Tailoring light-sound interactions in a single mode fiber for the high-power transmission or sensing applications

A full-vectorial numerically efficient Finite Element Method (FEM) based computer code is developed to study complex light-sound interactions in a single mode fiber (SMF). The SBS gain or SBS threshold in a fiber is highly related to the overlap between the optical and acoustic modes. For a typical SMF the acoustic-optic overlap strongly depends on the optical and acoustic mode profiles and it is observed that the acoustic mode is more confined in the core than the optical mode and reported overlap is around 94 % between these fundamental optical and acoustic modes. However, it is shown here that selective co-doping of Aluminum and Germanium in core reduces the acoustic index while keeping the optical index of the same value and thus results in increased acoustic- optic overlap of 99.7%. On the other hand, a design of acoustic anti-guide fiber for high-power transmission systems is also proposed, where the overlap between acoustic and optical modes is reduced. Here, we show that by keeping the optical properties same as a standard SMF and introducing a Boron doped 2ndlayer in the cladding, a very low value of 2.7% overlap is achieved. Boron doping in cladding 2ndlayer results in a high acoustic index and acoustic modes shifts in the cladding from the core, allowing much high power delivery through this SMF

Implications of a Quantum Mechanical Treatment of the Universe

We attempt to treat the very early Universe according to quantum mechanics. Identifying the scale factor of the Universe with the width of the wave packet associated with it, we show that there cannot be an initial singularity and that the Universe expands. Invoking the correspondence principle, we obtain the scale factor of the Universe and demonstrate that the causality problem of the standard model is solved.Comment: LaTex, 5 pages, 1 figure, to be published in Mod. Phys. Lett.

Design of On-chip Hybrid Plasmonic Mach-Zehnder Interferometer for Temperature and Concentration Detection of Chemical Solution

We report a compact lab-on-chip design of a Mach-Zehnder interferometer (MZI) incorporating a novel metal strip loaded horizontal slot hybrid plasmonic waveguide (HSHPW) in the sensing arm and a dielectric horizontal slot (DHS) waveguide in the reference arm. The HSHPW is optimized to confine a high ∼60% and ∼82% evanescent optical field in the low index dielectric slot and an active sensing region, respectively which enhance the device sensitivity with a comparative lower propagation loss than a typical plasmonic waveguide. We report here a single MZI configuration which not only exhibits an excellent temperature sensitivity of 243.9 pm/°C but also liquid concentration sensitivity of 437.3 nm/RIU for a 40 μm long HSHPW. To mitigate loss arising from each section such as butt coupling and plasmonic modal losses, the HSHPW has been optimized by incorporating an asymmetric power splitter which shows a considerable improvement in the fringe visibility and device insertion loss. Thus, the proposed single MZI design shows an excellent response to the temperature and liquid concentration sensing with a maximum total loss and extinction ratio of 2.56 dB and>25 dB, respectively. A much simpler CMOS friendly compact design is also found to have a great robustness to the fabrication tolerances

Pulse-induced acoustoelectric vibrations in surface-gated GaAs-based quantum devices

We present the results of a numerical investigation which show the excitation of acoustoelectric modes of vibration in GaAs-based heterostructures due to sharp nano-second electric-field pulses applied across surface gates. In particular, we show that the pulses applied in quantum information processing applications are capable of exciting acoustoelectric modes of vibration including surface acoustic modes which propagate for distances greater than conventional device dimensions. We show that the pulse-induced acoustoelectric vibrations are capable of inducing significant undesired perturbations to the evolution of quantum systems.Comment: To be published in Phys. Rev.

Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

Machine learning is an application of artificial intelligence that focuses on the development of computer algorithms which learn automatically by extracting patterns from the data provided. Machine learning techniques can be efficiently used for a problem with a large number of parameters to be optimized and also where it is infeasible to develop an algorithm of specific instructions for performing the task. Here, we combine the finite element simulations and machine learning techniques for the prediction of mode effective indices, power confinement and coupling length of different integrated photonics devices. Initially, we prepare a dataset using COMSOL Multiphysics and then this data is used for training while optimizing various parameters of the machine learning model. Waveguide width, height, operating wavelength, and other device dimensions are varied to record different modal solution parameters. A detailed study has been carried out for a slot waveguide structure to evaluate different machine learning model parameters including number of layers, number of nodes, choice of activation functions, and others. After training, this model is used to predict the outputs for new input device specifications. This method predicts the output for different device parameters faster than direct numerical simulation techniques. Absolute percentage error of less than 5% in predicting an output has been obtained for slot, strip and directional waveguide coupler designs. This study pave the step towards using machine learning based optimization techniques for integrated silicon photonics devices

Enhancing mode stability of higher order modes in a multimode fiber

An innovative strategy to increase the modal stability of the higher order modes of multimode fiber is proposed where the modal stability is increased by more than 80% between LP05 and its neighboring antisymmetric modes

Efficient strategy to increase higher order inter-modal stability of a step index multimode fiber

We demonstrate a novel approach to enhance the mode stability through increased effective index difference (Δneff) between the higher-order modes (LP18, LP09 and LP19) of a multimode fiber. Fibers with large diameters have bigger effective mode areas (Aeff) and can be useful for high power lasers and amplifiers. However, a large mode area (LMA) results in an increased number of modes that can be more susceptible to mode coupling. The modal effective index difference (Δneff) strongly correlates with mode stability and this increases as the modal order (m) increases. We report here that the mode spacing between the higher order modes can be further enhanced by introducing doped concentric rings in the core. In our work, we have shown a more than 35% increase in the mode spacing between the higher order modes by optimizing the doping profile of a LMA fiber. The proposed design technique is also scalable and can be applied to improve the mode spacing between different higher order modes and their neighboring antisymmetric modes, as necessary

Oral cancer secretome: Identification of cancer-associated proteins

This study aims to identify cancer-associated proteins in the secretome of oral cancer cell lines. We have successfully established four primary cell cultures of normal cells with a limited lifespan without human telomerase reverse transcriptase (hTERT) immortalization. The secretome of these primary cell cultures were compared with that of oral cancer cell lines using 2DE. Thirty five protein spots were found to have changed in abundance. Unambiguous identification of these proteins was achieved by MALDI TOF/TOF. In silico analysis predicted that 24 of these proteins were secreted via classical or nonclassical mechanisms. The mRNA expression of six genes was found to correlate with the corresponding protein abundance. Ingenuity Pathway Analysis (IPA) core analysis revealed that the identified proteins were relevant in, and related to, cancer development with likely involvements in tumor growth, metastasis, hyperproliferation, tumorigenesis, neoplasia, hyperplasia, and cell transformation. In conclusion, we have demonstrated that a comparative study of the secretome of cancer versus normal cell lines can be used to identify cancer-associated proteins.Article Link: http://onlinelibrary.wiley.com/doi/10.1002/elps.201300126/abstrac