Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

Nanomaterials are at the leading edge of the emerging field of nanotechnology. Their unique and tunable size-dependent properties (in the range 1–100 nm) make these materials indispensable in many modern technological applications. In this Review, we summarize the state-of-art in the manufacture and applications of inorganic nanoparticles made using continuous hydrothermal flow synthesis (CHFS) processes. First, we introduce ideal requirements of any flow process for nanoceramics production, outline different approaches to CHFS, and introduce the pertinent properties of supercritical water and issues around mixing in flow, to generate nanoparticles. This Review then gives comprehensive coverage of the current application space for CHFS-made nanomaterials including optical, healthcare, electronics (including sensors, information, and communication technologies), catalysis, devices (including energy harvesting/conversion/fuels), and energy storage applications. Thereafter, topics of precursor chemistry and ...

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Continuous Hydrothermal Synthesis of Inorganic Nanoparticles: Applications and Future Directions

Popularity of graphene oxide (GO) became ballistic in the last few years – in short, it almost seems that now everyone wants to work with graphene. However, as the number of electrochemical papers about graphene grows, so is the amount of misinterpretations. Our work was mostly focused on preparation of GO and its electrochemical properties, namely catalysis. Our results show, that chemical preparation of GO by strong oxidation is very tricky. Several approaches were tested, such as different sizes of graphite particles, oxidation mixtures, even repetitive preparations under the same conditions usually resulted in different products. Another big topic are impurities which are present due to natural occurrence in graphite and some of them might be also introduced during purification processes of GO. This could also lead to often observed catalytic activity of graphene. From our observations neither selectivity nor significant electrocatalytic abilities of the reduced graphene oxide modified electrodes towards H2O2 were observed. Moreover, our results suggest that GO does not need linking molecules in order to be deposited onto the surface of the electrodes, since it attaches spontaneously and keeps its electrochemical properties. Graphene is usually reported as a superior material, but its proper description and characterization should not be underestimated.

Sherif Elbasuney

Papers

Surface engineering of layered double hydroxide (LDH) nanoparticles for polymer flame retardancy

Novel multi-component flame retardant system based on nanoscopic aluminium-trihydroxide (ATH)

Sustainable steric stabilization of colloidal titania nanoparticles

Combustion characteristics of extruded double base propellant based on ammonium perchlorate/aluminum binary mixture

Stabilized super-thermite colloids: A new generation of advanced highly energetic materials