Arsalan Hashemi

Organic electrolytes for aqueous redox flow batteries must be able to complete full redox cycles. Simple changes in the molecular design, such as adding a ring fusion, can result in irreversible steps in the redox cycle.

Atomic substitution provides a controlled route to engineer lattice dynamics in low-symmetry two-dimensional materials. Here, by combining polarization-resolved Raman spectroscopy and first-principles calculations, we investigate the evolution of phonon characteristics in CrSBr 1 − x Cl x ( 0 ⩽ x ⩽ ∼ 0.5 ) upon partial substitution of Br with Cl atoms. Progressive Cl substitution of Br induces systematic shifts of parent CrSBr out-of-plane A g phonon modes and activates additional Raman features. These features persist across different polarization configurations and excitation energies, reflecting substitution-induced symmetry lowering and local lattice perturbations. Explicit supercell phonon calculations combined with Raman Γ -density-of-states simulations identify these features as symmetry-lowered descendants of parent modes arising from alloy disorder. Complementary strain-dependent calculations reveal that anisotropic lattice compression plays a key role in renormalizing Cr–S dominated phonons. Under near-resonant excitation, stimulated Raman scattering-like amplification remains observable with increasing Cl content, highlighting the resilience of anisotropic electron–phonon coupling in this system.

When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interlayer interactions can strongly influence their physicochemical properties. These effects are particularly pronounced when the interface exhibits local order and near-perfect structural alignment, giving rise to moiré patterns. Using density-functional theory calculations, we investigate a bilayer heterostructure composed of hexagonal boron nitride (hBN) and silicon carbide (SiC). We predict that introducing a boron vacancy, V B , at specific lattice sites alters the interlayer interaction from weak vdW coupling to localized silicon-nitrogen covalent bonding. Motivated by this mechanism, we examine the binding of transition-metal adatoms and identify principles for enhancing surface reactivity and stabilizing isolated single-metal atoms. Machine-learning molecular dynamics at finite temperature further demonstrate rapid and effectively irreversible trapping of Cu at V B sites, and show how the V B :Cu ratio governs the transition from single-atom isolation to vacancy-directed aggregation. These results suggest the hBN/SiC heterostructure as a versatile platform for atomically precise transition-metal functionalization, with implications for catalytic energy-conversion materials.

When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interactions between adjacent layers significantly influence their physicochemical properties. These effects seem particularly pronounced when the interface exhibits local order and near-perfect structural alignment, leading to the emergence of Moiré patterns. Using quantum mechanical density functional theory calculations, we propose a prototypical bilayer heterostructure composed of hexagonal boron nitride (hBN) and silicon carbide (SiC), characterized by a lattice mismatch of 18.77\% between their primitive unit cells. We find that the removal of boron atoms from specific lattice sites can convert the interlayer interaction from weak vdW coupling to robust localized silicon-nitrogen covalent bonding. Motivated by this, we study the binding of transition-metal adatoms and formulate design guidelines to enhance surface reactivity, thereby enabling the controlled isolation of single-metal atoms. Our machine-learning-assisted molecular dynamics simulations confirm both dynamical stability and metal anchoring feasibility at finite temperatures. Our results suggest the hBN/SiC heterostructure as a versatile platform for atomically precise transition-metal functionalization, having potential for next-generation catalytic energy-conversion technologies.

The scheme of squares is an effective way to explore the redox potential and protonation state of a molecule.

For many materials, Raman spectra are intricately structured and provide valuable information about compositional stoichiometry and crystal quality. Here we use density-functional theory calculations, mass approximation, and the Raman intensity weighted Γ -point density of state approach to analyze Raman scattering and vibrational modes in zincblende, wurtzite, and hexagonal BX (X = N, P, and As) structures. The influence of crystal structure and boron isotope disorder on Raman line shapes is examined. Our results demonstrate that long-range Coulomb interactions significantly influence the evolution of Raman spectra in cubic and wurtzite BN compounds. With the evolution of the compositional rate from B 11 to B 10 , a shift toward higher frequencies, as well as the maximum broadening and asymmetry of the Raman peaks, is expected around the 1:1 ratio. The calculated results are in excellent agreement with the available experimental data. This study serves as a guide for understanding how crystal symmetry and isotope disorder affect phonons in BX compounds, which are relevant to quantum single-photon emitters, heat management, and crystal quality assessments. Published by the American Physical Society 2025

The integration of charged polymers and reverse osmosis membranes presents a promising approach to capture undesired ions and enhance water purification efficiency. In this paper, di-block cationic polyacrylamides (DCPAMs) as charged polymers are evaluated separately in bulk solution and within a reverse osmosis process to capture nitrite ions introduced by NaNO3. To achieve this, molecular dynamics simulations are conducted to systematically investigate the effects of polymer configuration, concentration, and salt concentration on ion adsorption and water purification performance. The results reveal that nitrate ions exhibit strong adsorption to the charged polymers, leading to the displacement of chloride ions into the bulk solution. The polymer conformations are found to play a critical role in the performance of the process. When an electric potential is applied, anions are strongly attracted to the electrode with a total positive charge, and nitrate ions remain closer to the electrode surface than other ions. The simultaneous application of RO membranes and DCPAMs achieves salt rejection efficiencies ranging from 78% to 100%, depending on DCPAM type and salt concentration. These findings pave the way for further computational studies on combined processes to advance water purification technologies.

The water solubility of organic molecules is critical for optimizing the performance and stability of aqueous flow batteries, as well as for various other applications. Although relatively straightforward to measure in some cases, the theoretical prediction of the solubility remains a considerable challenge. To this end, machine learning algorithms have become increasingly important tools in the past decade. High-quality data and effective descriptors are essential for constructing reliable data-driven estimation models. We systematically investigate the effectiveness of enhanced structure-based descriptors and an outlier detection procedure for improving aqueous solubility predictability. We train and evaluate random forest regression models using various descriptors to predict experimental solubility. Outliers are identified through an iterative maximum-error deletion procedure. We discover that descriptors derived from hydration free energy and weighted fingerprints, along with other established features, are effective. Notably, solvation energy, octanol-water partition coefficient, atomic charge polarizability interactions, and the presence of a full-carbon aromatic ring are critical for solubility prediction. Furthermore, the effectiveness of the outlier detection protocol is validated by improving the performance of the model and detailed analysis of the dataset. This study significantly improves the predictive capacity of supervised machine learning for molecular properties, enabling advancements in various technological applications.

For many materials, Raman spectra are intricately structured and provide valuable information about compositional stoichiometry and crystal quality. Here we use density-functional theory calculations, mass approximation, and the Raman intensity weighted $Γ$-point density of state approach to analyze Raman scattering and vibrational modes in zincblende, wurtzite, and hexagonal BX (X = N, P, and As) structures. The influence of crystal structure and boron isotope disorder on Raman line shapes is examined. Our results demonstrate that long-range Coulomb interactions significantly influence the evolution of Raman spectra in cubic and wurtzite BN compounds. With the evolution of the compositional rate from $^{11}$B to $^{10}$B, a shift toward higher frequencies, as well as the maximum broadening and asymmetry of the Raman peaks, is expected around the 1:1 ratio. The calculated results are in excellent agreement with the available experimental data. This study serves as a guide for understanding how crystal symmetry and isotope disorder affect phonons in BX compounds, which are relevant to quantum single-photon emitters, heat management, and crystal quality assessments.