Improved model for the refractive index: application to potential components of ambient aerosol

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines

The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, opacity, and gloss) to materials and various end-use products. Since few methods exist to accurately estimate this property, we have designed a graph machine model (GMM) capable of predicting the RI of liquid organic compounds containing up to 16 different types of atoms and effective in discriminating between stereoisomers. Using 8267 carefully checked RI values from the literature and the corresponding 2D organic structures, the GMM provides a training root mean square relative error of less than 0.5%, i.e., an RMSE of 0.004 for the estimation of the refractive index of the 8267 compounds. The GMM predictive ability is also compared to that obtained by several fragment-based approaches. Finally, a Docker-based tool is proposed to predict the RI of organic compounds solely from their SMILES code. The GMM developed is easy to apply, as shown by the video tutorials provided on YouTube.

Hansen Solubility Parameters Applied to the Extraction of Phytochemicals

In many analytical chemical procedures, organic solvents are required to favour a better global yield upon the separation, extraction, or isolation of the target phytochemical analyte. The selection of extraction solvents is generally based on the solubility difference between target analytes and the undesired matrix components, as well as the overall extraction procedure cost and safety. Hansen Solubility Parameters are typically used for this purpose. They are based on the product of three coordinated forces (hydrogen bonds, dispersion, and dipolar forces) calculated for any substance to predict the miscibility of a compound in a pure solvent, in a mixture of solvents, or in non-solvent compounds, saving time and costs on method development based on a scientific understanding of chemical composition and intermolecular interactions. This review summarises how Hansen Solubility Parameters have been incorporated into the classical and emerging (or greener) extraction techniques of phytochemicals as an alternative to trial-and-error approaches, avoiding impractical experimental conditions and resulting in, for example, saving resources and avoiding unnecessary solvent wasting.

Machine Learning Identification of Organic Compounds Using Visible Light

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.

Complexities in Modeling Organic Aerosol Light Absorption

Aerosol particles dynamically evolve in the atmosphere by physicochemical interactions with sunlight, trace chemical species, and water. Current modeling approaches fix properties such as aerosol refractive index, introducing spatial and temporal errors in the radiative impacts. Further progress requires a process-level description of the refractive indices as the particles age and experience physicochemical transformations. We present two multivariate modeling approaches of light absorption by brown carbon (BrC). The initial approach was to extend the modeling framework of the refractive index at 589 nm (nD), but that result was insufficient. We developed a second multivariate model using aromatic rings and functional groups to predict the imaginary part of the complex refractive index. This second model agreed better with measured spectral absorption peaks, showing promise for a simplified treatment of BrC optics. In addition to absorption, organic functionalities also alter the water affinity of the molecules, leading to a hygroscopic uptake and increased light absorption, which we show through measurements and modeling.

Structural analysis of hyperbranched polyhydrocarbon synthesized by electrochemical polymerization

Structure of a hyperbranched polyhydrocarbon obtained by electrochemical polymerization was analyzed by various NMR techniques and modeling. The calculated physical properties from its bulk model system well matched with experimental results.

London Dispersion Helps Refine Steric A-Values: The Halogens

Halogens are rarely considered as dispersion energy donors for organic reaction design. Here, we re-examine one of the textbook examples for assessing steric hindrance, the A-value, and demonstrate that even in this system, halogens cannot be treated solely as classic repulsive hard spheres. A significant part of the steric demand of the halogens is compensated by attractive London dispersion (LD) interactions, explaining the experimental lack of a clear trend when going down the halogens' row. Beyond monohalogenated cyclohexanes, dihalo- and perhalocyclohexanes also show significant LD interactions. We also explored several other small organic systems containing halogens. Our findings show that organic chemists should treat halogens as possible sources of LD interactions in reaction design, as these atoms can change the landscape of the potential energy surface and reverse trends of conformer stabilities and reaction selectivities.

A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors

Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an optical material. Presented in this work is a quantitative structure–property relationship (QSPR) model that was developed to predict the refractive index for a diverse set of polymers. A number of models were created, where a four-variable model showed the best predictive performance with R² = 0.904 and Q²_LOO = 0.897. The robustness and predictability of the best model was validated using the leave-one-out technique, external set and y-scrambling methods. The predictive ability of the model was confirmed with the external set, showing the R²_ext = 0.880. For the refractive index, the ionization potential, polarizability, 2D and 3D geometrical descriptors were the most influential properties. The developed model was transparent and mechanistically explainable and can be used in the prediction of the refractive index for new and untested polymers.

QSPR versus fragment-based methods to predict octanol-air partition coefficients: Revisiting a recent comparison of both approaches

The octanol-air partition coefficient (K_OA) is useful to assess the fate of organic chemicals in the environment. Very recently, an interesting comparison of current methods to predict this property (Chemosphere 148 (2016) 118-125) highlighted a newly introduced Quantitative Structure-Property Relationship (QSPR), as a group-contribution (GC) method and a quantum chemical solvation model were reported to yield significantly less accurate results. Based on the observation that the so-called GC method investigated in this earlier study was inconsistent with the temperature dependence of K_OA, the previously recommended QSPR is presently compared to the geometrical fragment (GF) additivity scheme. In addition to providing some improvement in terms of accuracy, this fragment-based procedure exhibits many advantages in terms of simplicity, interpretability, applicability and availability.

The wavelength-dependent optical properties of weakly absorbing aqueous aerosol particles

A model for calculating the wavelength-dependent refractive index of multicomponent mixtures is presented and applied to aqueous systems in the atmosphere and oceans.

Thermally induced characterization and modeling of physicochemical, acoustic, rheological, and thermodynamic properties of novel blends of (HEF + AEP) and (HEF + AMP) for CO2/H2S absorption

CO₂ and H₂S removal from flue gases is indispensable to be done for protection of environment with respect to global warming as well as clean air. Chemical absorption is one of the most developed and capable techniques for the removal of these sour gases. Among the many solvents, ionic liquids (ILs) are more capable due to their desirable green solvent properties. However, ILs being usually costlier, the blends of ILs and amines are more suggestive for absorption. In the present work, various essential characterization properties such as density, viscosity, sound velocity, and refractive index of two ionic liquid-amine blend systems viz. (1) 2-Hydroxy ethyl ammonium formate (HEF) + 1-(2-aminoethyl) piperazine (AEP) and (2) 2-Hydroxy ethyl ammonium formate (HEF) + 2-Amino-2-methyl-1-propanol (AMP) are reported. The temperature range for which all the measurements were conducted is 298.15 to 333.15 K. For both systems of (HEF + AEP) and (HEF + AMP), HEF mass fractions were varied from 0.2 to 0.8.The density and viscosity results were correlated as a function of temperature and concentration of ionic liquid and amine with Redlich-Kister and Grunberg-Nissan models, respectively. Moreover, feed forward neural network model (ANN) is explored for correlating experimentally determined sound velocity and refractive index data. The measured properties are further analyzed to estimate various thermodynamic as well as transport properties such as diffusivity of CO₂/H₂S in the (HEF + AEP) and (HEF + AMP), thermal expansion coefficients, and isentropic compressibility, ΔG⁰, ΔS⁰, ΔH⁰, using the available models in the literature.

Intercomparison of in-situ aircraft and satellite aerosol measurements in the stratosphere

Aerosol composition and optical scattering from particles in the lowermost stratosphere (LMS) have been studied by comparing in-situ aerosol samples from the IAGOS-CARIBIC passenger aircraft with vertical profiles of aerosol backscattering obtained from the CALIOP lidar aboard the CALIPSO satellite. Concentrations of the dominating fractions of the stratospheric aerosol, being sulphur and carbon, have been obtained from post-flight analysis of IAGOS-CARIBIC aerosol samples. This information together with literature data on black carbon concentrations were used to calculate the aerosol backscattering which subsequently is compared with measurements by CALIOP. Vertical optical profiles were taken in an altitude range of several kilometres from and above the northern hemispheric extratropical tropopause for the years 2006-2014. We find that the two vastly different measurement platforms yield different aerosol backscattering, especially close to the tropopause where the influence from tropospheric aerosol is strong. The best agreement is found when the LMS is affected by volcanism, i.e., at elevated aerosol loadings. At background conditions, best agreement is obtained some distance (>2 km) above the tropopause in winter and spring, i.e., at likewise elevated aerosol loadings from subsiding aerosol-rich stratospheric air. This is to our knowledge the first time the CALIPSO lidar measurements have been compared to in-situ long-term aerosol measurements.

Predicting dielectric constants of pure liquids: fragment-based Kirkwood-Fröhlich model applicable over a wide range of polarity

In view of developing a procedure to predict the dielectric constant (ε_r) of pure liquids from molecular structure, a thorough analysis of prominent factors affecting this property is carried out. The results suggest that the orientational dipolar parameter gμ² involved in the Kirkwood-Fröhlich theory may be estimated as a sum of additive contributions (gμ²)_i associated with suitably defined polar fragments i. Associated with third-party models for the molar volume V_m and the refractive index n_D, this provides a practical route to predicting ε_r for new compounds. Advantages over previous methods include: simplicity, as the present model relies on fragment-additivity and does not require 3D structures; sound physical bases; demonstrated applicability to polar liquids with ε_r values up to 200; predictive ability extensively demonstrated against large datasets (for a total of 1220 compounds) covering a broad structural diversity, resulting in values of the root mean square deviation/average percent error as low as 3.7/10% for data sets focused on simple organic compounds as considered in previous studies, although the inclusion of many alcohols in the data set leads to poorer statistics (5.0/32%) due to the lack of specific parameters for hydroxyl groups in distinct environments. The approach should be of special interest in the current search for new aprotic electrolytes aimed at improving the performances of electrochemical energy storage systems. Although its reliance on many fitting parameters restricts its domain of applicability, the present implementation is recommended over current procedures whenever possible. A Python script is provided to allow its straightforward application.

Infrared intensities and molar refraction of amorphous dimethyl carbonate – comparisons to four interstellar molecules

The first measurements of infrared (IR) band intensities of solid dimethyl carbonate are presented along with measurements of this compound's refractive index and density near 15 K, neither of which has been reported.

Pencil and Paper Estimation of Hansen Solubility Parameters

Simple procedures to estimate Hansen solubility parameter (HSP) components from structural formulas are investigated. The best results are obtained using a simple relationship with molar volume and refractivity for the dispersion component, and using additivity models based on tailored fragments specifically designed for the polar and hydrogen bonding components. Despite large errors for some classes of chemicals, including small inorganic molecules, ionic liquids, and high halogen compounds, these models yield average absolute deviations from reference on par with state-of-the-art models and lower than reported using molecular dynamics simulations or nonlinear quantitative structure-property relationship models based on a limited set of quantum chemical descriptors. In contrast to group contribution methods that are either more restricted in scope or heavily parameterized, they are thoroughly validated and very easy to apply. Furthermore, the errors observed are easy to rationalize and may usually be anticipated. This work sheds light on some limitations inherent to pure additivity approaches for HSP prediction and provides a first step toward better models. A Python script implementing the procedure and the fully detailed results are provided as the Supporting Information.