The effect of Al(NO3)3 concentration on the formation of AuNPs using low temperature hydrothermal reaction for memory application

Distribution of gold nanoparticles (AuNPs) on a substrate becomes crucial in nanotechnology applications. This work describes a route to fabricate AuNPs directly on silicon substrates by using an aluminum template in hydrothermal reaction at 80°C for 1 h. The effect of aluminum nitrate (Al(NO3)3) concentration in the hydrothermal bath was investigated. The properties of AuNPs were studied using field-emission scanning electron microscope (FESEM), x-ray diffractometer (XRD) and semiconductor characterization system (SCS). Two distinct sizes of AuNPs were observed by FESEM. XRD analysis proved the formation of AuNPs directly on the substrate. AuNPs were embedded between polymethylsilsesquioxane (PMSSQ) in order to investigate their effect on memory properties. The sample grown in 0.1 M Al(NO3)3 exhibited the largest hysteresis window (2.6 V) and the lowest Vth (2.2 V) to turn ‘ON’ the memory device. This indicated that good distribution of FCC structure AuNPs with 80±4 nm and 42±7 nm of large and small particles produced better charge storage capability. Charge transport mechanisms of AuNPs embedded in PMSSQ were explained in details whereby electrons from Si are transported across the barrier by thermionic effects via field-assisted lowering at the Si-PMSSQ interface with the combination of the Schottky and Poole Frenkel emission effect in Region 1. Trapped charge limited current (TCLC) and space charge limited current (SCLC) transport mechanism occurred in Region 2 and Region 3

Simulation Of Carbon Nanotubes For Hydrogen Storage Using Neural Network: A Preliminary Study

The discovery of carbon nanotubes (CNT) by 5umio Iijima in 1991 has attracted many researchers worldwide to study and explore the newly found materials. The unique characteristics that CNT possess include excellent properties for energy production and hydrogen storage. Currently, there are 4 technologies available for hydrogen storage: compressed gas, liquefaction, metal hydrides and physisorption. It has been claimed that physisorption is the most promising hydrogen storage method for meeting the goals of the US Department of Energy (DOE) Hydrogen Plan for fuel cell powered vehicles. CNT are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. A number of theoretical and experimental investigations have been made in this area mainly to study whether CNT can reach the benchmark of gravimetric density of 6.5 wt% and volumetric density of 62 kg H2/m3 set by the DOE Hydrogen Plan. Based on previous researches, a numerical simulation of CNT for hydrogen storage using Artificial Neural Network (ANN) will be developed

Airborne laser scanning for forested landslides investigation in temperate and tropical environments

Landslide hazard and risk have increased over the last decades and pose a significant threat to modern society. Despite remarkable efforts of compiling and updating landslide maps at regional, national or global scales, the number of landslide events is often underestimated, especially in forested areas where the vegetation obscures the geomorphic features indicative of landsliding. The primary objective of this study was to investigate the suitability of an active remote sensing technique, airborne laser scanning (ALS), for mapping and classifying landslides in temperate and tropical forest environments. The methods were developed in two study areas: (1) the Bois Noir area in France (Southern French Alps), (2) the Cameron Highlands in Malaysia (tropical rainforest region). In conclusion, the emergence of ALS for investigating geomorphic processes and activities has improved our ability to map, monitor and model the topographic terrain signature and landslide-induced vegetation anomalies. This study explicitly proved that ALS can be a very important new data source and mapping tool to characterize landslides even in a complex environment. The increased prevalence of modern ALS system and advanced point cloud processing had led the ways to improve future landslide maps and subsequently reduce landslide risk. Given the complexity of the terrain, automating the inventorization will still be challenging in the tropics with extensive anthropogenic activity, and differentiating the vegetative reaction in response to different earth surface processes requires further research. Airborne remote sensing is a critical and supportive tool for better understanding of landslide geomorphology in a changing environment

On the lack of observability for wave equations: a Gaussian beam approach

This paper is devoted to study the property of bservability for wave equations guaranteeing that the total energy of solutions may be estimated by means of the energy concentrated on a subset of the domain or of the boundary. We prove that this property fails in three different situations. First, we consider the wave equation with piecewise smooth coefficients when the observation is made in the exterior boundary. We also present a wave equation with highly oscillating Hölder continuous coefficients for which observability fails from any open set that does not contain the origin. Finally, lack of observability is proved for the constant coefficient wave equation when the observation is made from an interior hypersurface. All the counterexamples presented here are constructed using highly localized solutions known as Gaussian beams

Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area

The discovery of carbon nanotubes (CNT) by Sumiio Iijima in 1991 has attracted many researchers worldwide to study and explore thc newly found materials. CNT are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Rcccnt reports suggest that total surfacc area of carbon affect the hydrogen storage capacities in carbon nanotubes. An Artificial Neural Network model was created to study the relationship between the surface area of carbon and the hydrogen adsorption

High Density Airborne LIDAR Estimation of Disrupted Trees Induced by landslides

Airborne laser scanning (ALS) data has revolutionized the landslide assessment in a rugged vegetated terrain. It enables the parameterization of morphology and vegetation of the instability slopes. Vegetation characteristics are by far less investigated because of the currently available accuracy and density ALS data and paucity of field data validation. We utilized a high density ALS (HDALS) data with 170 points m-2 for characterizing disrupted vegetation induced by landslides by means of a variable window filter and the SkelTre-skeletonisation. Tree analyses in landslide areas resulted in relatively low height, small crown and more irregularities, whereas these peculiarities are not so obvious in the healthy forests. The statistical tests unveiled the clear differences between the extracted parameters in landslide and non-landslide zones and supported the field evidences. We concluded that HDALS is a promising tool to geometrically retrieve disrupted woody vegetation structures and can be good bioindicator to landslide activity