机器学习促进的熵工程合成CuCo纳米固溶体合金用于近100%硝酸盐转化为氨的选择性
Machine Learning Boosted Entropy-Engineered Synthesis of CuCo Nanometric Solid Solution Alloys for Near-100% Nitrate-to-Ammonia Selectivity
AI总结：
本研究利用机器学习结合熵工程方法，成功合成CuCo纳米固溶体合金，实现近100%硝酸盐转化为氨的选择性，并在120小时测试中保持高稳定性，为高效催化剂设计提供新思路。
摘要：
纳米固溶体合金广泛应用于催化、储能、医疗应用和传感器技术等多个领域。然而，由于原子尺寸、熔点和化学亲和力的差异，随着金属元素之间差异的增大，这些合金的合成变得越来越具有挑战性。本研究采用数据驱动方法，结合样本平衡增强技术和多层感知机（MLP）算法，以提高模型处理不平衡数据的能力，显著提升了实验参数优化的效率。在此增强的数据处理框架基础上，我们开发了一种熵工程合成方法，专门用于制备稳定的纳米铜和钴（CuCo）固溶体合金。在-0.425 V（相对于RHE）条件下，CuCo合金表现出近100%的法拉第效率（FE）和232.17 mg h-1 mg-1的高氨产率。在模拟工业环境中的稳定性测试表明，该催化剂在120小时的测试期间保持了超过80%的FE和超过170 mg h-1 mg-1的氨产率，优于大多数已报道的催化剂。为了深入探究Cu和Co之间的协同相互作用机制，利用原位拉曼光谱进行实时监测，密度泛函理论（DFT）计算进一步证实了我们的发现。这些结果不仅突出了CuCo合金的优异催化性能，也反映了两种金属之间有效的电子和能量相互作用。
英文摘要：
Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization. Building on this enhanced data processing framework, we developed an entropy-engineered synthesis approach specifically designed to produce stable, nanometric copper and cobalt (CuCo) solid solution alloys. Under conditions of -0.425 V (vs. RHE), the CuCo alloy exhibited nearly 100% Faraday efficiency (FE) and a high ammonia production rate of 232.17 mg h-1 mg-1. Stability tests in a simulated industrial environment showed that the catalyst maintained over 80% FE and an ammonia production rate exceeding 170 mg h-1 mg-1 over a testing period of 120 hours, outperforming most reported catalysts. To delve deeper into the synergistic interaction mechanisms between Cu and Co, in situ Raman spectroscopy was utilized for realtime monitoring, and density functional theory (DFT) calculations further substantiated our findings. These results not only highlight the exceptional catalytic performance of the CuCo alloy but also reflect the effective electronic and energy interactions between the two metals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
