Modulating Dynamic Atomic Hydrogen Generation and Utilization for Selective Electrocatalytic Ammonia Recovery from Low-Concentration Nitrate-Containing Wastewater
APPLIED CATALYSIS B-ENVIRONMENT AND ENERGY(2025)
Abstract
Herein, a copper-doped nickel phosphide (CuxNi2-xP) electrode with a tailored metal d-band center was designed to boost ammonia (NH3) selectivity during electrocatalytic nitrate (NO3-) reduction (NO3RR) by modulating atomic hydrogen (H*) behavior. The Cu doping accelerated both the Volmer step of water splitting to form H* and the H*-mediated hydrogenation steps for NH3 production, addressing the mismatch between H* supply and utilization. Consequently, the CuxNi2-xP electrode achieved 100 % NO3- conversion efficiency and 99.2 % NH3 selectivity at a low NO3- concentration of 50 mg L- 1, outperforming Ni2P and Cu counterparts. The crucial role of H* in the performance enhancement was elucidated via in-situ characterizations and density functional theory (DFT) calculations. Furthermore, an integrated device combining NO3RR, organic pollutant degradation and NH3 recovery was constructed, demonstrating its scalability for practical wastewater treatment. This study paves the way for collaboratively addressing environmental and energy challenges through improving NH3 recovery from nitrate-laden wastewater.
MoreTranslated text
Key words
Electrocatalytic nitrate reduction,Atomic hydrogen,Ammonia synthesis,d -Band center modulation,Cu x Ni 2-x P
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined