Prof. Dr. Nadezda Tarakina

Engineering interfaces between organic semiconductors is an effective way to tailor organic electronic device performance, as charge transport and light interaction efficiency are strongly influenced by electronic coupling at molecular interfaces. Scanning transmission electron microscopy is routinely used to analyze interfaces at the atomic scale; however, its use for organic materials is limited due to the electron beam sensitivity of organic molecules, buried interfaces, and the semicrystalline nature of organics. In this work, we developed a workflow to correlate charge behavior at organic interfaces with their chemistry and structure, even when interface components are chemically and structurally similar and mixed at the nanoscale. We used this workflow to reveal the nanoscale mechanism behind enhanced charge transfer at the heterojunction between two-dimensional carbon nitride catalysts (poly-heptazine imide (PHI) and poly-triazine imide (PTI)) during the oxygen reduction reaction. We found that PHI crystallites grow on PTI layers formed at the gas–liquid interface in the salt melt, following the [001]PTI/[001]K-PHI orientation. This crystallographic alignment promotes the charge transfer from PTI to PHI and creates an electron-rich interface. Electron energy loss spectroscopy showed quaternary N atoms in the heterojunction, which aid O2 adsorption and 2e– reduction to H2O2, as well as a higher proportion of terminal and bridging N atoms, promoting charge separation during the reaction.

Nanodiamonds hosting colour centres are promising building blocks for quantum technologies, enabling advances in quantum computation1,2, nanoscale NMR spectroscopy3,4,5,6, single-spin magnetometry7,8, wide-field quantum imaging9 and single-photon sources10,11. However, the controlled bottom-up synthesis of ultrasmall and structurally uniform nanodiamonds has remained a challenge, with existing methods producing heterogeneous materials that vary in size, morphology, impurity content and defect quality. Here we show that well-defined, hydrogen-terminated molecular nanographenes serve as chemically confined precursors for high-pressure, high-temperature synthesis of ultrasmall (3–4 nm), monodisperse and highly crystalline molecular nanodiamonds with only a single sp2 surface reconstruction and produced on a milligram scale. The same bottom-up platform also enables a two-component strategy for incorporating silicon- and germanium-based colour centres during synthesis, yielding SiV− and GeV− emitters without ion implantation, irradiation or post-treatment. Because the nanographene precursor defines both the confined carbon framework and the hydrogen content, this approach provides intrinsic, precursor-level control over nanodiamond size and composition, particularly in the low-nanometre regime relevant for biological and quantum sensing. Molecular nanographenes, ultralarge polycyclic aromatic hydrocarbons, therefore, establish a scalable and modular route to high-quality molecular and fluorescent nanodiamonds and offer a general design principle for tailored quantum materials and nanoscale devices.

Structurally heterogeneous materials present major challenges for characterization due to their complex nanoscale order. Sodium poly(heptazine imide) (NaPHI), a layered carbon nitride photocatalyst, exemplifies this complexity, with its precise structure remaining unresolved. Here, we uncover new structural insights into NaPHI using energy-filtered four-dimensional scanning transmission electron microscopy combined with machine-learning-based diffraction image segmentation, supported by transmission electron microscopy, atomic force microscopy, X-ray diffraction, and Raman spectroscopy. At the mesoscale, NaPHI flakes display bent morphologies, while nanodiffraction patterns reveal features characteristic of stacking disorder. Based on these insights, we modeled a NaPHI-layered structure incorporating out-of-plane undulations (waves) with amplitudes of ∼0.5 Å and wavelengths of 2–3 nm. This model reproduces the observed line features in nanodiffraction patterns and agrees with powder X-ray diffraction data, thereby bridging local and bulk structural information. The introduced approach uses data-driven machine learning to identify statistically significant features, offering a robust framework for structural analysis of semi-crystalline materials.