We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and equivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces.
View Article and Find Full Text PDFMotivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e.e/Å) range for object detection and segmentation tasks with neural networks.
View Article and Find Full Text PDFIn the framework of first-principles calculations, we comprehensively investigate the external electric-field (EF) manipulation of the magnetic anisotropy energy (MAE) of alloyed CoPt dimers deposited on graphene. In particular, we focus on the possibility of tuning the MAE barriers under the action of external EFs and on the effects of Co-substitution. Among the various considered structures, the lowest-energy configurations were the and , having the Co-atom closest to the graphene layer.
View Article and Find Full Text PDFClin Nutr
December 2014
Background: Nutritional interventions have shown increased energy intake but not improvement in health-related quality of life (HRQL) or prognosis in non small cell lung cancer (NSCLC) patients. Eicosapentaenoic acid has been proposed to have anti-inflammatory, anticachectic and antitumoural effects.
Objective: To compare the effect of an oral EPA enriched supplement with an isocaloric diet on nutritional, clinical and inflammatory parameters and HRQL in advanced NSCLC patients.