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Title: Anharmonic contribution to the stabilization of Mg(OH) 2 from first principles
Geometrical and vibrational characterization of magnesium hydroxide was performed using density functional theory. Four possible crystal symmetries were explored: P 3̄ (No. 147, point group −3), C 2/ m (No. 12, point group 2), P 3 m 1 (No. 156, point group 3 m ) and P 3̄ m 1 (No. 164, point group −3 m ) which are the currently accepted geometries found in the literature. While a lot of work has been performed on Mg(OH) 2 , in particular for the P 3̄ m 1 phase, there is still a debate on the observed ground state crystal structure and the anharmonic effects of the OH vibrations on the stabilization of the crystal structure. In particular, the stable positions of hydrogen are not yet defined precisely, which have implications in the crystal symmetry, the vibrational excitations, and the thermal stability. Previous work has assigned the P 3̄ m 1 polymorph as the low energy phase, but it has also proposed that hydrogens are disordered and they could move from their symmetric position in the P 3̄ m 1 structure towards P 3̄. In this paper, we examine the stability of the proposed phases by using different descriptors. We compare more » the XRD patterns with reported experimental results, and a fair agreement is found. While harmonic vibrational analysis shows that most phases have imaginary modes at 0 K, anharmonic vibrational analysis indicates that at room temperature only the C 2/ m phase is stabilized, whereas at higher temperatures, other phases become thermally competitive. « less
Authors:
; ; ; ; ; ; ;
Award ID(s):
1740111 1434897
Publication Date:
NSF-PAR ID:
10072413
Journal Name:
Physical Chemistry Chemical Physics
Volume:
20
Issue:
26
Page Range or eLocation-ID:
17799 to 17808
ISSN:
1463-9076
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. 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New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
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