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   https://doi.org/10.1038/s42005-020-00442-x  

   Wilson, Richard B. ; Coh, Sinisa ( December 2020 , Communications Physics)

   null (Ed.)

   Abstract Understanding how photoexcited electron dynamics depend on electron-electron (e-e) and electron-phonon (e-p) interaction strengths is important for many fields, e.g. ultrafast magnetism, photocatalysis, plasmonics, and others. Here, we report simple expressions that capture the interplay of e-e and e-p interactions on electron distribution relaxation times. We observe a dependence of the dynamics on e-e and e-p interaction strengths that is universal to most metals and is also counterintuitive. While only e-p interactions reduce the total energy stored by excited electrons, the time for energy to leave the electronic subsystem also depends on e-e interaction strengths because e-e interactions increase the number of electrons emitting phonons. The effect of e-e interactions on energy-relaxation is largest in metals with strong e-p interactions. Finally, the time high energy electron states remain occupied depends only on the strength of e-e interactions, even if e-p scattering rates are much greater than e-e scattering rates. 

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2. Controlling One-Electron vs Two-Electron Pathways in the Multi-Electron Redox Cycle of Nickel Diethyldithiocarbamate

   https://doi.org/10.1021/acs.inorgchem.1c01699  

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   https://doi.org/10.1103/PhysRevA.87.043428  

   Emmanouilidou, A. ; Price, H. ( April 2013 , Physical Review A)
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   https://doi.org/10.1103/PhysRevB.84.085408  

   Zou, K. ; Hong, X. ; Zhu, J. ( August 2011 , Physical Review B)
5. Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

   https://doi.org/10.1093/micmic/ozad132  

   Wei, Jingrui ; Moore, Kalani ; Bammes, Benjamin ; Levin, Barnaby D ; Hagopian, Nicholas ; Jacobs, Ryan ; Morgan, Dane ; Voyles, Paul M ( December 2023 , Microscopy and Microanalysis)

   Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

    

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