Parametric Optimization of Dye-Sensitized Solar Cells Using Far red Sensitizing Dye with Cobalt Electrolyte

A far-red sensitizing dye SQ-75 has been employed as a model sensitizer with Co(bpy)2+/3+ redox electrolytes to fabricate dye-sensitized solar cells (DSSCs) and optimize the various device parameters which influence the overall photoconversion efficiency (PCE). It has been found that the optimization of the TiO2 thickness, surface treatment with TiCl4, and an optimum amount of the chenodeoxycholic acid (CDCA) as coadsorber are necessary to attain the overall improved PCE. TiCl4 surface treatment on both FTO and TiO2 has been found to outperform as compared to their untreated counterparts owing to the suppression of the charge recombination. DSSCs with an optimized TiO2 thickness of 6 μm and CDCA concentration of 4 mM have exhibited best performance due to enhanced photon harvesting and reduced dye aggregation, respectively.12th International Conference on Nanomolecular Electronics (ICNME-2016), December 14-16, 2016, Kobe International Conference Center, Kobe, Japa

Granite classification using machine learning and edge computing [version 1; peer review: 1 approved, 2 approved with reservations]

Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and counters have a major influence on the interior d ́ecor which is essential to set the mood and ambience of a house. A system is needed to help the end users differentiate between granites, which enhance the grandeur of their house and also check the frauds of different color granite being sent by the merchant as compared to what was selected by the end user. Several models have been developed for this cause using CNN and other image processing techniques. However, a solution for this purpose must be precise and computationally efficient. Methods: For this purpose,researchers in this work developed a machine learning based granite classifier using Edge Computing and a website to help users in choosing which granite would go well with their d ́ecor is also built. The developed system consists of a color sensor [TCS3200] integrated with an ESP8266 board. The data pertaining to RGB contrasts of different rocks is acquired by using the color sensor from a dealership.This data is used to train a Machine Learning algorithm to classify the rock into different granite types from a granite dealer and yield the category prediction. Results: The proposed system yields a result of 94% accuracy when classified using Random Forest Algorithm. Conclusion: Thus, this system provides an upper hand for the end users in differentiating between different types of granites

Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

We consider a set of data samples such that a fraction of the samples are arbitrary outliers, and the rest are the output samples of a single-layer neural network with rectified linear unit (ReLU) activation. Our goal is to estimate the parameters (weight matrix and bias vector) of the neural network, assuming the bias vector to be non-negative. We estimate the network parameters using the gradient descent algorithm combined with either the median- or trimmed mean-based filters to mitigate the effect of the arbitrary outliers. We then prove that $\tilde{O}( \frac{1}{p^2}+\frac{1}{\epsilon^2p})$ samples and $\tilde{O} ( \frac{d^2}{p^2}+ \frac{d^2}{\epsilon^2p})$ time are sufficient for our algorithm to estimate the neural network parameters within an error of $\epsilon$ when the outlier probability is $1-p$, {where 2/3< p \leq 1} and the problem dimension is $d$ (with log factors being ignored here). Our theoretical and simulation results provide insights into the training complexity of ReLU neural networks in terms of the probability of outliers and problem dimension. <br/

Photoluminescence and photoluminescence excitation studies in 80 MeV Ni ion irradiated MOCVD grown GaN

We report damage creation and annihilation under energetic ion bombardment at a fixed thence. MOCVD grown GaN thin films were irradiated with 80 MeV Ni ions at a thence of 1 x 10(13) ions/cm(2). Irradiated GaN thin films were subjected to rapid thermal annealing for 60 s in nitrogen atmosphere to anneal out the defects. The effects of defects on luminescence were explored with photoluminescence measurements. Room temperature photoluminescence spectra from pristine sample revealed presence of band to band transition besides unwanted yellow luminescence. Irradiated GaN does not show any band to band transition but there is a strong peak at 450 nm which is attributed to ion induced defect blue luminescence. However, irradiated and subsequently annealed samples show improved band to band transitions and a significant decrease in yellow luminescence intensity due to annihilation of defects which were created during irradiation. Irradiation induced effects on yellow and blue emissions are discussed

Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

We consider a set of data samples such that a fraction of the samples are arbitrary outliers, and the rest are the output samples of a single-layer neural network with rectified linear unit (ReLU) activation. Our goal is to estimate the parameters (weight matrix and bias vector) of the neural network, assuming the bias vector to be non-negative. We estimate the network parameters using the gradient descent algorithm combined with either the median- or trimmed mean-based filters to mitigate the effect of the arbitrary outliers. We then prove that $\tilde{O}( \frac{1}{p^2}+\frac{1}{\epsilon^2p})$ samples and $\tilde{O} ( \frac{d^2}{p^2}+ \frac{d^2}{\epsilon^2p})$ time are sufficient for our algorithm to estimate the neural network parameters within an error of $\epsilon$ when the outlier probability is $1-p$, {where 2/3< p \leq 1} and the problem dimension is $d$ (with log factors being ignored here). Our theoretical and simulation results provide insights into the training complexity of ReLU neural networks in terms of the probability of outliers and problem dimension. Signal Processing System

Photoluminescence and photoluminescence excitation studies in 80 MeV Ni ion irradiated MOCVD grown GaN

We report damage creation and annihilation under energetic ion bombardment at a fixed thence. MOCVD grown GaN thin films were irradiated with 80 MeV Ni ions at a thence of 1 x 10(13) ions/cm(2). Irradiated GaN thin films were subjected to rapid thermal annealing for 60 s in nitrogen atmosphere to anneal out the defects. The effects of defects on luminescence were explored with photoluminescence measurements. Room temperature photoluminescence spectra from pristine sample revealed presence of band to band transition besides unwanted yellow luminescence. Irradiated GaN does not show any band to band transition but there is a strong peak at 450 nm which is attributed to ion induced defect blue luminescence. However, irradiated and subsequently annealed samples show improved band to band transitions and a significant decrease in yellow luminescence intensity due to annihilation of defects which were created during irradiation. Irradiation induced effects on yellow and blue emissions are discussed. (C) 2011 Elsevier B.V. All rights reserved

Seismic Detection of Euroquakes Originating From Europa's Silicate Interior

Detecting a seismic event from Europa's silicate interior would provide information about the geologic and tectonic setting of the moon's rocky interior. However, the subsurface ocean will attenuate the signal, possibly preventing the waveforms from being detected by a surface seismometer. Here, we investigate the minimum magnitude of a detectable event originating from Europa's silicate interior. We analyze likely signal-to-noise ratios and compare the predicted signal strengths to current instrument sensitivities. We show that a magnitude Mw ≥ 3.5 would be sufficient to overcome the predicted background noise when the ice shell is 5 km thick. However, a minimum magnitude of Mw ≥ 5.5 would be required for current instrumentation to be able detect the event for any ice shell thickness, at any distance. A thinner ice shell transmits greater ground acceleration amplitudes than a thicker ice shell, which might allow for Mw ≥ 4.5 to be detectable.ISSN:2333-508

Ion beam-mixing effects in nearly lattice-matched AlInN/GaN heterostructures by swift heavy ion irradiation

10.1080/10420150.2012.662977Radiation Effects and Defects in Solids1677506-511REDS

Estimating the 3D structure of the Enceladus ice shell from Flexural and Crary waves using seismic simulations

A seismic investigation on Saturn's moon Enceladus could determine the thickness of the ice shell, along with variations from the mean thickness, by recovering phase and group velocities, and through the frequency content of surface waves. Here, we model the Enceladus ice shell with uniform thicknesses of 5 km, 20 km, and 40 km, as well as with ice topography ranging from 5-40 km. We investigate several approaches for recovering the mean ice shell thickness. We show that surface wave dispersions could be used to determine the mean ice shell thickness. Flexural waves in the ice only occur if the shell is thinner than a critical value < 20 km. Rayleigh waves dominate only in thicker ice shells. The frequency content of Crary waves depends on the ice shell thickness.ISSN:0012-821XISSN:1385-013

Parametric Optimization of Dye-Sensitized Solar Cells Using Far red Sensitizing Dye with Cobalt Electrolyte

A far-red sensitizing dye SQ-75 has been employed as a model sensitizer with Co(bpy)2+/3+ redox electrolytes to fabricate dye-sensitized solar cells (DSSCs) and optimize the various device parameters which influence the overall photoconversion efficiency (PCE). It has been found that the optimization of the TiO2 thickness, surface treatment with TiCl4, and an optimum amount of the chenodeoxycholic acid (CDCA) as coadsorber are necessary to attain the overall improved PCE. TiCl4 surface treatment on both FTO and TiO2 has been found to outperform as compared to their untreated counterparts owing to the suppression of the charge recombination. DSSCs with an optimized TiO2 thickness of 6 μm and CDCA concentration of 4 mM have exhibited best performance due to enhanced photon harvesting and reduced dye aggregation, respectively.12th International Conference on Nanomolecular Electronics (ICNME-2016), December 14-16, 2016, Kobe International Conference Center, Kobe, Japa