Chemistry of Materials最新文献

Peptide-based crystalline nanomaterials with a well-defined growth mechanism remain an unexplored avenue for efficient cellular protein delivery. Herein, we report the formation of Ni(II)-promoted coiled-coil peptide nanocrystals that demonstrate periodic banding and open hexagonal packing. Mechanistic experiments provide insights into the thermodynamic and kinetic interactions involved in crystal growth. Further, metal–ligand interactions facilitate protein inclusion within the crystals, and surface modification with a His-tagged cell-penetrating peptide was harnessed to achieve enhanced protein delivery to cells. As such, an understanding of coiled-coil interactions in nanocrystals may enable the development of modular morphologies via controlled crystal growth with an expansion of biomedical applications.

Donor–acceptor (D:A) cocrystals offer a promising platform for next-generation optoelectronic applications, but the impact of residual solvent molecules on their properties remains an open question. We investigate six novel D:A cocrystals of dibenzotetrathiafulvalene (DBTTF) and 1,4,5,8,9,11-hexaazatriphenylenehexacarbo-nitrile (HATCN), prepared via solvent evaporation, yielding 1:1 molar ratios, and horizontal vapor deposition, resulting in solvent-free 3:2 cocrystals. Combining spectroscopy and density-functional theory (DFT) calculations, we find that, while the electronic and optical properties of the cocrystals are largely unaffected by solvent inclusion, the charge-transfer mechanism is surprisingly complex. Raman spectroscopy reveals a consistent charge transfer of 0.11 e across all considered structures, corroborated by DFT calculations on solvent-free systems. Partial charge analysis reveals that in solvated cocrystals, solvent molecules actively participate in the charge-transfer process as primary electron acceptors. This involvement can perturb the expected D:A behavior, revealing a faceted charge-transfer mechanism in HATCN even beyond the established involvement of its cyano group. Overall, our study demonstrates that while solution-based methods preserve the intrinsic D:A characteristics, solvents can be leveraged as active electronic components, opening new avenues for material design.

Low-dimensional materials have attractive properties that drive intense efforts toward the discovery of novel materials. However, experiments are tedious for systematic discovery, and the present computational methods are often tuned to two-dimensional (2D) materials, overlooking other low-dimensional materials. Here, we combined universal machine-learning interatomic potentials (UMLIPs) and an advanced, interatomic force constant (FC)-based dimensionality classification method to make a massive discovery of novel low-dimensional materials. We first benchmarked the UMLIPs’ first-principles-level accuracy in quantifying FCs and calculated phonons for 35,689 materials from the Materials Project database. We then used the FC-based method for dimensionality classification to discover 9139 low-dimensional materials, including 1838 0D clusters, 1760 1D chains, 3057 2D sheets/layers, and 2484 mixed-dimensional materials, all of which conventional geometric descriptors have not recognized. By calculating the binding energies for the discovered 2D materials, we also identified 887 sheets that could be easily or potentially exfoliated from their parent bulk structures.

We unravel the lithiation/delithiation mechanism of Zn₂SnO₄ as a conversion-type negative electrode material in a lithium-ion battery cell by rigorous phase identification via ex situ ⁷Li magic-angle spinning (MAS) NMR, static ¹¹⁹Sn WCPMG NMR, and operando X-ray diffraction (XRD) techniques. With ongoing lithiation/delithiation, a cascade of Li_xZn phases is observed, and the ⁷Li shift for the formed Li_xZn phases is reported for the first time. Our results show that Zn undergoes an alloying/dealloying-type reaction during electrochemical lithiation/delithiation according to xLi + Zn ↔ Li_xZn (0 ≤ x ≤ 1). However, considering the Sn alloying/dealloying reaction, the ¹¹⁹Sn WCPMG NMR results indicate that the formed Li_xSn species differ from those expected for the lithiation of metallic Sn; hence, the alloying/dealloying processes differ from those known for Sn: xLi + Sn ↔ Li_xSn (0 ≤ x ≤ 4.4), indicating a more complicated conversion mechanism for Zn₂SnO₄. Furthermore, the ¹¹⁹Sn WCPMG NMR data of the delithiation reaction reveal the formation of amorphous SnO caused by partial oxidation of reformed Sn.

This work reveals the structural evolution and transport behavior of chromium-substituted Ba₂In₂O₅ (BIO) as a mixed ionic electronic conductor for oxygen transport membranes. Controlled substitution of In³⁺ by Cr⁶⁺ induces a transition from an orthorhombic brownmillerite to an on average cubic defect-perovskite (ABO_3−δ) phase while suppressing the high-temperature phase transformations typical of undoped BIO. A comprehensive set of structural and spectroscopic techniques confirms the stabilization of Cr⁶⁺ in the lattice and its function as a donor dopant. The aliovalent substitution introduces additional electrons while reducing the oxygen-vacancy concentration in the lattice, resulting in increased electronic and decreased ionic conductivities. The composition with x = 0.1 achieves a well-balanced contribution from ionic and electronic carriers, yielding the highest ambipolar conductivity and oxygen permeation flux among the studied samples. At higher substitution levels (e.g., x = 0.2), where In³⁺ and Cr⁶⁺ coexist on the B-site of the perovskite framework, a coupled donor/acceptor system (Cr⁶⁺/In³⁺) is formed, giving rise to complex charge compensation mechanisms and mixed electronic conduction. These findings provide fundamental insights into the crystal structure, defect chemistry, and charge transport mechanisms in Cr-substituted BIO, offering a rational design strategy for efficient oxygen transport membranes.

Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo⁶⁺, V⁵⁺) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.

Molecular doping of conjugated polymers (CPs) is a key strategy for improving the performance of organic electronics devices, particularly thermoelectrics. Doped donor–acceptor (D–A) conjugated polymers, characterized by a tunable energy gap between the Fermi level and the transport band, show great promise in achieving high electrical conductivity (σ) while preserving a favorable Seebeck coefficient (S). Despite the promising performance enhancement of chemically doped D–A polymers, their thermal stability remains largely underexplored, a crucial consideration for the long-term operation of organic thermoelectric devices. In this study, we investigated the dopant size-dependent thermal stability of a diketopyrrolopyrrole-thiophene (DPP-T) D–A copolymer, utilizing two p-dopants: 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F₄TCNQ) and Mo(tfd-CO₂Me)₃. Temperature-dependent UV–vis–NIR spectroscopy revealed that DPP-T/F₄TCNQ is more prone to dedoping under a high temperature thermal stress than DPP-T/Mo(tfd-CO₂Me)₃. Although the F₄TCNQ doped polymer shows higher initial in-plane conductivity than its Mo(tfd-CO₂Me)₃ counterpart, it undergoes a conductivity loss of more than an order of magnitude after annealing at 120 °C for 30 min. In contrast, the in-plane conductivity of DPP-T/Mo(tfd-CO₂Me)₃ remains stable under the same thermal conditions. Thermogravimetric analysis ruled out dopant sublimation as a primary contributor to dedoping, leading us to attribute the conductivity loss in F₄TCNQ-doped DPP-T to dopant phase separation and migration. This observation was further confirmed by X-ray scattering studies and nanoscale infrared microscopy and spectroscopy studies. This work could provide further insights into the thermal stability of doped conjugated polymers and suggests that incorporating bulkier dopants is an effective strategy to enhance the thermal robustness of doped DPP-type systems.

The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling the improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition (ALD) remains limited to a few pioneering studies. In this perspective, we take a first step toward closing this gap and bringing machine learning closer to ALD by introducing the key concepts of relevant algorithms and workflows, surveying the current literature, and outlining challenges and future directions for applying machine learning in ALD. We provide ideas on how the field can proceed to harvest the full potential of machine learning-based approaches for ALD which promises to enable precursor and material design as well as strongly improved computational and experimental approaches for atomic layer processing.