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Carbon nitride (C3N4) has gained considerable attention and has been regarded as an ideal candidate for photocatalytic hydrogen evolution. However, its photocatalytic efficiency is still unsatisfactory due to the rapid recombination rate of photo-generated carriers and restricted surface area with few active sites. Herein, we successfully synthesized a single-atom Pt cocatalyst-loaded photocatalyst by utilizing the anchoring effect of carbon dots (CDs) on C3N4. The introduction of CDs onto the porous C3N4 matrix can greatly enhance the specific surface area of C3N4 to provide more surface-active sites, increase light absorption capabilities, as well as improve the charge separation efficiency. Notably, the functional groups of CDs can efficiently anchor the single-atom Pt, thus improving the atomic utilization efficiency of Pt cocatalysts. A strong interaction is formed via the connection of Pt-N bonds, which enhances the efficiency of photogenerated electron separation. This unique structure remarkably improves its H2 evolution performance under visible light irradiation with a rate of 15.09 mmol h−1 g−1. This work provides a new approach to constructing efficient photocatalysts by using CDs for sustainable hydrogen generation, offering a practical approach to utilizing solar energy for clean fuel production.

Cross-modal retrieval plays a key role in the Natural Language Processing area, which aims to retrieve one modality to another efficiently. Despite the notable achievements of existing cross-modal retrieval methodologies, the complexity of the embedding space increases with more complex models, leading to less interpretable and potentially overfitting representations. Most existing methods realize outstanding results based on datasets without any error or noise, but that is extremely ideal and leads to trained models lacking robustness. To solve these problems, in this paper, we propose a novel approach, Soft Contrastive Cross-Modal Retrieval (SCCMR), which integrates the deep cross-modal model with soft contrastive learning and smooth label cross-entropy learning to boost common subspace embedding and improve the generalizability and robustness of the model. To confirm the performance and effectiveness of SCCMR, we conduct extensive experiments comparing 12 state-of-the-art methods on three multi-modal datasets by using image–text retrieval as a showcase. The experimental results show that our proposed method outperforms the baselines.