麻豆淫院

A research team led by Distinguished Professor Hao Li at WPI-AIMR, Tohoku University, has reported new findings on copper/cobalt-based catalysts that improve the efficiency of electrochemical nitrate reduction. The , published in Advanced Functional Materials, addresses a key rate-limiting step in the conversion of nitrate (NO鈧冣伝) to ammonia (NH鈧�), offering a refined approach to green ammonia production and nitrate wastewater treatment.

Electrochemical nitrate reduction (NO鈧冣伝RR) is emerging as a viable strategy for producing ammonia under ambient conditions while also mitigating nitrate pollution. However, the multi-step process is often hindered by the slow conversion of nitrate to nitrite (NO鈧傗伝), which can significantly reduce overall efficiency.

In this work, the researchers synthesized spherical and nanoflower-like CuO/CuCo鈧侽鈧� catalysts using an emulsion hydrothermal method. The design promotes small-particle stacking and leverages the structural benefits of both CuO and Co鈧僌鈧�. The catalysts were shown to facilitate the sequential reaction steps, effectively connecting NO鈧冣伝鈫扤翱鈧傗伝 and NO鈧傗伝鈫扤H鈧� in a unified system.

One of the study's central findings is the formation of monomeric copper during electrolysis. This newly formed Cu interacts with CuCo鈧侽鈧� to promote the rate-limiting NO鈧冣伝鈫扤翱鈧傗伝 step. As a result, the same level of ammonia production was observed in both nitrate and nitrite reduction reactions.

Under neutral conditions at 鈭�0.70 V (vs. RHE), the Cu/CuCo鈧侽鈧� catalyst achieved a peak ammonia yield of 24.58 mg h鈦宦� mgcat鈦宦� in nitrate reduction, alongside a Faraday efficiency of 100%. When NO鈧傗伝 was used as the substrate, the system achieved a nearly identical yield of 24.34 mg h鈦宦� mgcat鈦宦�.

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"The ability of Cu and CuCo鈧侽鈧� to work in tandem helps us better understand how to design more effective catalysts," said Professor Hao Li. "Our findings provide not only experimental results but also mechanistic insights that may guide future catalyst development."

The team has made the experimental and computational data available through the , a database developed by the Hao Li laboratory. These results contribute to ongoing efforts to optimize catalysts for sustainable ammonia production.

Future work will focus on extending the catalyst's performance to industrial settings, including long-duration tests and reactor-scale implementation, while deepening the mechanistic understanding through operando spectroscopy and modeling.