Observation of Size-Dependent Electron-Phonon Scattering and Temperature-Dependent Photoluminescence Quenching in Triangular-Shaped Silver Nanoparticles

Research studies on plasmonic properties of triangular-shaped silver nanoparticles might lead to several interesting applications. However, in this work, triangular-shaped silver nanoparticles have been synthesized by simple solvothermal technique and reported the effect of size on the electron-phonon scattering in the synthesized materials by analyzing their temperature-dependent photoluminescence (PL) emission characteristics. It has been observed that total integrated PL emission intensity is quenched by 33 % with the increase in temperature from 278 to 323 K. The observed decrease in PL emission intensity has been ascribed to the increase of electron-phonon scattering rate with the increase in temperature. The values of electron-phonon coupling strength (S) for synthesized samples have been evaluated by theoretical fitting of the experimentally obtained PL emission data. Smaller sized triangular nanoparticle has been found to exhibit stronger temperature dependence in PL emission, which strongly suggests that smaller sized triangular silver nanostructures have better electron-phonon coupling.

IMAGE DENOISING IN MULTIPLICATIVE NOISE

We address the problem of denoising images corrupted by multiplicative noise. The noise is assumed to follow a Gamma distribution. Compared with additive noise distortion, the effect of multiplicative noise on the visual quality of images is quite severe. We consider the mean-square error (MSE) cost function and derive an expression for an unbiased estimate of the MSE. The resulting multiplicative noise unbiased risk estimator is referred to as MURE. The denoising operation is performed in the wavelet domain by considering the image-domain MURE. The parameters of the denoising function (typically, a shrinkage of wavelet coefficients) are optimized for by minimizing MURE. We show that MURE is accurate and close to the oracle MSE. This makes MURE-based image denoising reliable and on par with oracle-MSE-based estimates. Analogous to the other popular risk estimation approaches developed for additive, Poisson, and chi-squared noise degradations, the proposed approach does not assume any prior on the underlying noise-free image. We report denoising results for various noise levels and show that the quality of denoising obtained is on par with the oracle result and better than that obtained using some state-of-the-art denoisers.