Dataset of structure-activity relationships in Pd/ZrO₂-TiO₂ catalysts for furfural reductive amination: Batch vs...

Area of Science:

• Catalysis
• Green Chemistry
• Materials Science

Background:

• Furfural (FUR) is a key platform chemical from lignocellulosic biomass, serving as a precursor for valuable compounds like secondary amines.
• Direct reductive amination of FUR with aniline (ANI) offers an environmentally friendly alternative to fossil-based processes.
• Challenges include parallel reactions and the need for bifunctional catalysts with stable performance, requiring detailed experimental data for catalyst design.

Purpose of the Study:

• To present a comprehensive dataset for the reductive amination of FUR with ANI over Pd/ZrO2-TiO2 catalysts.
• To provide detailed experimental data for elucidating structure-activity relationships in bifunctional catalytic systems under reaction conditions.
• To facilitate data-driven approaches for understanding and optimizing furfural reductive amination.

Main Methods:

• Catalyst characterization using N2 physisorption, XRD, XPS, H2-TPR, NH3-TPD, IR-Pyr, TEM, and STEM-EDX.
• Catalytic activity assessment in batch reactors and *in operando* FTIR-ATR measurements.
• Integration of operando FTIR-ATR data with GC-MS and GC-FID for product identification and quantification.

Main Results:

• A dataset encompassing catalyst characterization, activity under various conditions, and *in operando* reaction profiles is presented.
• Varied Zr/Ti ratios in Pd/ZrO2-TiO2 catalysts modulated surface properties, influencing catalytic performance.
• Integrated data from multiple techniques provide a holistic view of the catalytic process.

Conclusions:

• The provided dataset facilitates the reuse, independent analysis, and data-driven optimization of furfural reductive amination.
• Understanding structure-activity relationships is crucial for designing efficient bifunctional catalysts for biomass valorization.
• This work supports the development of sustainable chemical processes using renewable feedstocks.