Crystal Field Effects on Atomic and Functional-Group Distributed Polarizabilities of Molecular Materials

Machine-learning-assisted SERS nanosensor platform toward chemical fingerprinting of Baijiu flavors

A novel fingerprinting platform for multiplex detection of flavor molecules in Baijiu was developed by using a surface-enhanced Raman scattering (SERS) nanosensor array in combination with machine learning. The SERS sensors were constructed by core-shell Fe₃O₄@Ag nanoparticles modified with molecules carrying end-groups of hydroxyl, pyridyl, methyl, and amino, respectively, which interacted with flavors and led to changes in the sensors' spectra. All the Raman spectra acquired from the nanosensor array contacting with the sample were concatenated into a single SERS super-spectrum, representing the flavor fingerprint which was recognized through machine learning. Principal component analysis, support vector machine, and partial least squares were utilized to build classification and quantitation models for predictive analyses. The SERS nanosensor array was successfully used for fingerprinting ten typical flavors in Baijiu including four esters, three alcohols, and three acids, with an accuracy of 100%, linear detection ranges over two orders of magnitude, and limits of detection ranging from 3.45 × 10^-3 mg/L of phenylethyl acetate to 1.21 × 10^-2 mg/L of ethyl hexanoate. It was also demonstrated that satisfactory accuracies (recoveries) ranging from 96.2 to 104% and relative standard deviations ranging from 0.65 to 2.78% were obtained for the simultaneous quantification of 3-methylbutyl acetate and phenylethyl acetate in eighteen Baijiu samples of three flavor types including sauce flavor, strong flavor, and light flavor. Compared with the existing detection techniques, this chemical fingerprinting platform is easy to use, highly sensitive, and can perform multiplex detection, which has great potential for practical applications.

A building-block database of distributed polarizabilities and dipole moments to estimate optical properties of biomacromolecules in isolation or in an explicitly solvated medium

Since atomic or functional-group properties in the bulk are generally not available from experimental methods, computational approaches based on partitioning schemes have emerged as a rapid yet accurate pathway to estimate the materials behavior from chemically meaningful building blocks. Among several applications, a comprehensive and systematically built database of atomic or group polarizabilities and related opto-electronic quantities would be very useful not only to envisage linear or non-linear optical properties of biomacromolecules but also to improve the accuracy of classical force fields devoted to simulate biochemical processes. In this work, we propose the first entries of such database that contains distributed polarizabilities and dipole moments extracted from fragments of peptides. Twenty three prototypical conformers of the dipeptides alanine-alanine and glycine-glycine were used to extract functional groups such as CH₂ , CHCH₃ , NH₂ , COOH, CONH, thus allowing construction of a diversity of chemically relevant environments. To evaluate the accuracy of our database, reconstructed properties of larger peptides containing up to six residues of alanine and glycine were tested against density functional theory calculations at the M06-HF/aug-cc-pVDZ level of theory. The procedure is particularly accurate for the diagonal components of the polarizability tensor with errors up to 15%. In order to include solvent effects explicitly, the peptides were also surrounded by a box of water molecules whose distribution was optimized using the CHARMM force field. Solvent effects introduced by a classical dipole-dipole interaction model were compared to those obtained from polarizable-continuum model calculations.

Distributed functional-group polarizabilities in polypeptides and peptide clusters toward accurate prediction of electro-optical properties of biomacromolecules

CONTEXT

Aiming at accurately predicting electro-optical properties of biomolecules, this work presents distributed atomic and functional-group polarizability tensors for a series of polypeptides and peptide clusters constructed from glycine and its residuals. By partitioning the electron density using the quantum theory of atoms in molecules, we demonstrated a very good transferability of the group polarizabilities. We were able to identify and extract the most efficient functional groups capable of generating the largest electrical susceptibility in condensed phases. Both the isotropic polarizability and its anisotropy were used to understand the way functional groups act as sources of linear optical responses, how they interact with each other reinforcing the macroscopic optical behavior within the material, and how covalent bonds and non-covalent interactions, such as hydrogen bonds, determine refractive indices and birefringence. Particular attention is devoted to the peptide bonds as they provide links to build biomacromolecules or polymers. An adequate quantum-mechanical treatment of at least the first interaction sphere of a given functional group is required to properly describe the effects of mutual polarization, but we identified optimum cluster size and shape to better estimate polarizabilities and dipole moments of larger molecules or molecular aggregates from the knowledge of the electron density of a central molecule or amino acid residual that is representative of the bulk. The strategy outlined here is a fast yet effective tool for estimating the optical properties of proteins but could eventually find application in the rational design of optical organic materials as well.

METHODS

Electronic-structure calculations were performed on the Gaussin16 program at the DFT level using the CAMB3LYP functional and the double-ζ quality Dunning basis set aug-cc-pVDZ. Electron density partitioning followed the concepts of the Quantum Theory of Atoms and Molecules (QTAIM) and was performed using the AIMAll program. The locally developed Polaber routine was applied to calculate dipole moment vectors and polarizability tensors. It was amended to include the effects of the local field on a given central molecule by means of a modified Atom-Dipole Interaction Model (ADIM).

Benchmark of a functional-group database for distributed polarizability and dipole moment in biomolecules

The extraction of functional-group properties in condensed phases is very useful for predicting material behaviors, including those of biomaterials. For this reason, computational approaches based on partitioning schemes have been developed aiming at rapidly and accurately estimating properties from chemically meaningful building blocks. A comprehensive database of group polarizabilities and dipole moments is useful not only to predict the optical properties of biomacromolecules but also to improve molecular force fields focused on simulating biochemical processes. In this work we benchmark a database of distributed polarizabilities and dipole moments for functional groups extracted from a series of polypeptides. This allows reconstruction of a variety of relevant chemical environments. The accuracy of our database was tested to predict the electro-optical properties of larger peptides and also simpler amino acids for which density functional theory calculations at the M06-HF/aug-cc-pVDZ level of theory was chosen as the reference. This approach is reasonably accurate for the diagonal components of the polarizability tensor, with errors not larger than 15-20%. The anisotropy of the polarizability is predicted with smaller efficacy though. Solvent effects were included explicitly by surrounding the database entries by a box of water molecules whose distribution was optimized using the CHARMM force field.

Accurate Atom-Dipole Interaction Model for Prediction of Electro-optical Properties: From van der Waals Aggregates to Covalently Bonded Clusters

This work aims at the accurate estimation of the electro-optical properties of atoms and functional groups in organic crystals. A better understanding of the nature of building blocks and the way they interact with each other enables more efficient prediction of self-assembly, and thus physical properties in condensed matter. We propose a modified version of an atom-dipole interaction model that is based on atomic dipole moments calculated from the quantum theory of atoms in molecules. The method is very reliable for the prediction of various optical and electric properties in diverse chemical environments, ranging from hydrocarbon molecules bonded by dispersive interactions to polar rings connected by hydrogen bonds, or even polymeric structures whose monomers are covalently linked. Distributed polarizabilities and electrostatic potentials are compared to those obtained using a complete quantum-mechanical approach on finite-size aggregates. Our electrostatic approximation recovers isotropic polarizabilities with an accuracy of ca. 5 au and electrostatic potentials of ca. 0.05 au, even in the worst-case scenario in which polarization and charge-transfer effects are large. Functional groups are highly exportable, estimating the properties of small peptides and polyaromatics with a maximum deviation as low as ca. 15%.