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Abstract We demonstrate a novel approach of utilizing methanol (CH₃OH) in a dual role for (1) the methanolysis of polyethylene terephthalate (PET) to form dimethyl terephthalate (DMT) at near‐quantitative yields (~97 %) and (2) serving as an in situ H₂source for the catalytic transfer hydrogenolysis (CTH) of DMT to p‐xylene (PX, ~63 % at 240 °C and 16 h) on a reducible ZnZrO_xsupported Cu catalyst (i.e., Cu/ZnZrO_x). Pre‐ and post‐reaction surface and bulk characterization, along with density functional theory (DFT) computations, explicate the dual role of the metal‐support interface of Cu/ZnZrO_xin activating both CH₃OH and DMT and facilitating a lower free‐energy pathway for both CH₃OH dehydrogenation and DMT hydrogenolysis, compared to Cu supported on a redox‐neutral SiO₂support. Loading studies and thermodynamic calculations showed that, under reaction conditions, CH₃OH in the gas phase, rather than in the liquid phase, is critical for CTH of DMT. Interestingly, the Cu/ZnZrO_xcatalyst was also effective for the methanolysis and hydrogenolysis of C−C bonds (compared to C−O bonds for PET) of waste polycarbonate (PC), largely forming xylenol (~38 %) and methyl isopropyl anisole (~42 %) demonstrating the versatility of this approach toward valorizing a wide range of condensation polymers. 

Abstract Identifying sustainable chemical processes often depends on the choice of enabling materials that directly influence the overall performance. Matching property targets while incorporating adequate process knowledge is essential for optimal material selection. Multi‐scale decisions need to be taken simultaneously to determine the optimal process configurations, operating conditions, and material structures. Integrating molecular to process scale decisions within an equation‐oriented optimization framework leads to large‐scale mixed‐integer nonlinear programs (MINLP). Over the years, several solution approaches have been suggested to tackle this issue. Here, the current state‐of‐the‐art in the field of computer‐aided molecular and process design (CAMPD) is discussed and key challenges and open questions are highlighted that may stimulate future research.