Constructing Unsaturated Ru Atom Arrays Confined in Mn Oxides to Boost Neutral Water Reduction.

Recently, Ru single atoms supported on carbon nanomaterials have demonstrated ultrahigh activity for acid hydrogen evolution reaction (HER), however their neutral HER activity remains low due to the sluggish kinetics for both the water dissociation step to generate H* intermediates and subsequent H* recombination in neutral electrolytes. Here, we synthesize ordered low-coordinated Ru atom arrays confined in Mn oxides (i.e., Li4Mn5O12) for concurrently boosting the water dissociation and H* recombination, thus achieving a 6-fold HER activity enhancement than commercial Pt/C in neutral media. Control experiments indicate that low-coordinated Ru atoms with strong affinity to oxygen atoms of water molecules facilitate the water dissociation to rapidly generate H*. More importantly, both electrochemical and theoretic results uncover that the array-like structure allows the activation of two water molecules on two adjacent Ru atoms for enabling direct H*-H* recombination via the Tafel step, while isolated Ru atoms can only activate water one by one for recombining H* via the sluggish Heyrovsky step. Clearly, this work paves new avenues to boosting the electrocatalytic activity by constructing ordered metal atoms assembles with controllable coordination environments.

Boosting Electrochemical Urea Synthesis via Constructing Ordered Pd-Zn Active Pair.

Electrochemical co-reduction of nitrate (NO3-) and carbon dioxide (CO2) has been widely regarded as a promising route to produce urea under ambient conditions, however the yield rate of urea has remained limited. Here, we report an atomically ordered intermetallic pallium-zinc (PdZn) electrocatalyst comprising a high density of PdZn pairs for boosting urea electrosynthesis. It is found that Pd and Zn are responsible for the adsorption and activation of NO3- and CO2, respectively, and thus the co-adsorption and co-activation NO3- and CO2 are achieved in ordered PdZn pairs. More importantly, the ordered and well-defined PdZn pairs provide a dual-site geometric structure conducive to the key C-N coupling with a low kinetical barrier, as demonstrated on both operando measurements and theoretical calculations. Consequently, the PdZn electrocatalyst displays excellent performance for the co-reduction to generate urea with a maximum urea Faradaic efficiency of 62.78% and a urea yield rate of 1274.42 µg mg-1 h-1, and the latter is 1.5-fold larger than disordered pairs in PdZn alloys. This work paves new pathways to boost urea electrosynthesis via constructing ordered dual-metal pairs.

Ultrathin High-Entropy Fe-Based Spinel Oxide Nanosheets with Metalloid Band Structures for Efficient Nitrate Reduction toward Ammonia.

Spinel oxides with tunable chemical compositions have emerged as versatile electrocatalysts, however their performance is greatly limited by small surface area and low electron conductivity. Here, ultrathin high-entropy Fe-based spinel oxides nanosheets are rationally designed (i.e., (Co0.2Ni0.2Zn0.2Mg0.2Cu0.2)Fe2O4; denotes A5Fe2O4) in thickness of ≈4.3 nm with large surface area and highly exposed active sites via a modified sol-gel method. Theoretic and experimental results confirm that the bandgap of A5Fe2O4 nanosheets is significantly smaller than that of ordinary Fe-based spinel oxides, realizing the transformation of binary spinel oxide from semiconductors to metalloids. As a result, such A5Fe2O4 nanosheets manifest excellent performance for the nitrate reduction reaction (NO3 -RR) to ammonia (NH3), with a NH3 yield rate of ≈2.1 mmol h-1 cm-2 at -0.5 V versus Reversible hydrogen electrode, outperforming other spinel-based electrocatalysts. Systematic mechanism investigations reveal that the NO3 -RR is mainly occurred on Fe sites, and introducing high-entropy compositions in tetrahedral sites regulates the adsorption strength of N and O-related intermediates on Fe for boosting the NO3 -RR. The above findings offer a high-entropy platform to regulate the bandgap and enhance the electrocatalytic performance of spinel oxides.

Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning.

Humans are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network model. This phenomenon, termed catastrophic forgetting, is one of the major roadblocks that prevent deep neural networks from achieving human-level artificial intelligence. Several research efforts (e.g., lifelong or continual learning algorithms) have proposed to tackle this problem. However, they either suffer from an accumulating drop in performance as the task sequence grows longer, or require storing an excessive number of model parameters for historical memory, or cannot obtain competitive performance on the new tasks. In this letter, we focus on the incremental multitask image classification scenario. Inspired by the learning process of students, who usually decompose complex tasks into easier goals, we propose an adversarial feature alignment method to avoid catastrophic forgetting. In our design, both the low-level visual features and high-level semantic features serve as soft targets and guide the training process in multiple stages, which provide sufficient supervised information of the old tasks and help to reduce forgetting. Due to the knowledge distillation and regularization phenomena, the proposed method gains even better performance than fine-tuning on the new tasks, which makes it stand out from other methods. Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracy on new tasks and performance preservation on old tasks.