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Abstract
<jats:p>Abstract</jats:p> <jats:p> The hydrogenation of CO <jats:sub>2</jats:sub> to light olefins (C <jats:sub>2</jats:sub> –C <jats:sub>4</jats:sub> ) via modified Fischer-Tropsch synthesis (mFTS) is a promising route for CO <jats:sub>2</jats:sub> utilization and sustainable olefin production. Iron-based catalysts promoted with sodium and cobalt have shown promise for low-temperature operation (<300 ℃), where energy efficiency and catalyst stability can see improvements. However, the compositional space of NaCoFe catalysts remains largely unexplored beyond a single literature composition (7Na2CoFe). In this work, multi-objective Bayesian optimization (BO) is applied to systematically explore and optimize the Na and Co promoter loadings on an iron oxide catalyst for low-temperature CO <jats:sub>2</jats:sub> hydrogenation. Six baseline catalysts and seven BO suggestions were synthesized via a citric acid sol-gel method and evaluated in a fixed-bed reactor at 250 ℃ and 40 bar, with four simultaneous objectives: maximizing olefin selectivity and CO <jats:sub>2</jats:sub> conversion, and minimizing CH <jats:sub>4</jats:sub> and CO selectivity. The BO identified 3.2Na0.4CoFe as the best performing catalyst, achieving an olefin space-time yield (STY) of 5.5 mg <jats:sub>ole</jats:sub> /g <jats:sub>cat</jats:sub> /h at 250 ℃, improving upon the baseline maximum of 3.5 mg <jats:sub>ole</jats:sub> /g <jats:sub>cat</jats:sub> /h. Gaussian process models of the chemical space confirm that Na is the dominant promoter for olefin selectivity, while Co primarily enhances CO <jats:sub>2</jats:sub> conversion. Although performance remains below the literature benchmark, the discrepancy is likely caused by differences in iron oxide particle size and surface area. These results demonstrate the utility of multi-objective BO for accelerated catalyst discovery, while highlighting the importance of controlling catalyst material properties for reproducibility across studies. </jats:p>