonica) at the stages of flower bud differentiation (FBD), floral bud elongation (FBE), and floral anthesis (FA). Comparative transcriptome analysis showed that differentially expressed genes (DEGs) were mainly enriched in metabolic pathways of hormone signal transduction and starch and sucrose metabolism. Importantly, the DEGs of hormone signal transduction were significantly involved in the signaling pathways of auxin, gibberellins (GAs), cytokinin, ethylene, abscisic acid (ABA), jasmonic acid, and salicylic acid. Meanwhile, key floral integrator genes FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) and floral meristem identity genes SQUAMOSA PROMOTER BINDING LIKE (SPL), LEAFY (LFY), APETALA1 (AP1), and AP2 were significantly upregulated at the FBD stage. However, key floral organ identity genes AGAMOUS (AG), AP3, and PISTILLATA (PI) were significantly upregulated at the stages of FBE and FA. Furthermore, transcription factors (TFs) such as bHLH (basic helix-loop-helix), NAC (no apical meristem (NAM), Arabidopsis transcription activation factor (ATAF1/2) and cup-shaped cotyledon (CUC2)), MYB_related (myeloblastosis_related), ERF (ethylene response factor), and C2H2 (cysteine-2/histidine-2) were also significantly differentially expressed. Accordingly, comparative proteomic analysis of differentially accumulated proteins (DAPs) and combined enrichment of DEGs and DAPs showed that starch and sucrose metabolism was also significantly enriched. Concentrations of GA3 and zeatin were high before the FA stage, but ABA concentration remained high at the FA stage. Our results provide abundant sequence resources for clarifying the underlying mechanisms of the flower development in loquat

A Highly Efficient Visible Absorber Coating on a Curved Substrate

In this study, we propose and fabricate a perfect absorber on a planar substrate using alternate silicon dioxide and ultrathin metallic lossy chromium (Cr) films. Furthermore, we transfer the absorber to a curved substrate via an optimization design of symmetric structures. The absorber exhibits a highly efficient absorption and large incident-angular tolerance characteristics in the whole visible region. We investigate each layer contribution to the absorption theoretically, and find that ultrathin (~5 nm) lossy Cr films play a dominant absorptive role. Using the effective interface method, we calculate the phase difference on the lossy Cr front surface. It is close to the destructive interference condition, from which we clarify why the proposed structures can produce a highly efficient absorption