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We report plasma parameters of laser ablated silicon plasma using the fundamental (1064 nm) and second harmonics (532 nm) of a Nd : YAG laser. The electron temperature and electron number density are evaluated using the Boltzmann plot method and Stark broadened line profile, respectively. The electron temperature and electron number density are deduced using the same laser irradiance 2-16 GW cm −2 for 1064 nm and 532 nm as 6350-7000 K and (3.42-4.44) × 10 16 cm −3 and 6000-6400 K and (4.20-5.72) × 10 16 cm −3 , respectively. The spatial distribution of plasma parameters shows a decreasing trend of 8200-6300 K and (4.00-3.60) × 10 16 cm −3 for 1064 nm and 6400-5500 K and (5.10-4.50) × 10 16 cm −3 for 532 nm laser ablation. Furthermore, plasma parameters are also investigated at low pressure from 45 to 550 mbar, yielding the electron temperature as 4580-5535 K and electron number density as (1.51-2.12) × 10 16 cm −3 . The trend of the above-mentioned results is in good agreement with previous investigations. However, wavelength-dependent studies and the spatial evolution of plasma parameters have been reported for the first time.

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This study is intended to develop a screening method for female breast cancer (BRC) from whole blood using Raman spectroscopy. A multivariate partial least squares (PLS) regression model is developed which is based upon Raman spectra of BRC-positive and healthy participants. It yields coefficients of regression at the corresponding Raman shifts. These coefficients represent the changes in molecular structures which are associated with the progress of disease. The present study pointed out some specific molecules which differentiated BRC-positive and healthy groups. In the BRC-positive group, a rising trend of calcium oxalate, calcium hydroxyapatite, phosphatidylserine and qunoid ring, and a lowering trend of tryptophan, tyrosine, and proline were observed in PLS-based coefficients of regression. The R-square value of the model was found to be 0.987, which is accepted clinically. The model was tested for the prediction of 50 randomly collected samples at a cutoff value of 0.5 with the gray region defined in the range of 0.4-0.6. Goodness of fit was estimated using accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under ROC curve. All of these parameters were found to be very promising.

In the present work, the experimentally determined oscillator strengths of the 3p 2 P → nd D 2 (16 ≤ n ≤ 43) Rydberg transitions of lithium have been reported for the first time. The experiments were performed using two dye lasers simultaneously pumped by the second harmonic (532 nm) of an Nd:YAG laser, and the vapor containment and detection system was a thermionic diode ion detector. The first frequency-doubled dye laser excites the groundstate atoms to the 3p 2 P excited state at 325.35 nm, and the second dye laser was scanned up to the first ionization threshold. The f -values of the aforementioned transitions have been determined using the threshold value of the photoionization cross section (30 4.8 Mb) from the 3p 2 P excited state. A complete picture of the oscillator strengths from n 3 to 43 has been presented, including the previously known oscillator strength and determined in this work.

This study presents the optical screening of hepatitis C and its associated molecular changes in human blood sera using a partial least-squares regression model based on their Raman spectra. In total, 152 samples were tested through enzyme-linked immunosorbent assay for confirmation. This model utilizes minor spectral variations in the Raman spectra of the positive and control groups. Regression coefficients of this model were analyzed with reference to the variations in concentration of associated molecules in these two groups. It was found that trehalose, chitin, ammonia, and cytokines are positively correlated while lipids, beta structures of proteins, and carbohydrate-binding proteins are negatively correlated with hepatitis C. The regression vector yielded by this model is utilized to predict hepatitis C in unknown samples. This model has been evaluated by a cross-validation method, which yielded a correlation coefficient of 0.91. Moreover, 30 unknown samples were screened for hepatitis C infection using this model to test its performance. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve from these predictions were found to be 93.3%, 100%, 96.7%, and 1, respectively.

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