Guangxi Normal University

Despite excellent charge-discharge performance in carbon-based supercapacitors, their mechanisms across pH solutions remain unclear due to difficulties decoupling capacitive effects from porous structures and intricate micro-nano molecular/ionic channels. To investigate these mechanisms, we electrochemically synthesize structure-defined, nanohole-free N-doped graphene (ENG) as a model system. Attenuated total reflection in situ infrared spectroscopy (ATR-FTIR) reveals splendid electrochemical activity for pyridinic N and carboxyl functional groups in acidic medium. In contrast, hydroxyl groups dominate the charge-discharge behavior of ENG in alkaline medium. This behavior differs from the neutral medium scenario, where only CO bonds produce detectable ATR-FTIR peaks. Theoretical calculations identified strong adsorption affinity of pyridinic N and carboxyl functional groups for H⁺ over OH^-, which directly enhances the electrochemical activity of ENG in acidic medium. This work pioneers a targeted synthesis strategy for ENG, establishing it as a robust model to decode charge-discharge mechanisms of carbon-based materials across the full pH range.

Near-infrared II (NIR-II) photoacoustic (PA) imaging offers enhanced performance through reduced photon scattering, minimized background interference, and improved resolution at greater depths, thereby driving urgent demand for NIR-II PA nanoprobes in translational biomedicine. In this study, we developed a smart stannous sulfide@cuprous oxide (SnS@Cu₂O) nanoprobe for colorectal cancer PA imaging within NIR-II window. Under the existence of HS^-, the SnS@Cu₂O nanoprobe exhibits a broad absorption peak within the 1000-1300 nm range, enabling concentration-dependent NIR-II PA signal amplification in colorectal cancer tissues with overexpression of endogenous H₂S. The in vitro PA intensity was linearly correlated to HS^- concentration in the range of 0-1.2 mM. Intratumoral injection of SnS@Cu₂O nanoprobe (10 mg/kg) induced time-dependent PA signal amplification at the tumor site, exhibited rapid intensification, peaking at 4-h post-administration. Additionally, the SnS@Cu₂O can catalyze the Cu⁺ with H₂O₂ in the tumor tissue yielding ·OH via Fenton-like reactions for photothermal-enhanced chemodynamic therapy, enabling NIR-II PA imaging-guided therapy against deep-tissue colorectal tumor. This paper reports on the synthesis and application of a smart SnS@Cu₂O nanoprobe that was activated by colorectal tumor microenvironment.

Protein-ligand binding affinity (PLBA) is a crucial metric in drug screening for identifying potential candidate compounds. In recent years, deep learning-based methods have used representation learning to model interactions within protein-ligand complexes, demonstrating great promise in affinity prediction tasks. Existing studies have considered both intramolecular (covalent) and intermolecular (non-covalent) interactions to some extent. However, these interactions are often treated as independent features, lacking explicit hierarchical dependency modeling, which may lead to insufficient representation of interaction information and ultimately limit the accuracy of affinity predictions. To address this issue, we propose a novel approach-Dual-channel Hierarchical Interactive Learning (DHIL)-to achieve a more comprehensive modeling of protein-ligand interactions. DHIL employs a dual-channel encoding structure to simultaneously learn intramolecular and intermolecular interactions, ensuring the completeness of interaction features. Additionally, we design a hierarchical interactive learning paradigm to facilitate information exchange between these two interaction types at multiple levels, promoting their collaborative modeling. This mechanism mimics the local-to-global working principles of biological systems, enabling a more detailed and holistic representation of protein-ligand interactions. We conduct extensive and comprehensive experiments on a diverse set of benchmark datasets, rigorously evaluating the effectiveness of DHIL. The results demonstrate that DHIL significantly improves PLBA prediction accuracy, outperforming existing methods and further validating its potential in drug discovery and screening tasks. Nevertheless, the proposed framework introduces notable computational overhead due to multi-scale graph construction and cross-level message passing. It also exhibits sensitivity to the quality of input 3D binding conformations, which may affect its robustness in practical applications. These limitations suggest future directions for improving model efficiency and generalizability. To facilitate reproducibility and further research, the complete source code of DHIL has been released at: https://github.com/WZY-0814/DHIL.