Understanding
the complex relationships among molecular structures,
weak solute–solvent interactions, and dissolving affinity is
fundamentally important to successfully identify potential solvents
from a large chemical space for diverse absorption capture and separation.
In this study, a series of compounds including alkanolamines, amines
with cyclic substituents, etheramines, polyamines, sulfonamides, and
several commercial physical absorption solvents were selected and
methyl mercaptan (MeSH) was used as a model organosulfide for both
the quantum chemistry calculation and solubility measurement. The
weak intermolecular interactions within different solvent–solute
systems were examined by using reduced density gradient and quantum
theory of atoms in molecules analyses. The relationship between the
structural characteristics of the solvents and their interactions
with MeSH was revealed, and the intermolecular interaction is correlated
to the dissolubility of MeSH-in-solvent. Rules for designing molecules
were proposed and used to guide the generation of a potential solvent
with enhanced dissolving affinity to MeSH, 1-(2-(diethylamino)ethoxy)butan-2-amine.
It is indicated that the designed compound has stronger intermolecular
interaction with MeSH as compared to the screened solvent candidate.
The present study enables efficient molecular exploration and design
of solvent candidates for absorption capture of diverse environmental
unfriendly compounds as well as wide separation applications based
on selective dissolving.
Metal–organic
frameworks (MOFs) have been finding increasing
involvements in the capture of environmentally unfriendly compounds
including volatile organosulfides contained in a wide range of energy
sources owing to their editable structures. In this study, modulation
of interaction and diffusion for sulfide adsorption on Cu-BTC frameworks
was achieved via doping a series of transition-metal ions (Ni, Co,
Fe, Zn, and Cr) at a low content through post-synthesis in an ethanol
system. Synthesized samples were characterized, and dimethyl disulfide
(DMDS) adsorption on different metal-doped structures were carefully
studied through adsorption measurements combined with computational
simulation. The results indicate that the transition-metal doping
into the Cu-BTC structure at low content not only enhances the guest–host
interaction via altering the cation compositions (introducing external
ions along with reduction of Cu(II) to Cu(I)) but also promotes pore
diffusion of DMDS through modifying porosities of the porous architectures.
The present study highlights the tunable modification of the metal–organic
framework structures via low metal doping and provides insights into
the function-oriented structural optimization of their infrastructures.
Non-bonding intermolecular interactions largely dominate
the selective
dissolution of trace species into physical solvents and, therefore,
are fundamentally important to solvent development for the capture
of environment-undesired compounds or purification of chemicals. However,
acquirement of the interaction energy requires costly quantum chemical
computation and still encounters a practical challenge to build a
chemically interpretable machine learning (ML) prediction model using
documented molecular descriptors. Herein, we report an ML model for
predicting the interaction energies (E
int) between dimethyl sulfide and potential absorbing solvents. Applying
the reduced density gradient and quantum theory of atoms in molecules
analyses, the non-bonding intermolecular interactions of dimethyl
sulfide with solvent compounds were elucidated through focusing on
the molecular fragments containing the main center (MC) and secondary
center (SC) rather than the whole molecule. The training data set
was obtained using a molecular generation strategy, and 21 molecular
descriptors were defined to describe the electronic states of the
central atoms in each solvent molecule and its nearby hydrogen-bond
donors. Model analysis reveals that E
int is mainly determined by the charge state of the crucial fragments
and the hydrogen-bond donors of the solvent molecule. The custom-defined
descriptors not only improve the regression and prediction performance
but also enable the interpretability of the ML model. Additionally,
the absorption equilibrium measurements of solubilities of dimethyl
sulfide in several commercial solvents verified the strong correlation
between the dissolving affinity and solute–solvent intermolecular
interaction energy. The present study provides an approach to building
practical and interpretable intelligent algorithms to aid the development
of sustainable chemicals or processes.
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