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The electrochemical nitrate reduction reaction (NO 3 RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d -states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO 3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO 3 RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V RHE and a yield rate of 6.25 mol h −1 g −1 at −0.6 V RHE . This study provides machine-learned design rules besides the d -band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations. Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt 3 Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt 3 Ir, and Pt 3 Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d -band center metric of binding sites. Electrochemical conversion of ammonia to nitrogen has important energy and environmental applications but is hindered by lack of efficient electrocatalysts. Here the authors use quantum chemistry and machine learning to gain insights into the reaction mechanism and accelerate the design of highly active Ir-free trimetallic catalysts.

Poyang Lake is the largest freshwater lake in China and is well known for its ecological and economic importance. Understanding the contribution of groundwater to Poyang Lake is important for the lake’s protection and management. In this study, stable isotopes (δD and δ18O), 222Rn measurements, and corresponding models (222Rn and 18O mass balance models) were employed to evaluate the groundwater discharge and associated chemical inputs to Poyang Lake. The results showed that the distribution of δ18O in the lake water reflects the groundwater discharge into the lake. The groundwater discharge estimated using the 222Rn mass balance model was in reasonable agreement with the groundwater discharge derived from the 18O mass balance model. The 222Rn mass balance model showed that the groundwater discharge rate was 24.18 ± 6.85 mm/d with a groundwater discharge flux of (2.24 ± 0.63) × 107 m3/d, which accounts for 6.52–11.14% of river-water input in the Poyang Lake area. The groundwater discharge flux estimated using the 18O mass balance model was 3.17 × 107 m3/d, and the average groundwater discharge rate was 26.62 mm/d. The estimated groundwater discharge was used to estimate the associated chemical fluxes. It was found that groundwater-derived heavy metals such as iron and manganese are potential threats to the lake ecological system because of their large inputs from groundwater discharge.

Objective: Colorectal cancer (CRC) is a common cancer that cannot be detected at an early stage and is a major challenge in oncology research. Studies have shown that vitamin D3 has some anti-cancer and preventive effects on colorectal cancer, but the exact anti-cancer mechanism is not clear. We applied the relevant research methods of network pharmacology to speculate and validate the possible potential pharmacological mechanisms of vitamin D3 for the prevention of colorectal cancer, and to provide more theoretical support for the clinical anticancer effects of vitamin D3. Methods: The relevant targets for vitamin D3 and CRC were obtained from the database of drug and disease targets, respectively. The target of vitamin D3 and the target of colorectal cancer were taken to intersect to obtain common targets. Then, the PPI network was constructed. In addition, the pathways of drug-disease interactions were predicted by GO and KEGG enrichment analysis. Finally, the obtained results were verified to ensure the reliability of the experiments. Results: 51 targets of vitamin D3 for the prevention of colorectal cancer were obtained. The 10 core targets were obtained from the PPI network. The 10 core targets include: ALB, SRC, MMP9, PPARG, HSP90AA1, IGF1, EGFR, MAPK1, MAP2K1 and IGF1R. The core targets were further validated by molecular docking and animal experiments. The results suggest that vitamin D3 plays a key role in the prevention of CRC through core targets, PI3K-Akt pathway, HIF-1 pathway, and FoxO pathway. Conclusion: This study will provide more theoretical support for vitamin D3 to reduce the incidence of CRC and is important to explore more pharmacological effects of vitamin D3.

Cu-W alloys are widely used in high-voltage circuit breaker contacts due to their high resistance to arc ablation, but few studies have analyzed the microstructure of Cu-W alloys under arc ablation. This study applied a phase-field model based on the phase-field model developed by Karma and co-workers to the evolution of dendrite growth in the solidification process of Cu-W alloy under arc ablation. The process of columnar dendrite evolution during solidification was simulated, and the effect of the supercooling degree and anisotropic strength on the morphology of the dendrites during solidification was analyzed. The results show that the solid–liquid interface becomes unstable with the release of latent heat, and competitive growth between dendrites occurs with a large amount of solute discharge. In addition, when the supercooling degree is 289 K, the interface is located at a lower height of only 15 μm, and the growth rate is slow. At high anisotropy, the side branches of the dendrites are more fully developed and tertiary dendritic arms appear, leading to a decrease in the alloy’s relative density and poorer ablation resistance. In contrast, the main dendrites are more developed under high supercooling, which improves the density and ablation resistance of the material. The results in this paper may provide a novel way to study the microstructure evolution and material property changes in Cu-W alloys under the high temperature of the arc for high-voltage circuit breaker contacts.

Mitochondrial dysfunction causes inflammatory cascades in cerebral ischemia-reperfusion injury (CIRI). However, precise pharmacological interventions are hindered by the restrictive blood-brain barrier (BBB). Here, we report a neutrophil-hitchhiking, biomimetic nanoplatform (NeuM@Mdivi-1) with a \"rocket-inspired\" sequential targeting strategy for restoring mitochondrial homeostasis. By co-opting the innate chemotaxis of circulating neutrophils, NeuM@Mdivi-1 effectively bypassed the BBB to infiltrate the ischemic penumbra. Within this pathological microenvironment, upregulated matrix metalloproteinase-9 (MMP9) triggers surface transformation, exposing mitochondrial-targeting peptides and enabling the precise intracellular release of the fission inhibitor Mdivi-1. This spatiotemporal delivery approach effectively suppresses aberrant dynamin-related protein 1 (Drp1)-mediated fission, thereby silencing the Drp1/mtDNA/cGAS-STING signaling axis and mitigating neuronal ferroptosis. NeuM@Mdivi-1 preserves neuronal viability and reengineers the immune microenvironment by decoupling mitochondrial fragmentation from the inflammatory response, thereby establishing a sophisticated therapeutic paradigm for CIRI management.