Role of surface composition in morphological evolution of GaAs nano-dots with low-energy ion irradiation

The surface chemistry of GaAs (100) with 50-keV Ar⁺ ion beam irradiation at off-normal incidence has been investigated in order to elucidate the surface nano-structuring mechanism(s). Core level and valence band studies of the surface composition were carried out as a function of fluences, which varied from 1 × 10¹⁷ to 7 × 10¹⁷ ions/cm². Core-level spectra of samples analyzed by X-ray photoelectron spectroscopy confirmed the Ga enrichment of the surface resulting in bigger sized nano-dots. Formation of such nano-dots is attributed to be due to the interplay between preferential sputtering and surface diffusion processes. Valence band measurement shows that the shift in the Fermi edge is higher for Ga- rich, bigger sized nano-dots due to the partial oxide formation of Ga. ‘One-dimensional power spectral density’ extracted from atomic force micrographs also confirms the significant role of surface diffusion in observed nano-structuring.     

Functional modelling,validation and testing of analog to digital converters (ADC) from system design perspective

In this paper a methodology to develop PSpice (SPICE) model for flash type analog to digital converters and its validation is presented. With the onset of the Internet of Things and ongoing digitization of electronic devices around us, almost every device needs an interface or controlling unit as a digital computing device. As more and more products perform calculations in the digital or discrete time domain, more sophisticated data converters must translate the digital data to and from our inherent analog world. This varied nature of interfaces necessitates simulation of ADC along with a functional simulation of the system to optimize the performance of design and improve reliability. The paper focuses on the development of a functional model for ADC which can expedite system development. The design philosophy was at the block level and the development was conducted for commercially available ADC, AD7828. The complete model has been simulated using PSpice and results were compared with laboratory test measurements using AD7828.     

Anion water in gypsum (CaSO4·2H2O) and hemihydrate (CaSO4·1/2H2O)

The bonding nature of water in gypsum, CaSO₄·2H₂O, and hemihydrate, CaSO₄·1/2H₂O, was suggested by characteristic absorption bands of water and sulphate ions. The infrared spectral data indicate the presence of anion water in gypsum and hemihydrate through hydrogen bonding. Negative shifting of bending vibration of SO₄ ion and lowering and broadening of the O-H stretching vibration at around 3600 cm⁻¹ indicate the presence of both anion water and hydrogen bonding in gypsum and hemihydrate     

Real-time nonlinear correction of back-focal-plane detection in optical tweezers

Photodiode based detection of laser trapped beads using forward scattered light is a frequently employed technique for position measurement. There is a nonlinear relationship between photodiode outputs and bead position but for small displacements linear approximation holds well. Traditionally, the nonlinearity is compensated by normalizing the photodiode's position signal with the intensity signal and then using a polynomial fit in the range where voltage to position mapping is one to one. In this article, this range is extended by using the intensity signal as an independent input along with the two position signals. A map from the input signals to the actual position values is obtained. This mapping is one-to-one for a larger range that results in an increased detection range. An artificial neural network that facilitates implementation is employed for this purpose. This scheme is implemented on a Field Programmable Gate Array based data acquisition and control hardware with closed loop bandwidth of 50 kHz. Detection of the order of 350 nm from the center of detection laser is demonstrated for a 500 nm diameter bead compared to 180 nm achieved by a polynomial fit.     

DDC-OMDC: Deadline-based data collection using optimal mobile data collectors in Internet of Things

Mobile data collectors (MDCs) are very efficient for data collection in internet of things (IoT) sensor networks. These data collectors collect data at rendezvous points to reduce data collection latency. It is paramount to determine these points in an IoT network to collect data in real time. It is important to consider IoT network characteristics to collect data on a specific deadline. First, the disconnected IoT sensor network is a real challenge in IoT applications. Second, it is essential to determine optimal data collection points (DCPs) and MDCs simultaneously to collect data in real time. In this study, Deadline-based Data Collection using Optimal Mobile Data Collectors (DDC-OMDC) scheme is proposed that aims to collect data in a disconnected network with the optimal number of mobile data collectors in a specific deadline for delay-intolerant applications. DDC-OMDC works in two phases. In the first phase, the optimal number of MDCs is determined to collect data at the optimal data collection points to guarantee one-hop data collection from each cluster. The optimal mobile data collectors are determined using optimal DCPs, data collection stopping time, and a specific deadline. In the second phase, the optimal data collection trajectory is determined for each MDC using the nearest neighbor heuristic algorithm to collect data in real time. The simulation results show that the proposed scheme outperforms in collecting data in real time and determines optimal mobile data collectors and optimal data collection trajectory to collect data in a specific deadline for delay-intolerant applications.     

Growth of InAs Quantum Dots on Germanium Substrate Using Metal Organic Chemical Vapor Deposition Technique

Self-assembled InAs quantum dots (QDs) were grown on germanium substrates by metal organic chemical vapor deposition technique. Effects of growth temperature and InAs coverage on the size, density, and height of quantum dots were investigated. Growth temperature was varied from 400 to 450 °C and InAs coverage was varied between 1.40 and 2.35 monolayers (MLs). The surface morphology and structural characteristics of the quantum dots analyzed by atomic force microscope revealed that the density of the InAs quantum dots first increased and then decreased with the amount of InAs coverage; whereas density decreased with increase in growth temperature. It was observed that the size and height of InAs quantum dots increased with increase in both temperature and InAs coverage. The density of QDs was effectively controlled by growth temperature and InAs coverage on GaAs buffer layer.     

Diffusion, counter-diffusion and lipid phase changes occurring during oil migration in model confectionery systems

This study investigated the diffusion of peanut oil and counter-diffusion of cocoa butter when cylindrical tempered or untempered samples, composed of cocoa butter with or without addition of cocoa powder, were suspended in peanut oil and stored at 23 °C. Oil migration kinetics was monitored using a novel pipette technique while diffusion and counter-diffusion were measured using gas chromatography. Tempering significantly lowered (p < 0.05) the peanut oil diffusion in the samples but did not influence the counter-diffusion of cocoa butter in peanut oil. Addition of cocoa powder at a volume fraction of 0.45 significantly increased (p < 0.05) peanut oil diffusion and decreased counter-diffusion. Due to the ingress of peanut oil, the solid fat content of tempered and untempered cocoa butter samples dropped from 75% to 59% after 7 weeks and resulted in corresponding dimensional changes in the samples which were also measured using image analysis. Diffusion, counter-diffusion and lipid phase changes were combined to develop a mass balance model to predict the volume changes occurring during oil migration.