计时安培法
介电谱
化学
电化学
无机化学
法拉第效率
氨
催化作用
硝酸盐
分析化学(期刊)
双层电容
电极
循环伏安法
物理化学
色谱法
有机化学
生物化学
作者
Luisa Barrera,Rachel Silcox,Katherine Giammalvo,Erika Brower,Emily Isip,Rohini Bala Chandran
出处
期刊:ACS Catalysis
[American Chemical Society]
日期:2023-03-13
卷期号:13 (7): 4178-4192
被引量:38
标识
DOI:10.1021/acscatal.2c05136
摘要
Wastewater nitrates (NO3–) represent an untapped source for nutrient recovery. This study explores the effects of NO3– concentration ranging from 0.1 to 1 M and pH conditions of 8, 10, and 14 on the electrochemical reduction to ammonia (NH3) with polycrystalline Cu electrodes. Cyclic voltammograms prove pH- and concentration-dependent reaction kinetics. Chronoamperometry tests probed the reaction selectivity to NH3 production for a fixed potential across different pH conditions. The maximum NH3 Faradaic efficiency achieved was 46% ± 11% for 1 M NaNO3 at pH 14 at −0.55 V vs the reversible hydrogen electrode (RHE), while the minimum was 25% ± 6% for 1 M NaNO3 at pH 8. Distinctly, at pH 8 and 10, 0.1 M NaNO3 results in higher NH3 Faradaic efficiencies compared to the 1 M solution. Product quantification reveals that as the pH decreases, more charge is utilized for the formation of NO2– as compared to NH3 as a product. Large trial-to-trial uncertainties motivated the application of in situ electrochemical impedance spectroscopy to provide insights into the causal factors. Fitted parameters from impedance measurements correlate with measured contributions of net charge utilized for NH3 and NO2– production. Trial-to-trial variations map with changes in both the charge-transfer resistance and the effective double-layer capacitance. Changes in surface roughness and consequently the electrochemically active surface area are more dominant for 0.1 M NaNO3 solutions, while other variations play a significant role for 1 M NaNO3 tests. Overall, these results indicate that catalytic performance of NO3– reduction on Cu is highly sensitive to pH, concentration, secondary ions, and surface composition.
科研通智能强力驱动
Strongly Powered by AbleSci AI