Improved model for the refractive index: application to potential components of ambient aerosol
Literature Information
Rémi Bouteloup, Didier Mathieu
Understanding the impact of atmospheric aerosols on the global radiative balance requires knowing the refractive index (RI) of their components. Currently available methods to estimate this property from molecular structure are mostly empirical and exhibit significant errors (>10%). This work reports a more physically sound model leading to predictions within ±5% from experiment. The root mean square relative error is <1% for general organic compounds, and <2% for oxygen-rich compounds of special interest in aerosol chemistry. In this approach, the RI is obtained from the Lorentz–Lorenz equation. The molar volume and polarizability required as input are obtained from the addition of a so-called geometrical fragment (GF) associated with every non-hydrogen atom in the molecule. The value of this GF method to the study of ambient aerosol is demonstrated through extensive validation and application to compounds that may be present in aerosol droplets. In so doing, insight is provided into the origin of significant errors previously noted using earlier methods. Moreover, it is demonstrated that reference values of the refractive index reported in widely used compilations should be considered with caution. Finally, a Python script is provided as supplementary information for easy use of the present model to estimate molar volume and refractive index for any molecule.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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