Photoelectron spectra and angular distributions in 267 nm detachment of the I(-)Ar, I(-)H(2)O, I(-)CH(3)I, and I(-)CH(3)CN cluster anions are examined in comparison with bare I(-) using velocity-map photoelectron imaging. In all cases, features are observed that correlate to two channels producing either I((2)P(3/2)) or I((2)P(1/2)). In the photodetachment of I(-) and I(-)Ar, the branching ratios of the (2)P(1/2) and (2)P(3/2) channels are observed to be approximately 0.4, in both cases falling short of the statistical ratio of 0.5. For I(-)H(2)O and I(-)CH(3)I, the (2)P(1/2) to (2)P(3/2) branching ratios are greater by a factor of 1.6 compared to the bare iodide case. The relative enhancement of the (2)P(1/2) channel is attributed to dipole effects on the final-state continuum wave function in the presence of polar solvents. For I(-)CH(3)CN the (2)P(1/2) to (2)P(3/2) ratio falls again, most likely due to the proximity of the detachment threshold in the excited spin-orbit channel. The photoelectron angular distributions in the photodetachment of I(-), I(-)Ar, I(-)H(2)O, and I(-)CH(3)CN are understood within the framework of direct detachment from I(-). Hence, the corresponding anisotropy parameters are modeled using variants of the Cooper-Zare central-potential model for atomic-anion photodetachment. In contrast, I(-)CH(3)I yields nearly isotropic photoelectron angular distributions in both detachment channels. The implications of this anomalous behavior are discussed with reference to alternative mechanisms, affording the solvent molecule an active role in the electron ejection process.
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http://dx.doi.org/10.1063/1.1839861 | DOI Listing |
Environ Technol
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Solid-State Physics and Accelerators Department, National Centre for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt.
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View Article and Find Full Text PDFACS Nano
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Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore.
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View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
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The School of Computer Science, Hangzhou Dianzi University, Hangzhou, China.
Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding.
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Chongqing Key Laboratory of Prevention and Treatment on Major Blinding Diseases, Chongqing Eye Institute, Chongqing Branch (Municipality Division) of National Clinical Research Center for Ocular Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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