Search for: All records

A rigorous methodology is developed for computing elastic fields generated by experimentally observed defect structures within grains in a polycrystal that has undergone tensile extension. An example application is made using a near-field high energy x-ray diffraction microscope measurement of a zirconium sample that underwent$13.6\mathrm{\%}$tensile extension from an initially well-annealed state. (Sub)grain boundary features are identified with apparent disclination line defects in them. The elastic fields of these features identified from the experiment are calculated.

 

Abstract We investigate the impacts of the neutrino cooling mechanism inside the neutron star (NS) core on the light curves of type I X-ray bursts and X-ray superbursts. From several observations of NS thermal evolution, physical processes of fast neutrino cooling, such as the direct Urca (DU) process, are indicated. They significantly decrease the surface temperature of NSs, though the cooling effect could be suppressed by nucleon superfluidity. In the present study, focusing on the DU process and nucleon superfluidity, we investigate the effects of NS cooling on the X-ray bursts using a general-relativistic stellar-evolution code. We find that the DU process leads to a longer recurrence time and higher peak luminosity, which could be obstructed by the neutrons’ superfluidity. We also apply our burst models to the comparison with Clocked burster GS 1826−24, and to the recurrence time of a superburst triggered by carbon ignition. These effects are significant within a certain range of binary parameters and the uncertainty of the NS equation of state. 

This paper presents an innovative approach to improve engineering students’ problem-solving skills by implementing think-aloud exercises. Sometimes engineering students claim they do not know where to start with the problem-solving process, or they are not sure how to proceed to the next steps when they get stuck. A systematic training that focuses on the problem-solving process and the justification of each step could help. Think-aloud techniques help make the invisible mental processes visible to learners. Engineering think-aloud technique engages students and helps them make their way through a solving process step-by-step, reasoning along with them. In this study, a multiple faceted systematic approach that integrates think-aloud exercises through video assignments and oral exams were developed and implemented in two pilot engineering classes. We present our think-aloud exercises and oral exams structures in each of the courses and their impacts on students' learning outcomes, and students’ perceptions towards the pedagogical approach. Both quantitative and qualitative results show that the think-aloud exercise assignments and oral exams enhance students’ problem-solving skills and promote learning. 

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups—for example, amide, amino acid, and carboxylic acid—we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. 

Scintillators, one of the essential components in medical imaging and security checking devices, rely heavily on rare‐earth‐containing inorganic materials. Here, a new type of organic‐inorganic hybrid scintillators containing earth abundant elements that can be prepared via low‐temperature processes is reported. With room temperature co‐crystallization of an aggregation‐induced emission (AIE) organic halide, 4‐(4‐(diphenylamino) phenyl)‐1‐(propyl)‐pyrindin‐1ium bromide (TPA‐PBr), and a metal halide, zinc bromide (ZnBr₂), a zero‐dimensional (0D) organic metal halide hybrid (TPA‐P)₂ZnBr₄with a yellowish‐green emission peaked at 550 nm has been developed. In this hybrid material, dramatically enhanced X‐ray scintillation of TPA‐P⁺is achieved via the sensitization by ZnBr₄²⁻. The absolute light yield (14,700 ± 800 Photons/MeV) of (TPA‐P)₂ZnBr₄is found to be higher than that of anthracene (≈13,500 Photons/MeV), a well‐known organic scintillator, while its X‐ray absorption is comparable to those of inorganic scintillators. With TPA‐P⁺as an emitting center, short photoluminescence and radioluminescence decay lifetimes of 3.56 and 9.96 ns have been achieved. Taking the advantages of high X‐ray absorption of metal halides and efficient radioluminescence with short decay lifetimes of organic cations, the material design paves a new pathway to address the issues of low X‐ray absorption of organic scintillators and long decay lifetimes of inorganic scintillators simultaneously.