The Journal of Physical Chemistry C最新文献

Bialkali antimonide photocathodes, such as cesium potassium antimonide (CsK₂Sb), have emerged as strong candidates for next-generation photocathodes in linear accelerators due to their low work function, fast response, high quantum yield, and visible-light sensitivity. However, a critical gap remains in understanding how defects─unavoidable in real materials and responsible for driving nonstoichiometric phases─affect these desirable photoemissive properties. Most theoretical studies have so far considered only perfectly stoichiometric crystals, overlooking the role of imperfections introduced during synthesis. Here, we address this gap using state-of-the-art first-principles calculations to explore both ideal and defected CsK₂Sb. For the stoichiometric phase, our results confirm strong absorption in the visible range and a significantly reduced work function compared to metallic photocathodes, consistent with experiments. Moving beyond the ideal case, we identify cesium and potassium vacancies as the most prevalent intrinsic defects. These vacancies might introduce midgap states, reshape the absorption spectrum, and are poised to strongly influence photoemission efficiency. By connecting intrinsic defects to performance, this work advances fundamental understanding of CsK₂Sb and provides practical insights for optimizing high-efficiency photocathodes.

Materials design for wider organic electronics greatly benefits from supramolecular chemistry approaches, whereby elucidating structure-driven noncovalent interactions across length scales represents a characterization challenge. Methods probing long-range order often struggle to identify the heterogeneous nature of van der Waals interactions, leaving locally disordered regions and their impact on bulk properties unexamined. This study demonstrates that structural polymorphism in regioisomeric pyridine-substituted naphthalene diimide (PyNDI) assemblies imparts varying degrees of order and disorder. A synergistic combination of X-ray diffraction (XRD) and solid-state nuclear magnetic resonance (ssNMR) spectroscopy, supported by computational modeling, reveals that supramolecular order in ortho (o)- and para (p)-PyNDIs arises from cooperative π-stacking, lone-pair-π interactions, and lamellar interdigitation. In contrast, the meta (m)-isomer exhibits local disorder with different packing motifs that can be iteratively modeled using the structural knowledge obtained from their o- and p-PyNDIs crystalline analogues. The approach uniquely resolves locally disordered motifs by leveraging information obtained from structurally similar, but well-ordered, isomers of π-conjugated assemblies relevant to the optoelectronic paradigm.

Pseudocapacitors (PSCs) based on small organic molecule materials have attracted significant research interest due to their structural diversity, tunable redox properties, and ability to support widening the operational potential window, offering higher energy density and rapid power density. Nevertheless, the progress is hindered by their unsatisfactory cycling stability. An approach to tailoring the cycling life of the organic electrode materials for supercapacitors (SCs) is extending π-conjugation of the molecular architecture. To improve the overall performance of the supercapacitor device, the electrode materials, in particular conjugated donor–acceptor–donor (D–A–D)-type molecules, can store both positive and negative charge in a pseudocapacitor device. The D–A–D-type conjugation with good packing could facilitate charge transport and enhance the electrochemical characteristics of the SC cell configurations. In this work, new D–A–D-type 2,7-bis(10-(2-ethylhexyl)-10H-phenothiazin-3-yl) benzo[lmn][3,8]phenanthroline-1,3,6,8(2H,7H)-tetraone (PTZ-NDI-EH) and 2,7-bis(10-(2-hexyldecyl)-10H-phenothiazin-3-yl)benzo[lmn][3,8]phenanthroline-1,3,6,8(2H,7H)-tetraone (PTZ-NDI-HD) bearing two different alkyl chains are designed and synthesized. The optimized PTZ-NDI-EH and PTZ-NDI-HD active organic electrode materials in combination with conducting graphite foil (GF) exhibit outstanding functionalities such as higher specific capacitance, higher energy density, and longer cycling stability. Such higher PSC performance could be originating from the molecular packing, higher electronic conductivity, and enhanced charge delocalization during the Faradaic reversible redox processes. The higher cycling stability could be attributed to efficient ion transportation. In addition, the alkyl chain length influences the electrochemical properties of PTZ-NDI-EH and PTZ-NDI-HD. The increase in contact angle of PTZ-NDI-HD (higher alkyl chain length) leads to the reduction of the specific capacitance compared to PTZ-NDI-EH (lower alkyl chain length). This provides the basis for the wettability of the electrode in the presence of aqueous electrolytes and their impact on the electrochemical properties. These results indicate that the D–A–D materials design and their applications contribute to the development of high-performance electrical energy storage (EES) devices.

In Raman spectroscopy, the optimal signal is obtained when the sample is irradiated by a laser at normal incidence. Ensuring both signal stability and high sensitivity for capturing analyte molecules on nonflat or tilted substrates remains difficult, especially in the process of rapid testing. Here, 3D hemispherical polydimethylsiloxane (PDMS) substrate with bioinspired compound-eye structure was fabricated by utilizing the liquid–liquid interface self-assembly and transfer technique. Using Au monolayer films decorated PDMS substrate as a surface-enhanced Raman scattering (SERS) substrate. The rotational symmetry of the bioinspired SERS substrate architecture enables stable SERS performance even under substrate tilting from 0 to 75°, demonstrating excellent suitability for field-based detection applications. The hemispherical SERS substrate demonstrated a minimum detectable urea concentration of 10^–5 M, which is significantly lower than the minimum urea level (10^–3 M) in the tear fluid of the body.

Ensuring the stability of the AuNP-gene complex until it reaches the target sites is a crucial factor for the success of gene therapy. Although different AuNP sizes and AuNP-to-DNA ratios are investigated for specific therapeutic needs, their role in the stability and packaging of AuNP-DNA complex remains unclear. In this study, we employ all-atom molecular dynamics simulations to investigate the influence of cationic ligand-functionalized AuNP (CAuNP) size and CAuNP-to-DNA ratio on DNA wrapping and binding affinity. The obtained results show that a single DNA interacting with smaller CAuNPs exhibits greater bending and wrapping due to their higher curvature. However, when two DNAs bind to smaller CAuNPs, electrostatic repulsion prevents the effective wrapping, which leads the DNAs to twist from their original orientation. Such behavior is not observed with larger CAuNPs since their increased size may mitigate repulsive forces. Further, the analysis on axial bending angle reveals that smaller AuNPs induce sharper DNA bending and larger AuNPs promote smoother bending. In addition, the potential of mean force (PMF) analysis confirms a stronger DNA binding affinity for larger AuNPs, with affinity decreasing when two DNAs attach to a single CAuNP. Our results from the DNA loading capacity calculations provide insights into the maximum number of DNA molecules that can be loaded onto CAuNPs of a given size. These findings offer key insights into optimizing the size of AuNP and DNA-to-AuNP ratios for the development of efficient gene delivery systems.

Developing catalysts based on graphene heterostructures is an attractive aspect for advancing the application of graphene. Our extensive first-principles calculations and ab initio molecular dynamics simulations reveal that the catalytic capability of graphene/Cu heterostructures for the electrochemical oxygen evolution reaction (OER) can be activated and significantly enhanced by the synergistic effect of introducing Stone–Wales (SW) defects into graphene and applying biaxial compressive strains to the heterostructures. The overpotential of the SW-defected graphene/Cu heterostructure for the OER decreases to 0.39 V under a biaxial compressive strain of −3%, which is lower than most theoretical overpotentials obtained when using graphene heterostructures as catalysts. The alteration and improvement in the catalytic capability of SW-defected graphene/Cu heterostructures under compressive strains are mainly attributed to the facilitated desorption of intermediates on graphene, the decreased reaction activation energy, and the charge transfer from the SW defect sites to the Cu substrates.

Scandia (Sc) and yttria (Y) codoped zirconia (ScYSZ) emerged as a promising candidate for high-performance solid electrolyte materials utilized in intermediate-temperature solid oxide fuel cells (IT-SOFCs). While it exhibits a record-high ionic conductivity (∼0.10 S/cm at 800 °C), the physical origin of the superior performance remains poorly understood, limiting the further optimization and the application in IT-SOFC. Here, we construct the Sc–Y–Zr–O global neural network potential and explore systematically the thermodynamic landscape of ScYSZ across 65 different compositions (6.7–14.3 mol % dopants). From millions of candidate structures, we identify a thermodynamically stable cubic phase region at Sc/Y < 1 with Y₂O₃ ≥ 8 mol %. Large-scale molecular dynamics simulations further show that ScYSZ at Sc₂O₃ = 3 mol % and Y₂O₃ = 8 mol % yields an exceptional ionic conductivity of 0.13 S/cm at 800 °C, surpassing conventional 8 mol % Y-stabilized zirconia (YSZ) by an order of magnitude. Our analysis reveals that the presence of Sc not only increases the O_v concentration by allowing ⟨111⟩ O_v–O_v pairs but also reduces the oxygen migration barriers markedly. Our results not only pinpoint the optimal ScYSZ composition for IT-SOFC applications theoretically but also establish a general framework for the rational design of advanced solid electrolyte materials.

Electrochemistry lies at the heart of modern energy technologies, yet connecting atomic-level insights to macroscopic performance remains an enduring challenge. Quantum-based simulations, such as density functional theory, have illuminated many fundamental processes, but their reach is limited by the complexity of real electrochemical environments. Bridging these scales requires a new conceptual framework that can expose the hidden connections between theory and experiment. Here, we argue that the thoughtful integration of artificial intelligence (AI) can transform electrochemical research by unifying theory, experiment, and data-driven inference. AI-assisted frameworks can accelerate convergence between computation and experiment, revealing hidden physical relationships and enabling closed-loop discovery. Realizing this vision will require developing transparent, interpretable AI models that earn the same scientific trust as human reasoning, unlocking deeper understanding and innovation across the electrochemical sciences.

Materials consisting of organic dye molecules play an important role as pigments, as active layer in organic light emitting diodes or solar cells, and as bistable medium in optical transistors and switches. Yet there is currently no working protocol to accurately predict one of the most basic optical properties of these solids: their reflection spectrum. Here we develop a method to calculate the reflection spectrum of crystals of dye molecules based on the crystal structure and optical absorption of the dye in solution. We treat the interaction between light and matter in the crystals as strong. Electromagnetic four-potentials are gauged consistently, and their boundary conditions at the reflecting interface are derived. Finally, we include both excitonic and charge transfer interactions between molecules in the crystals. We test our approach on a large data set of reflection spectra and crystal structures including several industrial pigments.

Varying the rate at which pressure is applied to a crystal is known to yield different pressure-induced polymorphic structures in experiments. In this work, we investigate the effect of pressure increase rate on pressure-induced polymerization in crystalline acrylamide, using room temperature constant pressure ab initio molecular dynamics simulations. Simulations performed with two different compression rates revealed very different structural evolutions of the system at lower pressures. Fast (nonequilibrated) pressure increase yields disordered (polymer) structures with unanticipated linkages for pressures up to 67 GPa. On the other hand, slow (quasi-static) pressure increase gives no new structures until 64 GPa. At pressures greater than 67 GPa, both pathways converge toward an ordered 3-dimensional polymer through a hierarchical mechanism involving 1-dimensional polymeric intermediates. The structural and electronic details of the mechanisms leading to polymerization are discussed.