Graphene and derivatives – Synthesis techniques, properties and their energy applications

2D nanomaterials with exceptional electrical, mechanical and thermal properties are promising reinforcing materials for fabricating high-performance composite materials. Rapid developments in nanotechnology in recent years have facilitated the development of advanced materials for functional devices. In particular, this review is focussed on the application of graphene nanoparticle-based composites (GNP's) and graphene derivatives in the fields of energy storage and conversion devices. This review focuses on these recent developments including the synthesis of graphene-based materials and its derivative, as well as the related achieved electrical, mechanical and thermal properties

Parametric study for graphene reinforced aluminum matrix composites production using Box Behnken design

The production of graphene reinforced aluminum matrix composite through powder metallurgical route requires optimization of process parameters to obtain better performance characteristics. One of the advanced method available for statistical analysis of parameters is Response Surface Methodology (RSM). The statistical analysis was carried out with three parameters, weight percentage of graphene reinforcement Wg (0.05%, 0.1% and 0.2%), stirring time ST(1h, 2h and 3h) and compaction pressure Pc(16T, 17T and 19T) while sintering temperature T kept constant. The performance of the Box Behnken design was analyzed and optimized using Design Expert software for the effective production of composites. From the results obtained from the analysis, the best set of parameters were considered for the future production of composites

Mechanical properties of graphene oxide reinforced aluminium matrix composites

In this paper, the properties of powder metallurgy produced samples of GO reinforced aluminium composites were examined. Discs of 20 mm diameter and 0.5 mm thickness were made from pure Al powder of 35 μm particle size and with GO reinforcement at different GO wt% (0.05, 0.1 and 0.2). The mixture of Al/GO powders prepared through liquid infiltration were cold compacted and then sintered. The GO reinforced Al matrix composites were characterised using the scanning e lectron microscope with energy dispersion spectroscopy (SEM/ EDX ) for investigation of the homogeneous dispersion of GO into the matrix. X - ray diffraction (XRD) analysis for crystallographic phase and micro - Raman spectroscopy w as used to identify the phases inside the composite matrix after the sintering process. Micro hardness and the strength values from the produced Al/GO composites were recorded. It is evident from the results obtained that where uniform mixing is achieved, GO reinforced Al composites ca n be produced with similar hardness values as for those produced from rGO reinforced Al composites

Calibration of second-order correlation functions for non-stationary sources with a multi-start multi-stop time-to-digital converter

A novel high-throughput second-order-correlation measurement system is developed which records and makes use of all the arrival times of photons detected at both start and stop detectors. This system is suitable particularly for a light source having a high photon flux and a long coherence time since it is more efficient than conventional methods by an amount equal to the product of the count rate and the correlation time of the light source. We have used this system in carefully investigating the dead time effects of detectors and photon counters on the second-order correlation function in the two-detector configuration. For a non-stationary light source, distortion of original signal was observed at high photon flux. A systematic way of calibrating the second-order correlation function has been devised by introducing a concept of an effective dead time of the entire measurement system.Comment: 7 pages, 6 figure

Observation of sub-Poisson photon statistics in the cavity-QED microlaser

We have measured the second-order correlation function of the cavity-QED microlaser output and observed a transition from photon bunching to antibunching with increasing average number of intracavity atoms. The observed correlation times and the transition from super- to sub-Poisson photon statistics can be well described by gain-loss feedback or enhanced/reduced restoring action against fluctuations in photon number in the context of a quantum microlaser theory and a photon rate equation picture. However, the theory predicts a degree of antibunching several times larger than that observed, which may indicate the inadequacy of its treatment of atomic velocity distributions.Comment: 4 pages, 4 figure

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

Additively Manufactured Carbon Fiber- Reinforced Thermoplastic Composite Mold Plates For Injection Molding Process

Polymer injection molding processes have been used to create high-volume parts quickly and efficiently. Injection molding uses mold plates that are traditionally made of very hard tool steels, such as P20 steel, which is extremely heavy and has very long lead times to build new molds. In this study, composite-based additive manufacturing (CBAM) was used to create mold plates using long-fiber carbon fiber and polyether ether ketone (PEEK). These mold plates were installed in an injection molding machine, and rectangular flat plates were produced using Lustran 348 acrylonitrile butadiene styrene (ABS). Tensile and flexural testing was performed on these parts as well as parts produced using traditional P20 steel mold plates with the same geometry to compare the performance of the different mold plates. The parts produced using the carbon fiber mold plates were within 5% of the tensile strength and 10% of the flexural strength of the traditionally manufactured parts. However, the parts produced using the carbon fiber mold plates required additional cooling time due to the lower conductivity of the carbon fiber composite compared to the P20 steel. This allows additively manufactured composite molds to be a good substitute for conventional molds in low-volume injection molding production

Digital health for optimal supportive care in oncology: benefits, limits, and future perspectives

Background Digital health provides solutions that capture patient-reported outcomes (PROs) and allows symptom monitoring and patient management. Digital therapeutics is the provision to patients of evidence-based therapeutic interventions through software applications aimed at prevention, monitoring, management, and treatment of symptoms and diseases or for treatment optimization. The digital health solutions collecting PROs address many unmet needs, including access to care and reassurance, increase in adherence and treatment efficacy, and decrease in hospitalizations. With current developments in oncology including increased availability of oral drugs and reduced availability of healthcare professionals, these solutions offer an innovative approach to optimize healthcare resource utilization. Design This scoping review clarifies the role and impact of the digital health solutions in oncology supportive care, with a view of the current segmentation according to their technical features (connection to sensors, PRO collection, remote monitoring, self-management in real time…), and identifies evidence from clinical studies published about their benefits and limitations and drivers and barriers to adoption. A qualitative summary is presented. Results Sixty-six studies were identified and included in the qualitative synthesis. Studies supported the use of 38 digital health solutions collecting ePROs and allowing remote monitoring, with benefits to patients regarding symptom reporting and management, reduction in symptom distress, decrease in unplanned hospitalizations and related costs and improved quality of life and survival. Among those 38 solutions 21 provided patient self-management with impactful symptom support, improvement of QoL, usefulness and reassurance. Principal challenges are in developing and implementing digital solutions to suit most patients, while ensuring patient compliance and adaptability for use in different healthcare systems and living environments. Conclusions There is growing evidence that digital health collecting ePROs provide benefits to patients related to clinical and health economic endpoints. These digital solutions can be integrated into routine supportive care in oncology practice to provide improved patient-centered care

Performance Evaluation Of Composite Sandwich Structures With Additively Manufactured Aluminum Honeycomb Cores With Increased Bonding Surface Area

Modern aerostructures, including wings and fuselages, increasingly feature sandwich structures due to their high-energy absorption, low weight, and high flexural stiffness. The face sheet of these sandwich structures are typically thin composite laminates with interior honeycombs made of Nomex or aluminum. Standard cores are structurally efficient, but their design cannot be varied throughout the structure. With additive manufacturing (AM) technology, these core geometries can be altered to meet the design requirements that are not met in standard honeycomb cores. This study used a modified aluminum honeycomb core, with increased surface area on the top and bottom, as the core material in sandwich panels. The modified honeycomb core was produced through the laser powder bed fusion method. The behavior of the modified sandwich composite panels was evaluated through three-point bend, edgewise compression, and impact tests, and their performance was compared to that of a conventional honeycomb core sandwich panel. The three-point bend test results indicated that the sandwich structure\u27s ultimate shear strength improved by 12.6% with the modified honeycomb core. Additionally, the displacement at the failure of the structure increased by 11%. The edgewise compression tests showed that the ultimate edgewise compressive strength improved by 19.1% when using the modified core. The impact test results revealed that the peak force increased by 8% and the energy-absorbing capacity of the sandwich structure increased by 20% with the use of the modified honeycomb core