Carbon Dioxide Separation with a Two-Dimensional Polymer Membrane

Carbon dioxide gas separation is important for many environmental and energy applications. Molecular dynamics simulations are used to characterize a two-dimensional hydrocarbon polymer, PG-ES1, that uses a combination of surface adsorption and narrow pores to separate carbon dioxide from nitrogen, oxygen, and methane gases. The CO2 permeance is 3 x 10(5) gas permeation units (GPU). The CO2/N-2 selectivity is 60, and the CO2/CH4 selectivity exceeds 500. The combination of high CO2 permeance and selectivity surpasses all known materials, enabling low-cost postcombustion CO2 capture; utilization of landfill gas, and horticulture applications. --author-supplied descriptio

Fluorinated and Nanoporous Graphene Materials As Sorbents for Gas Separations

The physisorption of gases on surfaces depends on the electrostatic and dispersion interactions with adsorbates. The former can be tuned by introducing charge variations in the material, and the latter can be tuned by chemical substitution. Using atomistic Monte Carlo calculations, the Henry\u27s law constants, and isosteric heats of adsorption of CH4, CO2, N2, O2, H2S, SO2, and H2O on graphene, two-dimensional polyphenylene (2D-PP), fluorographene, and fluoro(2D-PP) surfaces are used to demonstrate the tunability of these two types of interaction. With the exception of H2O, fluorination and nanoporosity-induced charge variations reduce the binding of the adsorbates. Gas separations relevant for CO2 sequestration, biogas upgrading, SO2 pollution control, and air dehumidification are considered, and in most cases, the nanoporosity and fluorination reduce the selectivity of adsorption. The exceptions are separations involving adsorption of H2O and the SO2/N2 separation, where the large dipole moments of the adsorbed species leads to enhanced binding relative to the nonpolar species. --author-supplied descriptio

A Simple Model for Magnetization Ratios in Doped Nanocrystals

Recent experiments on Mn-doped ZnS nanocrystals have shown unusual magnetization properties. We describe a nearest-neighbor Heisenberg exchange model for calculating the magnetization ratios of these antiferromagnetically doped crystals, in which the dopant atoms are distributed inhomogeneously within the nanocrystal. This simple inhomogeneous doping model is capable of reproducing the experimental results, and suggests that interior dopant atoms are localized within the crystal.Comment: 8 pages, 1 figure, 2 tables. Submitted to J. Appl. Phy

Atomistic Theory of Coherent Spin Transfer between Molecularly Bridged Quantum Dots

Time-resolved Faradary rotation experiments have demonstrated coherent transfer of electron spin between CdSe colloidal quantum dots coupled by conjugated molecules. We employ here a Green's function approach, using semi-empirical tight-binding to treat the nanocrystal Hamiltonian and Extended Huckel theory to treat the linking molecule Hamiltonian, to obtain the coherent transfer probabilities from atomistic calculations, without the introduction of any new parameters. Calculations on 1,4-dithiolbenzene and 1,4-dithiolcyclohexane linked nanocrystals agree qualitatively with experiment and provide support for a previous transfer Hamiltonian model. We find a striking dependence on the transfer probabilities as a function of nanocrystal surface site attachment and linking molecule conformation. Additionally, we predict quantum interference effects in the coherent transfer probabilities for 2,7-dithiolnaphthalene and 2,6-dithiolnaphthalene linking molecules. We suggest possible experiments based on these results that would test the coherent, through-molecule transfer mechanism.Comment: 12 pages, 9 figures. Submitted Phys. Rev.

Theoretical Characterization of the Air-Stable, High-Mobility Dinaphtho[2,3-b:2'3'-f]-thiophene Organic Semiconductor

Recently, an optimum mobility of $8.3 cm^2/(Vs)$ has been measured for single-crystal organic field-effect transistors based on the dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]-thiophene (DNTT) molecule. Here, on the basis of quantum chemistry calculations and molecular dynamics simulations, we investigate the microscopic charge transport parameters of the DNTT molecule and crystal. Our findings confirm that the moderate anisotropy of the hole mobility in DNTT is highly dependent on the presence of in-plane herringbonelike intermolecular interactions with large electronic coupling (transfer integral) values (ca. 0.1 eV). Also, we demonstrate that the π-extended heteroaromatic structure leads to strong electronic coupling interactions among neighboring molecules and to a decrease of the intramolecular reorganization energy. In DNTT, thermal modulations of the electronic couplings at 300 K remain small when compared to those exhibited by the pentacene single crystal. This theoretical study suggests that heteroacenes are a promising route toward high-mobility organic semiconductor materials. Charge transport is discussed in the framework of both band and hopping models.Chemistry and Chemical Biolog

Mechanical and electronic-structure properties of compressed CdSetetrapod nanocrystals

The coupling of mechanical and optical properties insemiconductor nanostructures can potentially lead to new types ofdevices. This work describes our theoretical examination of themechanical properties of CdSe tetrapods under directional forces, such asmay be induced by AFM tips. In addition to studying the general behaviorof the mechanical properties under modifications of geometry,nanocrystal-substrate interaction, and dimensional scaling, ourcalculations indicate that mechanical deformations do not lead to largechanges in the band-edge state eigenenergies, and have only a weak effecton the oscillator strengths of the lowest energy transitions

Data-driven imputation of miscibility of aqueous solutions via graph-regularized logistic matrix factorization

Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how to impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization (GR-LMF) to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph (nodes: solutes, edges: shared relationships) indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). For an experimental dataset of the pairwise miscibility of 68 solutions from Peacock et al. [ACS Appl. Mater. Interfaces 2021, 13, 11449–11460], we find that GR-LMF more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers that use physicochemical features of the solutes. GR-LMF obviates the need for features of the solutions/solutes to impute missing miscibility outcomes, but it cannot predict the miscibility of a new solution without some observations of its miscibility with other solutions in the training data set