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Edge-site anchored single-atom Ti on α-MnO2 for high performance enhancement in ultra-low temperature NH3-SCR
Mengyao Bian1, Xin Yang1, Xinyu Han1
1School of Rare Earths, University of Science and Technology of China, Hefei 230026, China; Ganjiang Innovation Academy, Chinese Academy of Sciences, No.1, Science Academy Road, Ganzhou 341000, China.
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Mn-based catalysts hold significant promise for industrial applications in low-temperature SCR reactions. In contrast to conventional approaches, where Mn is typically used as an active component deposited on other supports, this study introduces an innovative inverse-loaded catalyst, Ti1/α-MnO2, where inert single-atom Ti is precisely anchored at the step sites of α-MnO2 nanorods. This site-specific placement dramatically enhances catalytic performance, achieving 80 % NOx conversion at an exceptionally low 50 °C, a 20 % improvement over pristine α-MnO2. Comprehensive structural characterization, including HAADF-STEM and EXAFS, confirms the atomic dispersion of Ti and its coordination environment, revealing a Ti-O dominated single-atom structure at the MnO2 (200) edge. H2-TPR experiments demonstrate that trace Ti loading significantly modulated the redox ability of α-MnO2 in a quantifiable manner. Furthermore, a quantitative correlation between the H2 consumption per unit area (x) and specific activity (y) is established based on the H2-TPR results (y = 5.3832 ×10-4x-0.0713). DFT calculations elucidate that edge-localized Ti single atoms drastically reduced the oxygen vacancy formation energy in their vicinity, thereby activating surface lattice oxygen and driving the enhanced ultra-low temperature activity. Notably, the Ti1-Mn1 active site exhibited a TOF exceeding an order of magnitude over Mn1 site, directly attributed to this site-specific activation. This work not only presents a novel strategy for designing high-performance low-temperature NH3-SCR catalysts through precise single-atom placement but also provides a crucial quantitative framework linking redox ability and catalytic activity, which can offer fundamental data associations essential for future artificial intelligence-driven catalyst screening and accelerated materials discovery in catalysis.

