Computational modeling and neutron imaging to understand interface shape and solute segregation during the vertical gradient freeze growth of BaBrCl:Eu

Derby, Jeffrey J.; Zhang, Chang; Seebeck, Jan; Peterson, J.M.; Tremsin, Anton S.; Perrodin, Didier; Bizarri, Grégory; Bourret, Edith; Losko, Adrian S.; Vogel, Sven C.

doi:10.1016/j.jcrysgro.2020.125572

Cited by 7 publications

(9 citation statements)

References 82 publications

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“…The initial Eu concentrations, not shown in Figure 1(a), are uniform at 2.5% in the solid and 5% in the melt, consistent with the doping level in our previous BaBrCl:Eu crystal growth experiments [5,6] and the europium equilibrium distribution coefficient of k = 0.5.…”

Section: Crystal Growth and Eu Segregationsupporting

confidence: 87%

“…The physical properties employed in both computational models are listed in Table 1. Estimates of properties for the growth and segregation calculation are based on our prior work [6] and on recent experiments performed at Lawrence Berkeley National Laboratory (LBNL).…”

Section: Resultsmentioning

confidence: 99%

“…In previous studies, we have applied energy-resolved neutron radiography to image, in real time and in situ, the vertical gradient freeze growth of BaBrCl:Eu [4,5]. Following this work, we employed computational models to analyze the mechanisms that were responsible for the observed interface shape evolution and solute segregation in this experiment [6]. In related work on assessing the challenges for crystal growth of BaBrCl:Eu, Bourret et al [2,7] identified cracking as a potential problem that could affect the production of this material.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the real-time imaging of crystal growth described in [4][5][6], Tremsin et al [4,5,41] applied neutron transmission to perform tomographic interrogation of the grown crystals. Their results indicated a strong correlation between Eu distribution and cracking and that cracks originated from clusters where Eu concentration gradients were large.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of chemical stress and the propensity for cracking during the vertical Bridgman growth of BaBrCl:Eu

Zhang

Gao

Tremsin

et al. 2020

Journal of Crystal Growth

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Computational models are employed to analyze residual chemical stresses arising from compositional variations in europium-doped BaBrCl crystals grown by the vertical Bridgman method. We find that significant chemical stress is produced by radial segregation of Eu in this system. In particular, the distribution of normal stresses is set by the radial concentration gradient, whose changing sign produces surface states in tension or compression. Crack opening from surface flaws will be promoted or suppressed by tensile or compressive surface stresses, respectively. Thus, crystal growth processing strategies that change the radial dopant concentration gradients are posited to affect the propensity for cracking. For this system, surface stresses are changed from states of tension to compression when the growth rate is increased, thus improving the chances to avoid cracking-a strategy that defies classical wisdom that dictates slower growth to improve outcomes. Similar strategies affecting segregation may prove beneficial to tailor chemical stress fields to reduce cracking in other crystal growth systems.

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Section: Crystal Growth and Eu Segregationsupporting

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of chemical stress and the propensity for cracking during the vertical Bridgman growth of BaBrCl:Eu

Zhang

Gao

Tremsin

et al. 2020

Journal of Crystal Growth

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“…Researchers from Computer Science, Neuroscience, and Medical fields have applied EEG-based Brain-Computer Interaction (BCI) techniques in many different ways [2,15,19,22,24,26,34], such as diagnosis of abnormal states, evaluating the effect of the treatments, seizure detection, motor imagery tasks [4,5,6,17,23,27], and developing BCI-based games [14]. Previous studies have demonstrated the great potential of machine learning, deep learning, and transfer learning algorithms [1,3,7,8,12,16,18,20,21,25,28,29,37,38,39,40,41,42] in such clinical and non-clinical data analysis.…”

Section: Introductionmentioning

confidence: 99%

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

Dou¹,

Zhang²,

Qu³

2022

Preprint

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Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.

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Time Majority Voting, a PC-Based EEG Classifier for Non-expert Users

Dou

Zhou

2022

HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments

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Computational modeling and neutron imaging to understand interface shape and solute segregation during the vertical gradient freeze growth of BaBrCl:Eu

Cited by 7 publications

References 82 publications

Analysis of chemical stress and the propensity for cracking during the vertical Bridgman growth of BaBrCl:Eu

Analysis of chemical stress and the propensity for cracking during the vertical Bridgman growth of BaBrCl:Eu

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

Time Majority Voting, a PC-Based EEG Classifier for Non-expert Users

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