Angewandte Chemie最新文献

Mixed tin–lead (Sn–Pb) perovskite solar cells (PSCs) are critical for advancing all–perovskite tandem solar technologies, as they resolve the efficiency limitations of single-junction devices. However, the widely used hole transport layer (HTL) poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) restricts performance and durability due to its acidic nature and moisture absorption. Additionally, the rapid crystallization of Sn-containing perovskites hinders the fabrication of uniform, high-quality mixed SnPb perovskite films. To address these challenges, this study incorporates 4-sulfophthalic acid triammonium salt (SATS) as a multifunctional additive into both the PEDOT:PSS layer and perovskite precursor solution. SATS modifies the physicochemical properties of PEDOT:PSS and slows perovskite crystallization, promoting films with enhanced crystallinity. Through these synergistic effects, the optimized single-junction SnPb PSCs achieve a power conversion efficiency (PCE) of 23.85%. Integrating these devices into two-terminal all-perovskite tandem architectures further delivers a remarkable efficiency of 28.74%.

A modification to Etter's graph-set notation for hydrogen-bond patterns in molecular crystals is proposed to enable the classification of supramolecular patterns based on any type of non-covalent interaction (such as hydrogen, halogen, chalcogen, pnictogen, and tetrel bonding). It is recommended that the modified notation be applied to supramolecular patterns based on interactions other than hydrogen bonds, while retaining the original notation for patterns based solely on hydrogen bonds.

The development of porous materials that retain tailored porosity across different physical states beyond crystalline solids (e.g., liquid or glassy) could yield new functional materials for diverse applications, yet it remains challenging. To address this, researchers have turned to discrete porous cages such as metal–organic cages (MOCs) and metal–organic polyhedra (MOPs). The organization and physical state of these materials are governed by inter-cage interactions that can be modulated without altering the intrinsic porosity of the individual cages. In this minireview, we highlight how the peripheral functionality of such cages governs their interactions and physical state and explain how it can be harnessed to preserve and transfer porosity across distinct physical states, including liquids, glasses, and rubbers. We conclude by outlining emerging properties and potential applications for the resultant unique porous states.

Circularly polarized light orchestrates a chiral symphony on Au@CdS nanocatalysts, guiding electrons by the rhythm of handedness. When light and the catalyst share the same chirality, spin-aligned carriers flow to produce hydrogen photocatalytically. When this harmony is disrupted, recombination converts energy into heat, revealing a photothermal pathway for hydrogen evolution. More in the Research Article (e202517047) by Jiatao Zhang, Yiou Wang, and co-workers.

Die intrazelluläre Zufuhr von trans-Cycloocten und Bicyclo[6.1.0]nonin als sterisch anspruchsvolle Gruppen für die Diels–Alder-Reaktion mit inversem Elektronenbedarf wurde durch die Triphosphat-Prodrug-Strategie (TriPPPro) erreicht. Nach der Hydrolyse innerhalb der Zelle werden die modifizierten Uridine und Cytidine metabolisch in die neu entstehende RNA eingebaut und mit einem doppelt-fluorogenen Tetrazin-Farbstoff markiert, um eine waschfreie, kontrastreiche Bildgebung der RNA Synthese in Zellen zu ermöglichen.

Topological duality offers a novel paradigm for designing catalysts with both high activity and stability. In their Research Article (e202516704), Zhongmin Su, Chunyi Sun, and co-workers report a Cu₂₄Ag₅₄ nanocluster, featuring a double-truncated cubic {Ag₅₄} core with exposed {Ag₃} units encapsulated within an octahedral {Cu₂₄} shell, achieving high Faradaic efficiency at high current density along with excellent durability towards electrochemical CO₂ reduction coupled with Cl^– oxidation.

Angew. Chem. Int. Ed. 2025, 64, e202508565, https://doi.org/10.1002/ange.202508565

In Table 1 of the main text and Table S5 of the Supporting Information, several kinetic parameters (k_cat, K_m, and k_cat/K_m) contained numerical inconsistencies caused by rounding and formatting during data tabulation. All values have been recalculated using the original raw data, and the corrected tables are provided at the end of this document as corrected Table 1 and corrected Table S5.

These updated values are also reflected in the following revised sentences on page 3 of the main text.

The original text,

“The normalized catalytic efficiency (k_catⁿ/K_m) for CO was 100 000 s⁻¹·mM⁻¹ in A559W/V610H, comparable to 126 000 s⁻¹·mM⁻¹ in A559W. Similarly, A559H/V610H showed a catalytic efficiency of 55 000 s⁻¹·mM⁻¹ for CO”

Was updated as:

“…comparable to 128 000 s⁻¹·mM⁻¹ in A559W…”

“…showed a catalytic efficiency of 54 000 s⁻¹·mM⁻¹…”

These corrections do not affect the results or conclusions of the article.

The authors apologize for these errors.

Corrected Table 1 (main text)

Corrected Table S5 (Supporting Information)

Validating chemical synthesis success requires confirming the desired product using various analytical techniques. While spectroscopic data collection is increasingly automated, interpreting results remains a major bottleneck, often requiring expert input. With advances in laboratory automation and high-throughput synthesis, this challenge is expected to intensify. We introduce the MultiModalSpectralTransformer (MMST), a machine learning method that predicts chemical structures directly from diverse spectral data (NMR, IR, and MS). Trained on 4 million simulated compounds, MMST achieves 72% and 80% as top-1 and top-3 accuracy, respectively. To address out-of-distribution challenges, we implemented an active learning improvement cycle that generates molecules in similar chemical spaces, enabling the model to adapt to chemical structures beyond its original training data. We demonstrate MMST's capabilities through comprehensive benchmarking across diverse molecular weight ranges and chemical spaces. Notably, despite training solely on simulated data, MMST demonstrates good performance with experimental spectra. This research represents a significant advancement in automated structure elucidation, offering a powerful and adaptable tool that bridges the gap between simulated and real-world data.

“I would have liked to have discovered ribozymes, the realization that RNA can be both information and catalyst is fascinating… If I could be any age, I would like to be exactly where I am now because despite the challenges, I'm finally building something that's truly mine…”

Find out more about Mirco Dindo in his Introducing… Profile.

Integrating elemental doping and morphological engineering enhances the two-electron oxygen reduction reaction (2e^– ORR) performance of carbon electrocatalysts, as reported by Cheng Tang, Jian Liu, Zhong-Shuai Wu, and co-workers in the Research Article (e202519013). N/O dual-doping optimizes the adsorption strength of intermediates, while hierarchical pores improve mass transfer and suppress H₂O₂ decomposition. These effects inhibit the competitive 4e^– pathway, enabling sustainable H₂O₂ production with remarkable performance at industrial-grade current densities.