PARTICLES SIZE AND LATTICE STRAIN EFFECT ON THE OPTICAL CONSTANTS OF Fe3O4 NANOPARTICLES

In the case of optical sensors such as the Surface Plasmon Resonance (SPR) sensor, the Fe3O4 nanoparticles play a role to boost the signal however they can increase the detection sensitivity of the biosensor. For this application, the optical properties of Fe3O4 nanoparticles need to be studied. The optical properties are described in terms of their optical constants. Therefore, this work was purposed to study the effect of the particle size and lattice strain on the optical constants of Fe3O4 nanoparticles. Samples were synthesized by using the coprecipitation technique. Two calcination temperatures, i.e., 150oC and 250oC for 4 hours were applied to the samples. Samples were characterized for their diffraction pattern and optical properties by using XRD and Specular UV-Vis Spectroscopy technique, consecutively. The particle size and lattice strain were estimated by using the Williamson-Hall (W-H) method. The effect of the particle size and their optical constants on the reflectance curve in the SPR sensor application was also performed toward a simulation by using Winspall 3.02 software. The results show that calcination temperature causes an increase in particle size and a decrease in lattice strain. The optical constants, such as absorbance (A), absorption coefficient (α), extinction coefficient (k), refractive index (n), dielectric constants (ε), optical conductivity (σ), and the Urbach energy (Eu) significantly depended on particles size and lattice strain. However, the particle size and optical constant were significantly influent the SPR angle in the reflectance curve of Fe3O4

Creation of Laboratory Spectral Library of Beryl in Sevapure Area, Kadavur Basin, Karur District, Tamilnadu

The study aims to create a laboratory spectral library for the mineral beryl by using the spectroradiometer instrument and the Envi software. The mineral collected from Sevapure area of Kadavur Basin. The laboratory spectra were created and matched with the USGS spectral library. For the study of the spectra an imagery file platform is needed and the ASTER data used as the platform. The laboratory spectra formed between 0.556 to 2.4-micrometre wavelengths. The spectral reflectance curve shows the reflectance values from 0.09 to 0.33 micrometres and the high absorption takes place at 0.80 μm. The USGS spectra show the same value at the wavelength and the reflectance value changes as 0.55. Both shows maximum reflectance at 1.00 and the wavelength at 1.65 μm. The spectral library created has used for further studies.

Identification of Geospatial Objects Using Spectral Pattern

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

Characterization and Identification of Coal and Carbonaceous Shale Using Visible and Near-Infrared Reflectance Spectroscopy

Because of the high organic carbon concentration in carbonaceous shale, a large proportion of carbonaceous shales are often misclassified into coals using visible and near-infrared (VIS-NIR) reflectance spectroscopy in the field of coal-gangue identification of hyperspectral remote sensing of coal mine. In order to study spectral characterization of coal and carbonaceous shale, three bituminite samples and three carbonaceous shales were collected from a coal mine of China, and their spectral reflectance curves were obtained by a field spectrometer in the wavelength range of 350–2500 nm. Only one carbonaceous shale could be easily identified from the three bituminite samples according to obvious absorption valleys near 1400 nm, 1900 nm, and 2200 nm of its reflectance curve while the other two carbonaceous shales have similar reflectance curves to the three bituminite samples. The effect of carbon concentration on reflectance curve was simulated by the mixed powder of ultralow ash bituminite and clay in 0.5 mm grain size under various mixing ratios. It was found that absorption valleys near 1400 nm, 1900 nm, and 2200 nm of the mixed powder become not obvious when the bituminite content is more than 30%. In order to establish an effective identification method of coal and carbonaceous shale, 250 other samples collected from the same coal mine were divided into 150 training samples and 100 prediction samples. Principal component analysis (PCA) and Gauss radial basis kernel principal component analysis (GRB-KPCA) were employed to extract principal components (PCs) of continuum removed (CR) spectra of the training samples in eight selected wavelength regions which are related to the main mineral and organic compositions. Two support vector machine- (SVM-) based models PCA-SVM and GRB-KPCA-SVM were established. The results showed that the GRB-KPCA-SVM model had better identification accuracies of 94% and 92% for powder and nature block prediction samples, respectively.

Theoretical and Practical Analysis of Fiber Blend Model in Gray Spun Yarn

The final color of blend yarns is dependent on the proportion of the various colored fibers used in colored spun yarns. How to scientifically and effectively quantify the relationship between the proportion of different colored fibers and the color of the yarn is a major problem hindering the development of color spinning technology. This paper suggests three methods for analyzing and comparing the color of gray spun yarns. A color match software, Datacolor Match, was used to calculate a recipe simulating the match of dyes; the a predicting formula based on color mixing model of Kubelka-Munk (K-M) double-constant theory for fiber blends was established. Finally, the colored fiber formula based on the known reflectance curve was calculated according to the relationship between the reflectance value of gray spun yarn and proportion of colored fiber present. This work provides an understanding of colored spun yarn fabrication.

Identification of Geospatial Objects Using Spectral Pattern

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.