Publications by authors named "Kah Wei Ting"

Technological enablers that use CO as a feedstock to create value-added chemicals, including ethanol, have gained widespread appeal. They offer a potential solution to climate change and promote the development of a circular economy. However, the conversion of CO to ethanol poses significant challenges, not only because CO is a thermodynamically stable and chemically inert molecule but also because of the complexity of the reaction routes and uncontrollability of C-C coupling.

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Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts.

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Low temperature and high pressure are thermodynamically more favorable conditions to achieve high conversion and high methanol selectivity in CO hydrogenation. However, low-temperature activity is generally very poor due to the sluggish kinetics, and thus, designing highly selective catalysts active below 200 °C is a great challenge in CO-to-methanol conversion. Recently, Re/TiO has been reported as a promising catalyst.

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The activity and stability of supported metal catalysts, which exhibit high efficiency and activity, are significantly influenced by the interactions between the metal and the support, that is, metal-support interactions (MSIs). Here, we report an investigation of the MSIs between supported rhenium (Re) and oxide supports such as TiO, SiO, AlO, MgO, VO, and ZrO using experimental and computational approaches. The reducibility of the Re species was found to strongly depend on the oxide support.

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The use of triglycerides as an important class of biomass is an effective strategy to realize a more sustainable society. Herein, three heterogeneous catalytic methods are reported for the selective one-pot transformation of triglycerides into value-added chemicals: i) the reductive amination of triglycerides into fatty amines with aqueous NH under H promoted by ZrO -supported Pt clusters; ii) the amidation of triglycerides under gaseous NH catalyzed by high-silica H-beta (Hβ) zeolite at 180 °C; iii) the Hβ-promoted synthesis of nitriles from triglycerides and gaseous NH at 220 °C. These methods are widely applicable to the transformation of various triglycerides (C -C skeletons) into the corresponding amines, amides, and nitriles.

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