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Ge clusters are grown on Si substrate at room temperature (Ge-RT) and also at liquid nitrogen temperature (Ge-LNT) by cluster evaporation technique. These clusters show blue luminescence. Raman measurement demonstrates the increase in strain with annealing in diffused disordered Si at the interface between Ge-LNT clusters and Si substrate. This manifests in strain-relaxation in the clusters as observed by Photoluminescence (PL) measurements. The decrease in PL intensity for Ge-RT with annealing has been attributed to reduction in surface oxide species, which is supported by Raman spectroscopic measurements. The objective of the paper is to understand the effect of thermal annealing on both interfacial strain and interdiffusion of elemental Si at the interface, together with luminescence characteristics of the clusters. [Copyright &y& Elsevier]

This article reports a comprehensive study on damage evolution in undoped InP due to 100 MeV silver (Ag 7+ ) ion irradiation. Raman and Rutherford back scattering channeling (c-RBS) measurements are employed to bring out two different aspects of disorder in the system. While Raman spectroscopy revealed a monotonic increase in sub-surface strain due to ion irradiation, the later detected heavy damage in the wafer at high fluences. Furthermore, we have estimated the lattice temperature under above irradiation conditions in InP using a unified thermal spike model. The model includes both inelastic thermal spike and elastic collision spike along with the effect of latent heat. This model explains the observed damage evolution of the target. Above experimental techniques in conjunction with the simulation model provides a qualitative explanation of the damage accumulation leading to structural modification in the Ag ion-irradiated InP wafer. [ABSTRACT FROM AUTHOR]

[Display omitted] • Resonance Raman spectroscopy is employed for diagnosis of diabetic retinopathy. • Statistical analysis of spectral data holds promise in early detection of the disease. • The study endorses carotenoid level in human serum as a biomarker for the pathosis. Diabetic retinopathy (DR) is a common health concern. Unfortunately, the metabolic pathway causing DR is yet to be understood. The carotenoid level in the human body is known to protect the health of the eyes. In this work, resonance Raman spectroscopy and multivariate analysis of the spectral data of human serum are reported as next-generation spectropathologic tools to detect retinal degeneration efficiently. The proposed technique shows promise by endorsing ocular carotenoids as a critical biomarker for such pathosis. Furthermore, the multivariate analysis of the spectral data distinguishes between two different stages of the disease. The machine learning algorithm is used to estimate a significant accuracy of 94% of the proposed model for the classification. As the carotenoid level can be controlled by dietary intake, we believe that the reported results also indicate a therapeutic role of the same in DR. [ABSTRACT FROM AUTHOR]