Shape-control growth of 2D-In2Se3 with out-of-plane ferroelectricity by chemical vapor deposition.

For potential applications in ferroelectric switching and piezoelectric nano-generator devices, the promising ferroelectric properties of two dimensional (2D) layered In2Se3 attracted much attention. In the present study, 2D In2Se3 flakes down to monolayers are grown by the chemical vapor deposition (CVD) technique on a mica substrate with their structural, optical and ferroelectric properties being studied. The effect of growth parameters (time of growth and Ar flow rate) on the shape and size of the deposited flakes was studied. The optical microscopy study revealed that the flake changed from a circular shape to a sharp face triangle as the Ar flow rate and growth time increased. Raman spectroscopy and high-resolution scanning transmission electron microscopy (HR-STEM) studies revealed that the flakes were of α and ß phases, each of which has a hexagonal crystal structure. Strong second harmonic generation (SHG) was observed from α-In2Se3, demonstrating its non-centrosymmetric structure. The piezo-force microscopic (PFM) study showed the presence of out of plane (OOP) ferroelectricity with no in plane (IP) ferroelectricity in CVD grown α-In2Se3 indicating its vertically confined piezoresponse, which was tuned by the applied electric bias and the flake thickness. The present result of shape-controlled growth of In2Se3 with OOP ferroelectricity would open new pathways in the field of 2D ferroelectric switching devices.

Computer assisted optical screening of human ovarian cancer using Raman spectroscopy.

Conventional screening tools for ovarian cancer such as cancer antigen (CA-125) and trans-pelvic ultrasound have poor sensitivity and specificity, indicating the need for better and more reliable screening methodologies. Here, we investigate the capability of Raman spectroscopy as a screening technique for ovarian cancer. Raman spectra from the blood serum of healthy control and ovarian cancer subjects were measured. Highly significant Raman shifts (p<0.0001) and intensity variations were observed in the cancer group as compared to the healthy group. These spectral differences were exploited by support vector machine classifier towards computer assisted classification. Calculated evaluation metrics such as sensitivity (=90), specificity (=100), positive predictive value (=100) and negative predictive value (=87.5) for such classification indicated that these results are promising, with potential future application of Raman spectroscopy for ovarian cancer screening.

Optical diagnosis of malaria infection in human plasma using Raman spectroscopy.

We present the prediction of malaria infection in human plasma using Raman spectroscopy. Raman spectra of malaria-infected samples are compared with those of healthy and dengue virus infected ones for disease recognition. Raman spectra were acquired using a laser at 532 nm as an excitation source and 10 distinct spectral signatures that statistically differentiated malaria from healthy and dengue-infected cases were found. A multivariate regression model has been developed that utilized Raman spectra of 20 malaria-infected, 10 non-malarial with fever, 10 healthy, and 6 dengue-infected samples to optically predict the malaria infection. The model yields the correlation coefficient r(2) value of 0.981 between the predicted values and clinically known results of trainee samples, and the root mean square error in cross validation was found to be 0.09; both these parameters validated the model. The model was further blindly tested for 30 unknown suspected samples and found to be 86% accurate compared with the clinical results, with the inaccuracy due to three samples which were predicted in the gray region. Standard deviation and root mean square error in prediction for unknown samples were found to be 0.150 and 0.149, which are accepted for the clinical validation of the model.