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Electrochemical dehalogenation of polyhalogenated compounds is an inefficient process as the working electrode is passivated by the deposition of short-chain polymers that form during the early stages of electrolysis. Herein, we report the use of 1, 1, 1, 3, 3, 3-hexaflouroisopropanol (HFIP) as an efficient reagent to control C–H formation over the radical association. Debromination of 1,6-dibromohexane was examined in the presence of Ni(II) salen and HFIP as the electrocatalyst and hydrogen atom source, respectively. Electrolysis of 10 mM 1,6-dibromohexane and 2 mM Ni(II) salen in the absence of HFIP yields 50% unreacted 1,6-dibromohexane and ∼40% unaccounted for starting material, whereas electrolysis with 50 mM HFIP affords 65%n-hexane. The mechanism of hydrogen atom incorporation was examined via deuterium incorporation coupled with high-resolution mass spectrometry, and density functional theory (DFT) calculations. Deuterium incorporation analysis revealed that the hydrogen atom originated from the secondary carbon of HFIP. DFT calculations showed that the deprotonation of hydroxyl moiety of HFIP, prior to the hydrogen atom transfer, is a key step for C–H formation. The scope of electrochemical dehalogenation was examined by electrolysis of 10 halogenated compounds. Our results indicate that through the use of HFIP, the formation of short-chain polymers is no longer observed, and monomer formation is the dominant product. 

This paper proposes and analyzes a stochastic Susceptible-Exposed-Infected-Removed (SEIR) spreading model on networks. Imagine a nursing home housing 28 seniors and 7 staff workers, in which one of the staff has tested positive for COVID-19. Unfortunately, the results of this test are 3 days late and the infected person had not been quarantining while waiting for their test results. What is now the individual risk to the different people living in this nursing home? If the home has access to two rapid COVID-19 viral tests, who should they be given to and why? In order to answer questions like this, we need to study stochastic models rather than deterministic ones. Unlike the vast majority of works that analyze various deterministic models, stochastic models are required when analyzing the risk of COVID-19 to individual people rather than tracking aggregate numbers in a given region. More specifically, this paper compares the results provided by analyzing stochastic and deterministic models and investigating when it is suitable to use the different models. In particular, we show why it is not suitable to use deterministic models when analyzing the spread in small communities and how these questions can be better addressed using stochastic ones. Finally, we show the added complications that arise due to the relatively long incubation period of COVID-19, and how it can be addressed. A simulated case study of the spread of COVID-19 in a 35-person nursing home is used to help illustrate our results.