The multiple-channel reactions SiH(3) + SiH(CH(3))(3) --> products are investigated by direct dynamics method. The minimum energy path (MEP) is calculated at the MP2/6-31+G(d,p) level, and energetic information is further refined by the MC-QCISD (single-point) method. The rate constants for individual reaction channels are calculated by the improved canonical variational transition state theory with small-curvature tunneling correction over the temperature range of 200-2400 K. The theoretical three-parameter expression k(T) = 2.44 x 10(-23)T(3.94) exp(-4309.55/T) cm(3)/(molecule s) is given. Our calculations indicate that hydrogen abstraction channel R1 from SiH group is the major channel because of the smaller barrier height among five channels considered.
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http://dx.doi.org/10.1002/jcc.21047 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Nursing and Health Promotion, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
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Agricultural College, Faculty of Agricultural College, Inner Mongolia Agricultural University, Hohhot, 010019, China.
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View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
View Article and Find Full Text PDFSci Rep
January 2025
Department of urinary surgery, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, 450003, Henan, China.
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View Article and Find Full Text PDFNat Commun
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Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers.
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