Morphology and Performance Enhancement through the Strong Passivation Effect of Amphoteric Ions in Tin-based Perovskite Solar Cells.

Despite the optoelectronic similarities between tin and lead halide perovskites, the performance of tin-based perovskite solar cells remains far behind, with the highest reported efficiency to date being ≈14%. This is highly correlated to the instability of tin halide perovskite, as well as the rapid crystallization behavior in perovskite film formation. In this work, l-Asparagine as a zwitterion plays a dual role in controlling the nucleation/crystallization process and improving the morphology of perovskite film. Furthermore, tin perovskites with l-Asparagine show more favorable energy-level matching, enhancing the charge extraction and minimizing the charge recombination, leading to an enhanced power conversion efficiency of 13.31% (from 10.54% without l-Asparagine) with remarkable stability. These results are also in good agreement with the density functional theory calculations. This work not only provides a facile and efficient approach to controlling the crystallization and morphology of perovskite film but also offers guidelines for further improved performance of tin-based perovskite electronic devices.

Thermodynamics of π-π Interactions of Benzene and Phenol in Water.

The π-π interaction is a major driving force that stabilizes protein assemblies during protein folding. Recent studies have additionally demonstrated its involvement in the liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs). As the participating residues in IDPs are exposed to water, π-π interactions for LLPS must be modeled in water, as opposed to the interactions that are often established at the hydrophobic domains of folded proteins. Thus, we investigated the association of free energies of benzene and phenol dimers in water by integrating van der Waals (vdW)-corrected density functional theory (DFT) and DFT in classical explicit solvents (DFT-CES). By comparing the vdW-corrected DFT and DFT-CES results with high-level wavefunction calculations and experimental solvation free energies, respectively, we established the quantitative credibility of these approaches, enabling a reliable prediction of the benzene and phenol dimer association free energies in water. We discovered that solvation influences dimer association free energies, but not significantly when no direct hydrogen-bond-type interaction exists between two monomeric units, which can be explained by the enthalpy-entropy compensation. Our comprehensive computational study of the solvation effect on π-π interactions in water could help us understand the molecular-level driving mechanism underlying the IDP phase behaviors.

Understanding gas adsorption in MOF-5/graphene oxide composite materials.

Metal-organic framework (MOF) and graphene oxide (GO) composite materials (MOF/GO) have been regarded as promising for separation applications due to their synergistically enhanced adsorption properties. Molecular-level understandings of these materials, however, remain unknown to date. In this study, molecular simulations were used, for the first time, to model these composite materials. Specifically, the composite MOF-5/GO material was modeled as stacks of sandwich-like layers on top of one another, consistent with experimental observations inferred from XRD and the SEM images. Simulations indicate that CO2 and CH4 bind strongly in the MOF/GO interface region, resulting in synergistically enhanced adsorption properties. To exploit the interface region, we found that in simulating linear alkanes, larger guest molecules show substantially improved adsorption properties in composites compared to the parent MOF-5 structure, illustrating that the performance of adsorption in these molecules will benefit the most from the MOF/GO composites.

Towards accurate porosity descriptors based on guest-host interactions.

For nanoporous materials at the characterization level, geometry-based approaches have become the methods of choice to provide information, often encoded in numerical descriptors, about the pores and the channels of a porous material. Examples of most common descriptors of the latter are pore limiting diameters, accessible surface area and accessible volume. The geometry-based methods exploit hard-sphere approximation for atoms, which (1) reduces costly computations of the interatomic interactions between the probe guest molecule and the porous material framework atoms, (2) effectively exploit applied mathematics methods such as Voronoi decomposition to represent and characterize porosity. In this work, we revisit and quantify the shortcoming of the geometry-based approaches. To do so, we have developed a series of algorithms to calculate pore descriptors such as void fraction, accessible surface area, pore limiting diameters (largest included sphere, and largest free sphere) based on a classical force field model of interactions between the guest and the framework atoms. Our resulting energy-based methods are tested on diverse sets of metal-organic frameworks and zeolite structures and comparisons against results obtained from geometric-based method indicate deviations in the cases for structures with small pore sizes. The method provides both high accuracy and performance making it suitable when screening a large database of materials.