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

Graphene has attracted tremendous research interest in recent years, owing to its exceptional properties. The scaled-up and reliable production of graphene derivatives, such as graphene oxide (GO) and reduced graphene oxide (rGO), offers a wide range of possibilities to synthesize graphene-based functional materials for various applications. This critical review presents and discusses the current development of graphene-based composites. After introduction of the synthesis methods for graphene and its derivatives as well as their properties, we focus on the description of various methods to synthesize graphene-based composites, especially those with functional polymers and inorganic nanostructures. Particular emphasis is placed on strategies for the optimization of composite properties. Lastly, the advantages of graphene-based composites in applications such as the Li-ion batteries, supercapacitors, fuel cells, photovoltaic devices, photocatalysis, as well as Raman enhancement are described (279 references).

/pdf/graphene-based-composites-540z2xd3o0.pdf

Graphene-based composites

The PASCAL Visual Object Classes Challenge

Graphene, a two-dimensional, single-layer sheet of sp(2) hybridized carbon atoms, has attracted tremendous attention and research interest, owing to its exceptional physical properties, such as high electronic conductivity, good thermal stability, and excellent mechanical strength. Other forms of graphene-related materials, including graphene oxide, reduced graphene oxide, and exfoliated graphite, have been reliably produced in large scale. The promising properties together with the ease of processibility and functionalization make graphene-based materials ideal candidates for incorporation into a variety of functional materials. Importantly, graphene and its derivatives have been explored in a wide range of applications, such as electronic and photonic devices, clean energy, and sensors. In this review, after a general introduction to graphene and its derivatives, the synthesis, characterization, properties, and applications of graphene-based materials are discussed.

Graphene-Based Materials: Synthesis, Characterization, Properties, and Applications

Statistical Methods For Agricultural Workers

Hybridization of nanoscale metals and carbon nanotubes into composite nanomaterials has produced some of the best-performing sensors to date. The challenge remains to develop scalable nanofabrication methods that are amenable to the development of sensors with broad sensing ranges. A scalable nanostructured biosensor based on multilayered graphene petal nanosheets (MGPNs), Pt nanoparticles, and a biorecognition element (glucose oxidase) is presented. The combination of zero-dimensional nanoparticles on a two-dimensional support that is arrayed in the third dimension creates a sensor platform with exceptional characteristics. The versatility of the biosensor platform is demonstrated by altering biosensor performance (i.e., sensitivity, detection limit, and linear sensing range) through changing the size, density, and morphology of electrodeposited Pt nanoparticles on the MGPNs. This work enables a robust sensor design that demonstrates exceptional performance with enhanced glucose sensitivity (0.3 µM detection limit, 0.01–50 mM linear sensing range), a long stable shelf-life (>1 month), and a high selectivity over electroactive, interfering species commonly found in human serum samples.

/pdf/nanostructuring-platinum-nanoparticles-on-multilayered-4414iupb4c.pdf

Nanostructuring Platinum Nanoparticles on Multilayered Graphene Petal Nanosheets for Electrochemical Biosensing

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.

Audio Event Detection using Weakly Labeled Data

In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for sound event detection and classification using weakly labeled audio data. Our model trains efficiently from audios of variable lengths; hence, it is well suited for transfer learning. We then propose methods to learn representations using this model which can be effectively used for solving the target task. We study both transductive and inductive transfer learning tasks, showing the effectiveness of our methods for both domain and task adaptation. We show that the learned representations using the proposed CNN model generalizes well enough to reach human level accuracy on ESC-50 sound events dataset and sets state of art results on this dataset. We further use them for acoustic scene classification task and once again show that our proposed approaches suit well for this task as well. We also show that our methods are helpful in capturing semantic meanings and relations as well. Moreover, in this process we also set state-of-art results on Audioset dataset using balanced training set.

Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes

First report on the photocatalytic coupling of carbon dioxide with epoxides to give cyclic carbonates under extremely mild conditions such as room temperature and atmospheric pressure conditions using a hybrid photocatalyst consisting of cobalt phthalocyanine grafted on titanium oxide (CoPc/TiO2) under visible irradiation is described. The developed protocol provided almost quantitative conversion of various epoxides to corresponding cyclic carbonates in excellent yields without any evidence for the formation of any byproduct. At the end of the reaction, the photocatalyst was separated by centrifugation and reused for several subsequent recycling runs without any significant loss in activity, and no leaching had been observed during the photocatalytic reactions.

Anurag Kumar

Papers

Nanostructuring Platinum Nanoparticles on Multilayered Graphene Petal Nanosheets for Electrochemical Biosensing

Audio Event Detection using Weakly Labeled Data

Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes

Audio Event Detection using Weakly Labeled Data

First Photocatalytic Synthesis of Cyclic Carbonates from CO2 and Epoxides Using CoPc/TiO2 Hybrid under Mild Conditions