45 research outputs found
Peierls barrier characteristic and anomalous strain hardening provoked by dynamic-strain-aging strengthening in a body-centered-cubic high-entropy alloy
The temperature effect on the mechanical behavior of the HfNbTaTiZr high entropy alloy (HEA) was investigated at 77–673 K. The decrease of the yield strength with increasing the temperature was mechanistically analyzed by considering contributions from various strengthening mechanisms. An anomalous dependence of strain hardening on temperature was observed and was justified to be caused by dynamic strain aging (DSA) as an extra strengthening mechanism at elevated temperatures. A model was constructed to split the overall strain hardening into forest hardening and DSA hardening, both of which were theoretically quantified at all temperatures considered.
The work quantifies the height of Peierls barriers in the bcc HfNbTaTiZr HEA, and reveals dynamic strain aging as the strengthening mechanism causing the anomalous strain hardening at elevated temperatures
Identifying the Electronic Properties of Grain Boundaries in CdTe Thin-film Solar Cells Using Electron Backscatter Diffraction and Electron Beam Induced Current Techniques
Atom-probe study of Cu and NiAl nanoscale precipitation and interfacial segregation in a nanoparticle-strengthened steel
The Cu and NiAl nanoscale precipitation and interfacial segregation in the martensite and austenite phases within a high-strength steel were studied by atom-probe tomography (APT). In the martensite phase, APT reveals the precipitation of isolated NiAl nanoparticles and NiAl/Cu co-precipitates, indicating that NiAl nanoparticles form first in the precipitation sequence. In comparison, the austenite phase contains only Cu nanoparticles with Ni segregation at the particle/matrix interface, in which the Ni segregation reduces the Cu nanoparticle interfacial energy. In addition, Mn and C exhibit an enrichment at the martensite/austenite interface, and the mechanism for the interfacial segregation was also discussed
Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach
Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data. © 2016 Elsevier Inc. All rights reserved.3
Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach
Nano-scale insights regarding coke formation in zeolite SSZ-13 subject to the methanol-to-hydrocarbons reaction
The methanol-to-hydrocarbons (MTH) process, commonly catalyzed by zeolites, is of great commercial interest and therefore widely studied both in industry and academia. However, zeolite-based catalyst materials are notoriously hard to study at the nano-scale. Atom probe tomography (APT) is uniquely positioned among the suite of characterization techniques, as it can provide 3D chemical information with sub-nm resolution. In this work, we have used APT to study the nano-scale coking behavior of zeolite SSZ-13 and its relation to bulk coke formation on the macro-/micro-scale studied with operando and in situ UV-vis spectroscopy and microscopy. Radial distribution function analysis (RDF) of the APT data revealed short carbon–carbon length scale affinities, consistent with the formation of larger aromatic molecules (coke species). Using nearest neighbor distribution (NND) analysis, an increase in the homogeneity of carbon was found with increasing time-on-stream. However, carbon clusters could not be isolated due to spatial noise and limited clustering. Therefore, it was found that the coke formation in zeolite SSZ-13 (CHA) is reasonably homogeneous on the nano-scale, and is rather similar to the silicoaluminophosphate analogue SAPO-34 (CHA) but different in nano-scale coking behavior compared to previously studied zeolite ZSM-5 (MFI)
Nano-scale insights regarding coke formation in zeolite SSZ-13 subject to the methanol-to-hydrocarbons reaction
The methanol-to-hydrocarbons (MTH) process, commonly catalyzed by zeolites, is of great commercial interest and therefore widely studied both in industry and academia. However, zeolite-based catalyst materials are notoriously hard to study at the nano-scale. Atom probe tomography (APT) is uniquely positioned among the suite of characterization techniques, as it can provide 3D chemical information with sub-nm resolution. In this work, we have used APT to study the nano-scale coking behavior of zeolite SSZ-13 and its relation to bulk coke formation on the macro-/micro-scale studied with operando and in situ UV-vis spectroscopy and microscopy. Radial distribution function analysis (RDF) of the APT data revealed short carbon–carbon length scale affinities, consistent with the formation of larger aromatic molecules (coke species). Using nearest neighbor distribution (NND) analysis, an increase in the homogeneity of carbon was found with increasing time-on-stream. However, carbon clusters could not be isolated due to spatial noise and limited clustering. Therefore, it was found that the coke formation in zeolite SSZ-13 (CHA) is reasonably homogeneous on the nano-scale, and is rather similar to the silicoaluminophosphate analogue SAPO-34 (CHA) but different in nano-scale coking behavior compared to previously studied zeolite ZSM-5 (MFI)