氮还原反应中氨定量假阳性结果的来源与消除方法

Excluding false positives:A perspective toward credible ammonia quantification in nitrogen reduction reaction

氨(NH_(3))广泛应用于化肥等工业化学品的生产中,年消耗量巨大.同时,氨具有高氢含量和高能量密度,可作为清洁能源载体和燃料,具有广阔的应用前景.因此,合成氨工业在国民经济和社会发展中起着重要作用.目前,合成氨的主要采用传统的Haber-Bosch工艺,但其严苛的操作条件导致了大量能源消耗和二氧化碳排放,进一步加剧了全球变暖.在全球能源危机和环境问题的背景下,开发可再生能源驱动的绿色高效氨合成技术受到广泛关注.其中,以光催化和电催化为动力的氮还原反应(NRR)被认为是最有前途的方法之一.然而,由于N_(2)吸附动力学缓慢,N≡N键分裂困难且析氢反应严重,目前电催化和光催化氮还原的产率和法拉第效率都较低.近年来,得益于各种催化剂和电解液的发展,NRR产率和法拉第效率不断提升,但也逐渐暴露出一些严重的问题——测试结果呈现高波动性和低重复性,甚至假阳性,这使得人们对NRR的发展前景产生了怀疑.由于NRR反应的产量极低(通常为纳/微摩尔水平),所以反应过程中的微量污染都可能严重影响NH_(3)的定量结果,从而导致对NRR反应体系性能的误判.因此,如何保证得到的产物NH_(3)完全来自于氮气的还原是一个难题.本文基于NRR反应整个检测流程中的污染源和不确定性,包括来自反应物和实验装置中的杂质,各种定量方法的固有缺陷,甚至一些细微的定量仪器误差等,将影响氨定量的潜在因素细分为催化剂污染、实验过程中引入的污染以及量化方法的固有缺陷等,详细讨论了这些潜在因素如何干扰氨的最终定量结果,并总结了已报道的相应消除干扰策略.此外,结合本课题组在NRR领域的研究经验,提出了一套严格的NRR检测办法,该办法针对NRR检测的三个阶段:检测前、检测中和定量过程,提出了相应建议.最后,对该领域的未来发展进行了展望,提出一些关键问题和发展方向,希望能为促进NRR研究发展提供一些借鉴.

As an essential raw material for fertilizer production and promising green fuels,ammonia is significant to the national economy and social development.Recently,nitrogen reduction reaction(NRR)towards ammonia under mild conditions has received intensive attention due to environmental friendliness and low energy consumption,compared to the rigorous and contaminative Haber-Bosch process.However,as current research deepens,some fatal challenges emerged in this field-not only high volatility and low repeatability but false-positive results,casting serious doubts about its prospects.In this review,we summarized and discussed some potential factors that possibly induce false-positive results for accurate ammonia quantification,including the aspects of catalyst materials,experimental process,and quantification methods.And corresponding methods to eliminate these effects are also summarized.Furthermore,a promising protocol and several control principles are proposed to eliminate potential errors during ammonia quantification.This review establishes a paradigm base in NRR research field toward more accurate ammonia quantification.