Nonlinear interferometric vibrational imaging

A method of examining a sample, which includes: exposing a reference to a first set of electromagnetic radiation, to form a second set of electromagnetic radiation scattered from the reference; exposing a sample to a third set of electromagnetic radiation to form a fourth set of electromagnetic radiation scattered from the sample; and interfering the second set of electromagnetic radiation and the fourth set of electromagnetic radiation. The first set and the third set of electromagnetic radiation are generated from a source; at least a portion of the second set of electromagnetic radiation is of a frequency different from that of the first set of electromagnetic radiation; and at least a portion of the fourth set of electromagnetic radiation is of a frequency different from that of the third set of electromagnetic radiation

Optimizing density, dynamic viscosity, thermal conductivity and specific heat of a hybrid nanofluid obtained experimentally via ANFIS-based model and modern optimization

In this study, rGO/Co3O4 nanocomposite was synthesized, characterized, and then the thermophysical properties were obtained experimentally, after which the experimental data at varying values of temperature and particle loadings was used for optimization purposes. The study was concerned with different values of the controlling parameters. The in-situ/chemical reduction technique was used to synthesize the rGO/Co3O4 nanocomposite and then characterized with x-ray diffraction, transmission electron microscope, and magnetometry. The system was studied at temperature values ranging at 20, 30, 40, 50, and 60 °C and with particle loadings of 0.05%, 0.1%, and 0.2% wt%. The authors in this article have introduced a novel population-based algorithm that is known as Marine Predators Algorithm to obtain the optimal values of the controlling parameters (i.e., temperature and nanofluid mixture percentage) that minimize two controlled variables (i.e., density and viscosity) as well as maximize the other two controlled variables (thermal conductivity and specific heat). The rGO/Co3O4 nanocomposite nanofluid thermal conductivity and viscosity were investigated experimentally, and a maximum increment of 19.14% and 70.83% with 0.2% particle loadings at 60 °C was obtained. At 0.05%, 0.1%, and 0.2% particle loading wt%, the density increased by 0.115%, 0.23%, and 0.451% at a temperature of 20 °C; simultaneously, density increased by 0.117%%, 0.235%, and 0.469% at 60 °C, respectively as compared to water. At 0.2 wt%, the maximum decreased specific heat was 0.192% and 0.194% at 20 °C and 60 °C. When compared with water, no effect was observed with an increase in temperature/: a similar trend as that of the water was followed. The optimal values were found to be at a temperature of 60 °C and for 0.05% particle loading of the prepared nanofluid. However, among the conducted experiments, the optimizer pointed out that the optimal experiment was the one conducted at a temperature of 60 °C and a nanofluid percentage at 0.05. In conclusion, the proposed methodology of modelling with an artificial intelligence tool such as an adaptive network-based fuzzy inference system technique and then determining the optimal parameters with the marine predators algorithm accomplished the goal of the study with major success.publishe

Distinguishing non-resonant four-wave-mixing noise in coherent stokes and anti-stokes Raman scattering

A method of examining a sample comprises exposing the sample to a pump pulse of electromagnetic radiation for a first period of time, exposing the sample to a stimulant pulse of electromagnetic radiation for a second period of time which overlaps in time with at least a portion of the first exposing, to produce a signal pulse of electromagnetic radiation for a third period of time, and interfering the signal pulse with a reference pulse of electromagnetic radiation, to determine which portions of the signal pulse were produced during the exposing of the sample to the stimulant pulse. The first and third periods of time are each greater than the second period of time

Functional Nanostructure Synthesis and Properties

The dissertation addressed challenges in the nanostructure synthesis, applied the materials to engineering fields, such as lithium battery material, fluorescent and magnetic drug deliveries; and developed new characterization methods to better understand particle properties and formation mechanisms

Developing magnetic functionalized multi-walled carbon nanotubes-based buckypaper for the removal of Furazolid

Magnetic f-MWCNTs-based BP/PVA membrane was fabricated and utilized for the elimination of furazolidone (FZD) from aqueous solution. Characterisation and adsorption studies were performed to evaluate the performance and adsorptive efficiency, respectively of the membrane. Furthermore, statistical and machine learning technique were also applied to predict the removal efficiency of FZD on the membrane. The results revealed that magnetic f-MWCNTs-based BP/PVA membrane has the potential to be used as an efficient membrane for practical applications

1996 Laboratory directed research and development annual report

This report summarizes progress from the Laboratory Directed Research and Development (LDRD) program during fiscal year 1996. In addition to a programmatic and financial overview, the report includes progress reports from 259 individual R&D projects in seventeen categories. The general areas of research include: engineered processes and materials; computational and information sciences; microelectronics and photonics; engineering sciences; pulsed power; advanced manufacturing technologies; biomedical engineering; energy and environmental science and technology; advanced information technologies; counterproliferation; advanced transportation; national security technology; electronics technologies; idea exploration and exploitation; production; and science at the interfaces - engineering with atoms

Ag-Ag2Sコアシェル型ナノ粒子を用いたリザーバーコンピューティングデバイスの作製

The performance of the von Neumann computer was greatly improved by miniaturizing transistors and increasing the density according to Moore\u27s Law. However, in recent years, the maximum permissible number of CPU transistors has remained constant, and further performance improvements have not been possible. In today\u27s nanoscale era, scaling to smaller sizes represents a major challenge in device manufacturing, circuit, and system design and integration. On the other hand, nanoscale technology has the potential to develop new materials and devices with unique properties. Memristors exhibit nonlinear current-voltage characteristics and have unique memory characteristics. That is, such a new nanoscale device whose current state depends on the past. It has the potential to create new computing paradigms for both non-linear and memory characteristics of Memristors. The purpose of this paper is to investigate the possibility of using wet chemical synthesis and Ag-Ag2S core-shell nanoparticles to develop a new computing paradigm called “Reservoir Computing” (RC) which belong to such a new paradigm. However, it differs from the traditional Recurrent Neural Network (RNN) method in that the pre-processor (ie, the reservoir) is composed of nonlinear elements that are randomly connected repeatedly. This greatly reduces the complexity of learning. In this thesis, we reported RC devices with low power consumption. The synthesis conditions of Ag-Ag2S core-shell nanoparticles operating at low voltage were searched. Next, synthesis parameters such as Ag / S molar ratio were examined, to control the particle size. We confirmed that the nanoparticle agglomerates have nonlinear electrical conductivity necessary for the development of RC computations, such as constantly exhibiting hysteresis in the current-voltage (I-V) curve, and investigated other conditions necessary for RC hardware. Since the linear regression of the output channel was trained to fit the target waveform, the potential of the nanoparticle-based RC device was shown.九州工業大学博士学位論文 学位記番号:生工博甲第359号 学位授与年月日:令和元年12月27日1 Introduction and Literature Review|2 Methodology|3 Effect of various synthesis procedure to electrical characteristics of the nanoparticles-based device|4 Effect of the Ag-Ag2S volume ratio to the electrical properties|5 Switching mechanism of Ag-Ag2S nanoparticles-based device and neuromorphic learning properties|6 Recurrent neural network properties of Ag-Ag2S nanoparticles-based device and its application as reservoir computing|7 Conclusions and Suggestions九州工業大学令和元年