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Cited by 226

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The green solventethyl lactate: an experimental and theoretical characterization

Aparicio, Santiago, Alcalde, Rafael·2009·Green Chem.

Abstract

Ethyl lactate is a green, and economically viable, alternative to traditional solvents whose extensive use and scale-up to industrial level requires a deep and accurate knowledge of its properties in wide pressure–temperature ranges. In this work, the pressure–volume–temperature and pressure–viscosity–temperature behaviors are reported together with several derived properties of remarkable importance for process design purposes. The structure of the liquid is analyzed at the microscopic level using the Density Functional Theory and from classical molecular dynamics simulations. It is shown the competing effect of intra and intermolecular hydrogen bonding mainly through preferred positions. The predictive ability of the forcefield used for molecular dynamics simulations is studied, showing good results for most of the considered properties. Monte Carlo/Gibbs ensemble simulations were carried out to predict the phase equilibria of the fluid, considering the absence of experimental data.

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Crossref · 10.1039/b811909k

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Metadata

Authors
Aparicio, S., & Alcalde, R.
Venue
Green Chem.
Published
2009
Volume
11
Issue
1
Pages
65-78
ISSN
  • 1463-9262 (ISSN-L)
  • 1463-9270
OpenAlex
W1991683738
Cited by
226
References
2
FWCI
6.82
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The green solventethyl lactate: an experimental and theoretical characterization. https://app.cochat.ai/papers/10.1039/b811909k
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Aparicio, S., & Alcalde, R. (2009). The green solventethyl lactate: an experimental and theoretical characterization. Green Chem. https://doi.org/10.1039/b811909k

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.