2021
DOI: 10.1016/j.fuel.2021.120509
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Property predictions demonstrate that structural diversity can improve the performance of polyoxymethylene ethers as potential bio-based diesel fuels

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Cited by 29 publications
(18 citation statements)
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“…Furthermore, Bartholet et al demonstrated that different molecular structures of OME and derivatives affect the nanoparticle behavior during combustion in a laminar burner. 86 This must be investigated in the inner-engine combustion environment.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, Bartholet et al demonstrated that different molecular structures of OME and derivatives affect the nanoparticle behavior during combustion in a laminar burner. 86 This must be investigated in the inner-engine combustion environment.…”
Section: Discussionmentioning
confidence: 99%
“…Current models for the prediction of physical properties and especially fuel properties are very sophisticated, relying on large databases and complex calculation methods. 36,37,[39][40][41][42][43][44][45][46][47] A common type of predictive model described in literature is the group contribution method. It describes the investigated compound as a set of molecular groups.…”
Section: Prediction Of Physico-chemical and Fuel Propertiesmentioning
confidence: 99%
“…35 Furthermore, systematic investigations on physico-chemical properties, including various OMDAE compounds, have been published recently. 36,37 Within this work, symmetric as well as asymmetric OMDAEs have been investigated with a strong focus on physico-chemical and fuel properties. Regarding the symmetric compounds, diethoxymethane (DEM), dipropoxymethane (DPM), dibutoxymethane (DBM) and di(2-ethylhexyloxy)methane (D(2-EH)M) have been purchased and extensively characterized.…”
Section: Introductionmentioning
confidence: 99%
“…To do this, first, YSI and CN of 50 ethers, at least one of whose CN or YSI values have been studied experimentally, were curated in Section S7 of SI. The missing CN or YSI of some species were predicted using an in-house machine-learning (ML) prediction model for YSI [41] and CN [42]. Next, we devised the models accounting for the quantitative relationship between the molecular structure and YSI/CN values.…”
Section: Guide For the Future Design Of Diesel Fuels With Improved Sooting Tendency And Cetane Numbermentioning
confidence: 99%