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The acentric factor $ω$ is a conceptual number introduced by Kenneth Pitzer in 1955, proven to be useful in the description of fluids.^[1] It has become a standard for the phase characterization of single and pure components, along with other state description parameters such as molecular weight, critical temperature, critical pressure, and critical volume (or critical compressibility). The acentric factor is also said to be a measure of the non-sphericity (centricity) of molecules.^[2]

Pitzer defined $ω$ from the relationship

\omega =-\log _{10}(p_{\text{r}}^{\text{sat}})-1{\text{ at }}T_{\text{r}}=0.7,

where $p_{\text{r}}^{\text{sat}}=p^{\text{sat}}/p_{c}$ is the reduced saturation vapor pressure, and $T_{\text{r}}=T/T_{c}$ is the reduced temperature.^[3]

Pitzer developed this factor by studying the vapor-pressure curves of various pure substances. Thermodynamically, the vapor-pressure curve for pure components can be mathematically described using the Clausius–Clapeyron equation.

The integrated form of equation is mainly used for obtaining vapor-pressure data mathematically. This integrated version shows that the relationship between the logarithm of vapor pressure and the reciprocal of absolute temperature is approximately linear.^[1]

For a series of fluids, as the acentric factor increases the vapor curve is "pulled" down, resulting in higher boiling points. For many monatomic fluids, $p_{\text{r}}^{\text{sat}}\approx 0.1$ at $T_{\text{r}}=0.7,$ which leads to $\omega \to 0$ . In many cases, $T_{\text{r}}=0.7$ lies above the boiling temperature of liquids at atmosphere pressure.

Values of $ω$ can be determined for any fluid from accurate experimental vapor-pressure data. The definition of $ω$ gives values close to zero for the noble gases argon, krypton, and xenon. $\omega$ is also very close to zero for molecules which are nearly spherical.^[2] Values of $ω \leq -1$ correspond to vapor pressures above the critical pressure and are non-physical.

The acentric factor can be predicted analytically from some equations of state. For example, it can be easily shown from the above definition that a van der Waals fluid has an acentric factor of about −0.302024, which if applied to a real system would indicate a small, ultra-spherical molecule.^[4]

Values of some common gases

[edit]

Molecule	Acentric factor^[5]
Acetone	0.304^[6]
Acetylene	0.187
Ammonia	0.253
Argon	0.000
Carbon dioxide	0.228
Decane	0.484
Ethanol	0.644^[6]
Helium	−0.390
Hydrogen	−0.220
Krypton	0.000
Methanol	0.556^[6]
Neon	0.000
Nitrogen	0.040
Nitrous oxide	0.142
Oxygen	0.022
Xenon	0.000

References

[edit]

^ ^a ^b Adewumi, Michael. "Acentric Factor and Corresponding States". Pennsylvania State University. Retrieved 2013-11-06.
^ ^a ^b Saville, G. (2006). "ACENTRIC FACTOR". A-to-Z Guide to Thermodynamics, Heat and Mass Transfer, and Fluids Engineering. doi:10.1615/AtoZ.a.acentric_factor.
^ "Acentric Factor Calculator". www.calculatoratoz.com. Retrieved 2024-05-17.
^ Shamsundar, N.; Lienhard, J. H. (December 1983). "Saturation and metastable properties of the van der waals fluid". Canadian Journal of Chemical Engineering. 61 (6): 876–880. doi:10.1002/cjce.5450610617. Retrieved 10 August 2022.
^ Yaws, Carl L. (2001). Matheson Gas Data Book. McGraw-Hill.
^ ^a ^b ^c Reid, R. C.; Prausnitz, J. M.; Poling, B. E. (1987). The Properties of Gases and Liquids (4th ed.). McGraw-Hill. ISBN 0070517991.

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The acentric factor (ω) is a dimensionless parameter introduced in thermodynamics to characterize the non-sphericity of molecules and quantify deviations from the simple two-parameter principle of corresponding states, enabling more accurate predictions of fluid properties such as compressibility, vapor pressure, and entropy of vaporization.^[1] Developed by Kenneth S. Pitzer and colleagues in 1955, the acentric factor addresses limitations in the original corresponding states theory, which assumes molecules behave like simple spherical fluids (e.g., noble gases with ω = 0), by incorporating molecular asymmetry effects observed in hydrocarbons and other complex substances.^[1] It is formally defined as ω = -log₁₀(Pʳ) - 1.0, where Pʳ is the reduced vapor pressure (Pᵛ/Pᶜ) at a reduced temperature Tʳ = T/Tᶜ = 0.7, with Pᵛ as the saturation vapor pressure, Pᶜ as the critical pressure, and Tᶜ as the critical temperature.^[2] This definition arises from empirical analysis of vapor pressure data via the Clausius-Clapeyron equation, where deviations from the log(Pʳ) versus 1/Tʳ slope for simple fluids are measured at Tʳ = 0.7 to capture non-ideal behavior.^[3] The acentric factor's primary importance lies in extending the corresponding states principle to a three-parameter framework, allowing thermodynamic properties like the compressibility factor Z to be expressed as Z = Z⁰(Tʳ, Pʳ) + ω Z¹(Tʳ, Pʳ), where Z⁰ and Z¹ are functions for simple fluids and first-order corrections, respectively; this improves accuracy for non-spherical or polar molecules without requiring extensive experimental data.^[2] For typical substances, ω values range from near 0 for spherical molecules (e.g., argon: ω ≈ 0) to around 0.3–0.5 for elongated hydrocarbons (e.g., n-pentane: ω ≈ 0.252), with higher values indicating greater deviation and stronger intermolecular forces due to shape.^[3] In practice, the acentric factor is integral to modern equations of state, such as the Peng-Robinson and Soave-Redlich-Kwong models, where it adjusts attraction and repulsion parameters to predict phase equilibria, densities, and other properties in chemical processes, reservoir engineering, and natural gas handling.^[4] These applications rely on tabulated ω values derived from critical properties or estimated via group contribution methods for unmeasured compounds, ensuring reliable simulations in industries like petrochemicals and cryogenics.^[5]

Definition and Physical Meaning

Mathematical Formulation

The acentric factor, denoted as

\omega

, is a dimensionless parameter introduced by Pitzer to quantify deviations from the behavior of simple fluids with spherical molecules in non-ideal gases. It is defined by the equation

\omega = -\log_{10} P_r^s - 1.0,

where

P_r^s

is the reduced saturation vapor pressure at a reduced temperature

T_r = 0.7

.^[2] The reduced variables are based on the critical properties of the substance: the reduced temperature

T_r = T / T_c

, where

T

is the absolute temperature and

T_c

is the critical temperature, and the reduced pressure

P_r = P / P_c

, where

P

is the pressure and

P_c

is the critical pressure.^[2] The value of

P_r^s

is determined from the saturation vapor pressure

P^s

measured or calculated at the temperature corresponding to

T_r = 0.7

, which lies below the critical point for most substances. For spherical molecules such as argon,

\omega \approx 0

by definition, reflecting conformity to the simple fluid model.^[2] The parameter is dimensionless and typically ranges from 0 for spherical molecules to values exceeding 0.5 for highly complex, nonspherical molecules.^[2]

Interpretation for Molecular Structure

The acentric factor serves as a dimensionless parameter that quantifies the degree of acentricity in a molecule, representing deviations from the idealized spherical symmetry assumed in simple models such as the van der Waals equation.^[6] It captures the asymmetry arising from molecular shape, which influences the overall thermodynamic behavior beyond what critical properties alone can describe.^[7] For molecules with centric, nearly spherical structures, the acentric factor approaches zero, indicating minimal deviation from spherical symmetry and thus simpler intermolecular interactions dominated by central forces.^[6] In contrast, higher values reflect increased non-sphericity, such as in elongated, chain-like, or more polarizable structures, which enhance anisotropic attractions and repulsions between molecules.^[8] This non-sphericity affects the distribution and strength of intermolecular forces, leading to broader applicability in corresponding-states correlations for non-ideal fluids.^[6] Conceptually, the acentric factor relates to the intermolecular potential energy landscape, particularly by influencing the width and depth of the attractive potential well in models like the Lennard-Jones potential, which assumes spherical symmetry.^[8] Deviations measured by the acentric factor account for how non-central forces, such as those from molecular orientation or shape, alter the potential from its simple spherical form, thereby impacting properties like vapor pressure and compressibility.^[6] Representative examples illustrate this interpretation: noble gases like neon exhibit very low acentric factors near zero due to their atomic, spherical nature, while for n-alkanes, the value increases progressively with chain length—from near zero for methane to higher values for longer chains like n-heptane—reflecting growing molecular elongation and non-sphericity.^[8] However, the acentric factor's physical interpretation is most reliable for non-polar fluids, as it primarily encodes shape-related deviations rather than strong dipole moments or hydrogen bonding; for highly polar or associating compounds like water or ammonia, it shows limitations in capturing these additional interaction types.^[8]

Historical Context

Introduction by Pitzer

The acentric factor was introduced in 1955 by Kenneth S. Pitzer in collaboration with D. Z. Lippmann, R. F. Curl Jr., C. M. Huggins, and D. E. Petersen, as part of a series of papers on the volumetric and thermodynamic properties of fluids within the framework of reduced equations of state.^[1] This concept emerged from Pitzer's long-standing research on hydrocarbon thermodynamics, which began in the 1930s with studies on entropy and phase behavior.^[9] The original publication appeared in the Journal of the American Chemical Society, where the authors proposed the acentric factor as a third parameter to enhance predictive accuracy for fluid properties.^[1] The motivation stemmed from the recognized limitations of the traditional two-parameter corresponding states principle, which relied solely on critical temperature and pressure and performed poorly for non-spherical molecules, particularly hydrocarbons.^[9] Pitzer's key insight was that incorporating an acentric factor, denoted ω and defined as a measure of deviation from spherical molecular shape via vapor pressure at a reduced temperature of 0.7, would extend the principle to better correlate properties such as compressibility factors and fugacity coefficients.^[1] This addition allowed for more reliable generalizations across diverse fluids, addressing inaccuracies in earlier models for complex systems.^[9] Early adoption of the acentric factor occurred within Pitzer's own correlations for second virial coefficients and critical properties, demonstrating its utility in fitting experimental data for hydrocarbons.^[1] This development aligned with post-World War II advancements in petroleum engineering, where precise models of gas and fluid behavior were essential for refining processes and reservoir simulations involving non-ideal hydrocarbon mixtures.^[9]

Developments in Corresponding States Theory

The acentric factor extended the classical two-parameter corresponding states principle, which relied solely on critical temperature

T_c

and critical pressure

P_c

, to a three-parameter framework by incorporating

\omega

as a measure of molecular non-sphericity. This advancement allowed for more accurate scaling of thermodynamic properties across diverse fluids, particularly non-spherical ones.^[6] Key milestones in integrating the acentric factor into corresponding states theory occurred during the 1950s and 1960s, when it was incorporated into generalized charts for second virial coefficients to better capture deviations from ideality in non-polar fluids. By the 1970s, the principle influenced modifications to the Benedict-Webb-Rubin equation of state, enabling broader applicability to real gases through acentric scaling of parameters for enhanced volumetric and caloric property predictions. The Pitzer-Curl correlation, developed in 1957, represented an early systematic method to apply the acentric factor for scaling acentric effects in virial coefficients and mixture combining rules, providing a functional form that adjusted the reduced second virial coefficient

B_r

B_r = B^{(0)} + \omega B^{(1)}

, where

B^{(0)}

and

B^{(1)}

are reference functions for simple and acentric fluids, respectively. This correlation improved the representation of intermolecular forces in mixtures without requiring substance-specific adjustments. The broader impact of these developments lay in enabling generalized corresponding states charts for key thermodynamic functions, such as enthalpy departure

(H - H^{ig})/RT_c

, entropy departure

(S - S^{ig})/R

, and phase equilibria, which could be applied across fluids using only critical parameters and

\omega

, thereby reducing reliance on extensive experimental data for engineering calculations. As of 2025, the three-parameter corresponding states framework with the acentric factor remains a foundational tool in thermodynamic modeling, though it is increasingly augmented by data-driven extended corresponding states approaches using machine learning for complex systems like hydrofluoroolefins.^[10]

Determination Methods

From Vapor Pressure Data

The standard procedure for determining the acentric factor

\omega

relies on vapor-liquid equilibrium data for pure substances, specifically the saturation vapor pressure at a reduced temperature of 0.7. This approach quantifies deviations from simple corresponding states behavior by comparing the observed vapor pressure to that expected for spherical molecules. The step-by-step process begins with acquiring the critical temperature

T_c

and critical pressure

P_c

, which serve as reference points. Next, measure or obtain the saturation vapor pressure

P^s

at the temperature

T = 0.7 T_c

. Calculate the reduced values

T_r = T / T_c = 0.7

and

P_r^s = P^s / P_c

. The acentric factor is then computed using the defining equation:

\omega = -\log_{10} P_r^s - 1

This yields

\omega \approx 0

for simple fluids like noble gases, increasing for more complex, non-spherical molecules.^[11] Reliable critical constants are typically drawn from experimental measurements or established compilations in databases such as the NIST Chemistry WebBook or the DIPPR 801 database. Vapor pressure data requirements include the full curve or key points, often fitted with the Antoine equation

\log_{10} P^s = A - B / (T + C)

for interpolation and extrapolation, or direct measurements to ensure precision at

T_r = 0.7

.^[12] Vapor pressure measurements for pure compounds are commonly performed using PVT apparatus, which simultaneously records pressure, volume, and temperature under controlled equilibrium conditions, or ebulliometers, which determine boiling points at specified pressures to derive

P^s

. These techniques are suitable for subcritical ranges but face challenges near the critical point, where high pressures (often exceeding 10 MPa) complicate sample containment, and the vanishing density difference between phases hinders clear separation of liquid and vapor, potentially leading to metastable states or measurement artifacts.^[13]^[14] For well-behaved non-polar substances with high-purity samples (>99.9%), the resulting

\omega

achieves an accuracy of typically

\pm 0.01

, reflecting the precision of modern vapor pressure data. Errors can stem from impurities shifting the equilibrium curve, uncertainties in

T_c

P_c

(e.g.,

\pm 0.1\%

for

P_c

), or attempts to apply the method beyond the critical point, where no distinct vapor pressure exists.^[13] This vapor pressure-based method is the established standard for non-polar fluids, as implemented in major thermophysical databases like NIST and DIPPR, providing consistent

\omega

values for thermodynamic applications.^[12]

Estimation Techniques

Group contribution methods provide a predictive framework for estimating the acentric factor (ω) of organic compounds by decomposing the molecule into functional groups and summing their incremental contributions. These methods are particularly useful for hydrocarbons and simple organics where experimental vapor pressure data is limited. A widely adopted approach involves assigning specific values to common groups, such as the -CH₂- group contributing approximately 0.03 to ω, allowing for rapid estimation based on molecular structure alone. For instance, the method developed by Nannoolal et al. uses second-order group contributions fitted to a large dataset of organic compounds, achieving average absolute deviations of about 0.02 in ω for non-polar molecules. The Ambrose-Walton corresponding-states method offers another estimation route by correlating vapor pressure behavior with critical properties and the normal boiling point to back-calculate ω when direct vapor pressure measurements at T_r = 0.7 are unavailable. This technique inverts the vapor pressure equation to solve for ω, providing reliable predictions for non-polar fluids with errors typically under 0.05. It is often integrated into group contribution frameworks for enhanced accuracy in complex structures.^[15] Empirical correlations based on critical properties and the reduced normal boiling point (T_{br} = T_b / T_c) enable quick approximations of ω without detailed structural analysis. More refined versions, such as those derived from Riedel's vapor pressure factor or relations linking parameters at the boiling point to the acentric factor definition, adjust for molecular weight and achieve better precision for alkanes and aromatics. These correlations are benchmarked against the standard vapor pressure method for validation.^[16] Quantum chemical estimations leverage computational chemistry to derive ω from molecular properties like electron density distributions or polarizability tensors, capturing the underlying non-sphericity of the molecule. By performing ab initio or density functional theory calculations, descriptors such as the anisotropy of the polarizability tensor or integrated electron density moments can be correlated to ω through quantitative structure-property relationships (QSPR) or machine learning models. For example, Biswas et al. (2023) developed graph neural network models using quantum mechanical descriptors to predict ω for over 1100 compounds, with mean absolute errors around 0.03–0.04, offering advantages for novel or hypothetical molecules where empirical data is absent.^[17] Advanced methods, including molecular dynamics (MD) simulations, are employed for chain molecules and polymers where traditional approaches falter due to size and flexibility. MD computes thermodynamic properties like vapor-liquid equilibria from first principles, allowing indirect estimation of ω via simulated critical points or vapor pressure curves. Group contribution schemes like SAFT-γ incorporate chain length and branching effects to model equation-of-state parameters for long-chain hydrocarbons and associating fluids. These techniques are computationally intensive but essential for macromolecules.^[18] Estimation techniques generally exhibit reduced accuracy for polar and associating fluids, where errors can reach ±0.1 due to neglected intermolecular forces like hydrogen bonding. Validation against comprehensive databases, such as the former PPDS (now DIPPR), confirms their utility for non-polar systems but highlights the need for corrections in polar cases.^[19]

Thermodynamic Applications

Role in Equations of State

The acentric factor serves as a key third parameter in cubic equations of state (EOS), enabling them to account for deviations from the two-parameter corresponding states principle due to molecular acentricity. By incorporating the acentric factor ω, these EOS adjust the temperature dependence of the attractive term to better represent real fluid behavior, particularly for non-spherical molecules like hydrocarbons. In the Peng-Robinson EOS, introduced in 1976, ω modifies the attractive parameter as

a(T) = a_c \left[1 + \kappa (1 - \sqrt{T_r})\right]^2,

a(T)=ac[1+κ(1−Tr

)]2,
where
$\kappa = 0.37464 + 1.54226 \omega - 0.26992 \omega^2,$
and $a_c$ , $T_r$ denote the critical value and reduced temperature, respectively. This formulation enhances the EOS's ability to predict liquid densities and vapor pressures more accurately than earlier models. Similarly, the Soave-Redlich-Kwong EOS, proposed in 1972, employs ω in the temperature function
$\alpha(T_r) = \left[1 + m (1 - \sqrt{T_r})\right]^2,$

)]2,
with
$m = 0.480 + 1.574 \omega - 0.176 \omega^2.$
This adjustment improves the representation of vapor-liquid equilibria by scaling the intermolecular attractions based on molecular shape.^[20] The inclusion of ω in these cubic EOS significantly enhances predictions of thermodynamic properties, such as vapor pressure and liquid density, by capturing non-sphericity effects; for hydrocarbons, it provides better alignment with experimental pressure-volume-temperature (P-V-T) data near the critical point, ensuring reliable extrapolations across a wide range of conditions.^[20] In practical applications, the acentric factor is integrated into software tools like Aspen Plus and REFPROP for process simulations and fluid property calculations, where cubic EOS with ω provide efficient estimates for engineering design.^[21]

Extensions to Mixtures and Polar Fluids

The acentric factor concept, originally developed for pure fluids, has been extended to multicomponent mixtures through combining rules that define an effective mixture acentric factor, often using linear mole-fraction averaging,

\omega_m = \sum_i x_i \omega_i

, or volume-weighted variants such as

\omega_m = \frac{\sum_i x_i \omega_i b_i}{\sum_i x_i b_i}

in equations of state like the Schmidt-Wenzel model.^[22] In cubic equations of state such as Peng-Robinson (PR), the acentric factor influences pure-component attraction parameters

a_i

, which are then combined quadratically for mixtures via

a_m = \sum_i \sum_j x_i x_j \sqrt{a_i a_j} (1 - k_{ij})

am=∑i∑jxixjaiaj

(1−kij), where binary interaction parameters $k_{ij}$ account for non-ideality, though direct linear mixing of $\omega_{ij} = (\omega_i + \omega_j)/2$ is applied in some van der Waals-type formulations for simplified corresponding-states predictions.^[23] These rules enable the prediction of mixture properties like compressibility and phase behavior by treating the mixture as a pseudo-pure fluid with averaged parameters. For polar and associating fluids, the standard acentric factor often underpredicts vapor-liquid equilibrium (VLE) and other properties due to unaccounted hydrogen bonding and dipolar interactions; for instance, water has an acentric factor of 0.344, but conventional cubic EOS fail to capture its phase behavior without additional terms.^[24] Modified equations like the Cubic-Plus-Association (CPA) EOS address this by combining a cubic term (e.g., PR or SRK, parameterized by $\omega$ ) with an association contribution for hydrogen-bonding sites, improving predictions for systems like water-hydrocarbon mixtures in reservoir conditions.^[25] In CPA, the acentric factor retains its role in the physical (non-associating) part, while association parameters (e.g., volume and energy of bonding) are fitted separately, yielding improved VLE predictions for polar binaries.^[26] Advanced thermodynamic models, such as Group Contribution Statistical Associating Fluid Theory (GC-SAFT), incorporate a polar factor alongside the acentric factor to handle electrolytes and associating mixtures; the polar term models dipole or quadrupole effects via perturbation theory, with $\omega$ used to derive shape and dispersion parameters from critical properties.^[27] This dual-parameter approach in polar GC-SAFT outperforms standard SAFT by explicitly separating non-polar (acentric-influenced) and polar contributions.^[28] Applications include phase equilibrium modeling in oil-gas separators, where incorporating $\omega$ -based mixing rules in CPA or SAFT enhances mutual solubility predictions for water-hydrocarbon systems compared to non-associating models.^[24] Post-2000 developments leverage machine learning to correlate mixture properties using the acentric factor as a key input feature; for example, neural networks have been used to model acentric factors for binary ionic liquid mixtures from critical properties and compositions.^[29] These ML approaches, often integrated with EOS, improve VLE forecasts for complex mixtures by learning non-linear dependencies beyond traditional combining rules, as demonstrated in graph neural network models that predict acentric factors from molecular structures.^[30]

Representative Values

For Simple Gases

The acentric factor for simple gases, derived from vapor pressure data, quantifies deviations from ideal spherical symmetry in their molecular structure and thermodynamic behavior. These values serve as benchmarks for the corresponding states principle, where simple fluids like noble gases approximate ω ≈ 0, indicating minimal non-sphericity. Representative acentric factor values for selected simple gases are compiled below, drawn from standard thermodynamic references including Pitzer's foundational data and modern compilations. Uncertainties are typically ±0.005 for most entries, reflecting experimental vapor pressure precision.

Category	Gas	ω
Noble gases	He	-0.365
	Ne	-0.038
	Ar	0.000
	Kr	-0.002
	Xe	0.000
Diatomic gases	N₂	0.037
	O₂	0.022
	H₂	-0.219
	CO	0.066
Others	CO₂	0.225
	CH₄	0.011

Trends in these values highlight near-zero ω for spherically symmetric noble gases like argon, krypton, and xenon, which closely follow simple fluid corresponding states. Negative values occur for quantum gases such as helium and hydrogen due to their light mass and pronounced quantum mechanical effects on intermolecular potentials. These acentric factors for simple gases provide essential benchmarks for validating corresponding states correlations in thermodynamic modeling.

For Organic Compounds

The acentric factor for organic compounds generally ranges from near zero for simple hydrocarbons to higher values exceeding 0.6 for polar molecules, reflecting increasing molecular non-sphericity and complexity. Representative values from the DIPPR 801 database, which compiles critically evaluated thermophysical properties updated through 2025, illustrate this variation across key classes of organics.^[31]

Compound Class	Compound	Acentric Factor (ω)
Alkanes	Methane	0.011
Alkanes	Ethane	0.099
Alkanes	Propane	0.152
Alkanes	n-Pentane	0.252
Alkenes	Ethylene	0.087
Aromatics	Benzene	0.210
Aromatics	Toluene	0.263
Others	Ethanol	0.643
Others	Acetone	0.304

These values are consistent with those reported in standard references and confirm the acentric factor's role in capturing deviations from spherical symmetry in organic structures. In alkanes, the acentric factor increases with molecular weight due to elongated chain structures that enhance non-central force fields, and branching further elevates ω by introducing asymmetry, as seen in comparisons between n-pentane and isopentane analogs. Aromatic compounds exhibit higher values than comparably sized aliphatics, attributable to π-electron delocalization that contributes to molecular asphericity and altered intermolecular interactions. For associating compounds like alcohols, the DIPPR database notes potential temperature dependence in effective acentric factors, arising from hydrogen bonding influences that modulate vapor pressure behavior beyond simple acentricity.^[31] High ω values in such cases, exemplified by ethanol's 0.643, highlight polarity effects that extend the parameter's utility despite its origins in nonpolar systems.

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Acentric factor

Definition and Physical Meaning

Mathematical Formulation

Interpretation for Molecular Structure

Historical Context

Introduction by Pitzer

Developments in Corresponding States Theory

Determination Methods

From Vapor Pressure Data

Estimation Techniques

Thermodynamic Applications

Role in Equations of State

Extensions to Mixtures and Polar Fluids

Representative Values

For Simple Gases

For Organic Compounds

References

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Acentric factor

Definition and Physical Meaning

Mathematical Formulation

Interpretation for Molecular Structure

Historical Context

Introduction by Pitzer

Developments in Corresponding States Theory

Determination Methods

From Vapor Pressure Data

Estimation Techniques

Thermodynamic Applications

Role in Equations of State

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