Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Gold nanoparticles (2)
- Machine learning (2)
- Materials science (2)
- Physics (2)
- Artificial intelligence (1)
-
- Ceria (1)
- Ceria nanoparticles (1)
- Chemistry (1)
- Condensed matter physics (1)
- Dissolved oxygen (1)
- Engineering (1)
- Fluorescence (1)
- Fluorescence quenching (1)
- Hospitality (1)
- Linear accelerators (1)
- Metal-organic framework (1)
- Operability (1)
- Optics (1)
- Polymers (1)
- Quantum dots (1)
- Reliability (1)
- Science & Technology (1)
- Seebeck coefficient (1)
- Sensing (1)
- Superconducting rf (1)
- Superconductivity and superfluidity (1)
- Thin-films (1)
Articles 1 - 5 of 5
Full-Text Articles in Physics
In-Situ Gold-Ceria Nanoparticles: Superior Optical Fluorescence Quenching Sensor For Dissolved Oxygen, Nader Shehata, Ishac Kandas, Effat Samir
In-Situ Gold-Ceria Nanoparticles: Superior Optical Fluorescence Quenching Sensor For Dissolved Oxygen, Nader Shehata, Ishac Kandas, Effat Samir
Electrical & Computer Engineering Faculty Publications
Cerium oxide (ceria) nanoparticles (NPs) have been proved to be an efficient optical fluorescent material through generating visible emission (~530 nm) under violet excitation. This feature allowed ceria NPs to be used as an optical sensor via the fluorescence quenching Technique. In this paper, the impact of in-situ embedded gold nanoparticles (Au NPs) inside ceria nanoparticles was studied. Then, gold–ceria NPs were used for sensing dissolved oxygen (DO) in aqueous media. It was observed that both fluorescence intensity and lifetime were changed due to increased concentration of DO. Added gold was found to enhance the sensitivity of ceria to DO …
Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin
Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin
Electrical & Computer Engineering Faculty Publications
This guest editorial summarizes the Special Section on Machine Learning in Optics.
Gold/Qds-Embedded-Ceria Nanoparticles: Optical Fluorescence Enhancement As A Quenching Sensor, Nader Shehata, Effat Samir, Ishac Kandas
Gold/Qds-Embedded-Ceria Nanoparticles: Optical Fluorescence Enhancement As A Quenching Sensor, Nader Shehata, Effat Samir, Ishac Kandas
Electrical & Computer Engineering Faculty Publications
This work focuses on improving the fluorescence intensity of cerium oxide (ceria) nanoparticles (NPs) through added plasmonic nanostructures. Ceria nanoparticles are fluorescent nanostructures which can emit visible fluorescence emissions under violet excitation. Here, we investigated different added plasmonic nanostructures, such as gold nanoparticles (Au NPs) and Cadmium sulfide/selenide quantum dots (CdS/CdSe QDs), to check the enhancement of fluorescence intensity emissions caused by ceria NPs. Different plasmonic resonances of both aforementioned nanostructures have been selected to develop optical coupling with both fluorescence excitation and emission wavelengths of ceria. In addition, different additions whether in-situ or post-synthesis have been investigated. We found …
Thermoelectric Porous Mof Based Hybrid Materials, Engelbert Redel, Helmut Baumgart
Thermoelectric Porous Mof Based Hybrid Materials, Engelbert Redel, Helmut Baumgart
Electrical & Computer Engineering Faculty Publications
Porous hybrid materials and MOF (Metal-Organic-Framework) films represent modern designer materials that exhibit many requirements of a near ideal and tunable future thermoelectric (TE) material. In contrast to traditional semiconducting bulk TE materials, porous hybrid MOF templates can be used to overcome some of the constraints of physics in bulk TE materials. These porous hybrid systems are amenable for simulation and modeling to design novel optimized electron-crystal phonon-glass materials with potentially very high ZT (figure of merit) numbers. Porous MOF and hybrid materials possess an ultra-low thermal conductivity, which can be further modulated by phonon engineering within their complex porous …
Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning At Jefferson Laboratory, Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin
Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning At Jefferson Laboratory, Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin
Electrical & Computer Engineering Faculty Publications
We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through five passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level rf system configured such that a cavity fault triggers waveform recordings of 17 rf signals for each of the eight cavities in the cryomodule. Subject matter experts are able to analyze the collected time-series data and identify which of the …