Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 4,669

The DOI matches its Crossref record.

Statistical thermodynamics of liquid mixtures: A new expression for the excess Gibbs energy of partly or completely miscible systems

Abrams, Denis S., Prausnitz, John M.·1975·AIChE Journal

Abstract

To obtain a semi‐theoretical equation for the excess Gibbs energy of a liquid mixture, Guggenheim's quasi‐chemical analysis is generalized through introduction of the local area fraction as the primary concentration variable. The resulting universal quasi‐chemical (UNIQUAC) equation uses only two adjustable parameters per binary. Extension to multicomponent systems requires no ternary (or higher) parameters. The UNIQUAC equation gives good representation of both vapor‐liquid and liquid‐liquid equilibria for binary and multicomponent mixtures containing a variety of nonelectrolyte components such as hydrocarbons, ketones, esters, amines, alcohols, nitriles, etc., and water. When well‐defined simplifying assumptions are introduced into the generalized quasi‐chemical treatment, the UNIQUAC equation reduces to any one of several well‐known equations for the excess Gibbs energy, including the Wilson, Margules, van Laar, and NRTL equations. The effects of molecular size and shape are introduced through structural parameters obtained from pure‐component data and through use of Staverman's combinatorial entropy as a boundary condition for athermal mixtures. The UNIQUAC equation, therefore, is applicable also to polymer solutions.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/aic.690210115

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 4,669

Metadata

Authors
Abrams, D. S., & Prausnitz, J. M.
Venue
AIChE Journal
Publisher
Wiley
Published
1975-01
Volume
21
Issue
1
Pages
116-128
ISSN
  • 0001-1541 (ISSN-L)
  • 1547-5905
OpenAlex
W2159544707
Cited by
4,669
References
0
FWCI
20.89
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Statistical thermodynamics of liquid mixtures: A new expression for the excess Gibbs energy of partly or completely miscible systems. https://app.cochat.ai/papers/10.1002/aic.690210115
The paper
Abrams, D. S., & Prausnitz, J. M. (1975). Statistical thermodynamics of liquid mixtures: A new expression for the excess Gibbs energy of partly or completely miscible systems. AIChE Journal. https://doi.org/10.1002/aic.690210115

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.