not-yet-known not-yet-known not-yet-known unknown Selective oxidation of long-chain fatty alcohols into acids is an important value-added reaction. However, exploring the basic catalytic steps over Pt-based catalysts throughout the entire oxidation process is still ambiguous. In this work, we systematically investigated the synergistic mechanisms of adsorption, reaction, and diffusion over Pt/MCM-41 for normal/isomeric alcohols oxidation into acids via molecular dynamics, in-situ characterization, and experiments. Specifically, diffusion coefficients decrease with the increase of the molecular weight of normal molecules due to the increased van der Waals forces, while isomeric alcohols exhibit more complex patterns originated from the steric hindrance between Pt particles and mesopores. To quantitatively describe this pattern, a cluster size descriptor of d Pt 0 . 75 × d Pore 0 . 25 was defined. Notably, 2-ethylhexanol exhibits the best self-diffusion coefficients at the descriptor value of 3.14. Correspondingly, the oxidation of 2-ethylhexanol to 2-ethylhexanoic acid displays highest reaction conversion (68.67%) and selectivity (65.59%).

Selective oxidation at low temperatures without alkali of biomassis a promising and sustainable avenue to manufacture glycolic acid (GA), a biodegradable functional material to protect the environment. However, producing glycolic acid with high selectivity and yield using the traditional research and development approach is time-consuming and labor-intensive. To this context, a hybrid deep learning framework driven by data and reaction mechanisms for predicting sustainable glycolic acid production was proposed, considering the lack of related reaction mechanisms in the machine learning algorithms. Results showed that the fully connected residual network exhibited superior performance (average R2=0.98) for the multi-task prediction of conversion rate, GA, and by-product yields, therefore employed for the following super parameters optimization by the genetic algorithm. The L further identifies that using the optimized operating parameters, the fossil energy demand and greenhouse emissions have decreased by 2.96% and 3.00%, respectively.