We report here the upcycling of PET (polyethylene terephthalate) waste via semihydrogenation to make ethyl 4-(hydroxymethyl)benzoate. The reaction is catalyzed by a ruthenium pincer catalyst at 80 °C in bioderived solvents – a combination of 2-methyl THF and ethanol. A detailed mechanistic investigation through organometallic and kinetic studies, as well as chemical exchange saturation transfer (CEST) NMR spectroscopy, provides insights into the nature of active species and factors that promote and inhibit the catalytic hydrogenation of PET. Using this mechanistic knowledge, a record high turnover number of >30 000 was achieved for the hydrogenative depolymerization of end-of-life PET waste (e.g., bottles and textiles). The semihydrogenation product, ethyl 4-(hydroxymethyl)benzoate, was utilized to make precursors of various known pharmaceutical drugs, an agrochemical, as well as a new and recyclable polyester. A cradle-to-gate life cycle assessment demonstrated that using PET waste as a feedstock for EHMB production significantly reduces the environmental footprint compared to the conventional route from p-toluic acid.

Simulation and systematic analysis of the surfaces of amorphous materials is a challenge for computational chemistry. For example, silica has found widespread industrial use as an adsorbent and catalyst support but available models for use with periodic DFT are limited in variety and representativeness of realistic materials. Herein we present a generic approach for the systematic construction of ensembles of amorphous materials surface models with varied roughness and termination characteristics. The power of the approach is shown with silica as the representative example. By combining MD simulations and Fourier-series-based randomization, bulk amorphous silica was modeled and cleaved to produce surfaces with systematically varied roughness and surface saturation. An automated saturation procedure resulted in surface models with silanol densities typical of high-temperature activation protocols in the range 0.35–2.00 OH nm−2, in excellent agreement with the experimental data on surface chemistry of dehydroxylated silica materials.

Sulfated zirconium oxide (SZO) catalyzes the hydrogenolysis of isotactic polypropylene (iPP, Mw=13.3 kDa, Đ=2.4, <mmmm>=94 %) or high-density polyethylene (HDPE, Mn=2.5 kDa, Đ=3.6) to branched alkane products. We propose that this reactivity is driven by the pyrosulfate sites SZO, which open under mild conditions to transiently form adsorbed SO3 and sulfate groups. This adsorbed SO3 is a very strong Lewis acid that binds 15N-pyridine or triethylphosphineoxide (TEPO) (ΔEads>−39 kcal mol−1), reacts with Ph3CH to form Ph3C+, and mediates H/D exchange in dihydroanthracene-d4. DFT studies show that pyrosulfate sites open with a modest 26.1 kcal mol−1 barrier to form the adsorbed SO3 and sulfate in the presence of a tetramer of propylene. Hydride abstraction from the tertiary C−H in this model is exothermic and subsequent β-scission forms cleaved products. Analysis of the energetics provided here brackets the hydride ion affinity (HIA) of the adsorbed SO3 between 226.2 to 237.9 kcal mol−1, among largest values reported for a formally neutral Lewis acid. This study explains how SZO, a classic heterogeneous catalyst, can form carbocations by a redox neutral hydride abstraction reaction by very strong Lewis sites.

Understanding how surface species evolve under reaction conditions is essential for improving catalyst design for efficient CO2 hydrogenation. This work combines systematic DFT calculations with grand canonical sampling to investigate the stability and reactivity of Ga–H species on β-Ga2O3 across a range of reaction conditions. Initial DFT studies reveal that when Ga–H species are present, they facilitate formate formation via a low-barrier pathway, largely independent of the surface termination or hydrogen site. However, grand canonical sampling shows that under a broad range of reaction conditions─especially at high oxygen chemical potentials associated with high water content─Ga–H species are thermodynamically inaccessible. Furthermore, adsorbed water molecules can block reactive sites, inhibiting CO2 activation even when hydrides are present. These findings suggest that the lack of accessible hydride species, rather than their intrinsic reactivity, could contribute to reduced catalytic performance of β-Ga2O3 under more oxidizing, high-conversion conditions.

Plastic waste is a major environmental issue; converting it directly into valuable chemicals by using catalysts is a promising alternative to plastic recycling. Here, we report the selective catalytic cracking of polypropylene (PP), a typical commodity plastic, to high-value light olefins (C2–C5), below pyrolytic temperature (290 °C) and without external hydrogen supply, by using zeolite catalysts. Among the H+-form zeolites with different structures, HMFI showed the highest yields of light hydrocarbons (C2–C5), of which light olefins (C2–C5) were the major products. The HMFI-catalyzed PP conversion was applicable to the upcycling of a model PP waste, resulting in a 61.9% light hydrocarbon yield. The results of catalytic and in situ IR experiments using model HMFI catalysts with a small amount of external Brønsted acid sites suggested that the Brønsted acid sites on the external surface of HMFI are indispensable for the PP conversion and are posited to be the active sites for the cracking of PP into short-chain (oligomeric) hydrocarbon species as intermediate products. Density functional theory analyses were conducted to determine plausible reaction pathways by adopting 2,4-dimethylheptene as the shortest unit of the oligomeric species. The obtained results show that the β-scission of 2,4-dimethylheptene by Brønsted acid sites yields isobutene and propylene (or a propyl alkoxide group) via carbocation intermediates with an activation energy below 118 kJ mol–1. Operando UV–vis and IR experiments under the reaction conditions, combined with ex situ 1H NMR and 13C NMR analyses of the spent catalyst, show that some of the olefins are further converted to light or heavy aromatics (coke deposit), probably via carbenium ion species.

This chapter provides a brief introduction to computational chemistry in the context of zeolite research, emphasizing the capabilities and limitations of modern theoretical models for investigating their reactivity and chemical properties under operando conditions. A brief overview of the computational chemistry toolbox is given, followed by a discussion of state-of-the-art applications in zeolite chemistry and catalysis. This chapter also highlights the increasing impact of data-driven techniques, such as machine-learning potentials, in advancing computational methods in zeolite studies.

Significant efforts have been dedicated to the direct syngas conversion into ethanol, however, achieving a high ethanol yield remains a formidable task. In this study, we present the direct syngas-to-ethanol conversion over Li-promoted RhOx/MgO catalyst (RhOx/Li2O/MgO). The ethanol space-time yield (EtOH STY) and selectivity reached 12.2 mmol gcat–1 h–1 and 20%, respectively, at a 35% CO conversion over the RhOx/Li2O/MgO catalyst. The RhOx/Li2O/MgO catalyst demonstrated superior performance in terms of both ethanol selectivity and STY compared to Rh/Li2O catalysts on other support materials and Rh/MgO catalysts promoted with other alkali metals. In situ/operando spectroscopic techniques, combined with other characterisations and theoretical calculations, have elucidated the interactions between Li2O and Rh on the MgO surface. These interactions promote the formation of new active sites and weaken CO adsorption on the Rh surface, thereby enhancing ethanol production. This work provides a promising strategy for improving ethanol yield in syngas conversion processes.

Many catalytic reactions suffer from product inhibition, which especially hard to control in homogeneous hydrogenation due to the scaling relation between the inhibited and active states of the catalyst. We recently reported one such pathway in Mn(I) hydrogenation and demonstrated that addition of alkoxide bases could affect the thermodynamic favorability of this reaction and selectively suppress the product inhibition. Since external reaction promotors are formally not involved in reaction thermodynamics, we set to investigate the explicit molecular interactions behind these apparently environmental effects. Herein, we reveal that the thermodynamic landscape of the inhibitory process exhibits a non-monotonic dependence on the base concentration. This study related this phenomenon to the presence of two dominant mechanisms operating at different base concentrations. Specifically, the base additives can enhance the ionic strength and lower the free energy of the inhibited state at low promotor concentration. At high base concentrations this study suggested the formation of highly labile alcohol-alkoxide clusters which stabilize the free alcohol and make its addition to the catalyst unfavourable, thereby suppressing the inhibition. While relatively weak, such noncovalent interactions between reactants and reaction environment can cause substantial perturbations to the free energy of catalytic process, ultimately deciding its fate.

Chemical recycling of polyolefins represented by polyethylene (PE) and polypropylene (PP) via catalytic cracking has emerged as a promising strategy for converting waste plastics into valuable hydrocarbons. In this study, we investigated the selective hydrocracking of PP into light alkanes (C₁–C₅) using zeolite catalysts at 280 °C under 1 MPa H₂. An HMFI zeolite with high Al content exhibited the best catalytic performance among various zeolite catalysts tested. In situ DRIFTS comparing bare HMFI and externally-silylated HMFI suggested that the external surface Brønsted acid sites serve as the active sites for the cracking of PP. Combination of in situ DRIFTS and UV–vis spectroscopy analyses identified the formation and consumption of oligomeric species as a reaction intermediate during reaction. Density functional theory (DFT) calculations suggested that a route in which the carbocation and alkoxide intermediates generated by hydrocracking of PP undergo low-energy barrier transformations into gaseous products such as C₃ and C₄ hydrocarbons. This study advances the development of sustainable polyolefin recycling technologies.

Computational exploration of chemical space is a powerful tool for designing organometallic homogeneous catalysts. While catalytic properties depend on ligand properties and spatial arrangement, the role of stereoisomerism in defining catalyst selectivity and reactivity has only been elucidated sporadically, leaving gaps in virtual screening workflows. This study investigates the necessity of exploring ligand configurations for virtual high-throughput (HT) screening of octahedral transition metal complexes. Using automated workflows, ligand configuration ensembles were generated for bisphosphine ligands with Ir(III), Ru(II), and Mn(I) metal centers. DFT calculations revealed distinct preferences for Ir(III) configurations, whereas Mn(I)- and Ru(II)-complexes displayed significant fluxionality, with multiple configurations within a 10 kJ mol−1 energy range. Linear regression analyses showed that global descriptors, such as bite angle and HOMO–LUMO gap, are transferable across configurations and metal centers, while local steric descriptors lacked such transferability. Machine learning (ML) models successfully classified ligand configurations (balanced accuracy >0.8) but struggled to predict stability across metal centers, especially for Mn(I) and Ru(II). Thus, improved descriptors of the first coordination sphere to capture fluxionality and stability more effectively can improve ML models. Overall, this study underscores the limitations of ignoring stereoisomerism in virtual HT screening, which may lead to incomplete exploration of chemical space and underrepresentation of key catalyst features. Until dynamic digital representations are developed, exhaustive stereoisomerism exploration should be implemented for screening workflows.

Transition-metal complexes serve as highly enantioselective homogeneous catalysts for various transformations, making them valuable in the pharmaceutical industry. Data-driven prediction models can accelerate high-throughput catalyst design but require computer-readable representations that account for conformational flexibility. This is typically achieved through high-level conformer searches, followed by DFT optimization of the transition-metal complexes. However, conformer selection remains reliant on human assumptions, with no cost-efficient and generalizable workflow available. To address this, we introduce an automated approach to correlate CREST(GFN2-xTB//GFN-FF)-generated conformer ensembles with their DFT-optimized counterparts for systematic conformer selection. We analyzed 24 precatalyst structures, performing CREST conformer searches, followed by full DFT optimization. Three filtering methods were evaluated: (i) geometric ligand descriptors, (ii) PCA-based selection, and (iii) DBSCAN clustering using RMSD and energy. The proposed methods were validated on Rh-based catalysts featuring bisphosphine ligands, which are widely employed in hydrogenation reactions. To assess general applicability, both the precatalyst and its corresponding acrylate-bound complex were analyzed. Our results confirm that CREST overestimates ligand flexibility, and energy-based filtering is ineffective. PCA-based selection failed to distinguish conformers by DFT energy, while RMSD-based filtering improved selection but lacked tunability. DBSCAN clustering provided the most effective approach, eliminating redundancies while preserving key configurations. This method remained robust across data sets and is computationally efficient without requiring molecular descriptor calculations. These findings highlight the limitations of energy-based filtering and the advantages of structure-based approaches for conformer selection. While DBSCAN clustering is a practical solution, its parameters remain system-dependent. For high-accuracy applications, refined energy calculations may be necessary; however, DBSCAN-based clustering offers a computationally accessible strategy for rapid catalyst representations involving conformational flexibility.

Catalysis Science & Technology, Evgeny Pidko and Núria López would like to acknowledge Weixue Li for their contributions to the Digital Catalysis themed collection as a Guest Editor.

Some of our recent developments and applications of algorithmic graph theory for extracting the physical and chemical properties of materials from molecular dynamics simulations are presented. From the chemical viewpoint, the power of graph theory is illustrated in the search for a catalyst's active sites at a silica solid surface. From the physical viewpoint, we present graph algorithms that recognize the structural motifs that exist at the silica/liquid water interface. Statistical analyses of the instances of these surface–water motifs provide a detailed understanding of the structures and dynamics at the aqueous interface.

Data-driven catalyst design is a promising approach for addressing the challenges in identifying suitable catalysts for synthetic transformations. Models with descriptor calculations relying solely on the precatalyst structure are potentially generalizable but may overlook catalyst-substrate interactions. This study explores substrate-specific interactions in the context of Rh-catalyzed asymmetric hydrogenation to elucidate the impact of substrate inclusion on the catalyst structure and on the descriptors derived from it. We compare a catalyst-substrate complex with methyl 2-acetamidoacrylate as a model substrate with the generic precatalyst structure involving a placeholder substrate, norbornadiene, across 11 Rh-based catalysts with bidentate bisphosphine ligands. For these systems, a full conformer ensemble analysis reveals an intriguing finding: the rigid substrate induces conformational freedom in the ligand. This flexibility gives rise to a more diverse conformer landscape, showing a previously overlooked aspect of catalyst-substrate dynamics. Electronic descriptor variations particularly highlight differences between substrate-specific and precatalyst structures. This study suggests that generic precatalyst-like models may lack crucial insights into the conformational freedom of the catalyst. We speculate that such conformational freedom may be a more general phenomenon that can influence the development of generalizable predictive models of computational TM-based catalysis.

The premise of most studies on the homogeneous electrocatalytic CO₂ reduction reaction (CO₂RR) is a good understanding of the reaction mechanisms. Yet, analyzing the reaction intermediates formed at the working electrode is challenging and not always attainable. Here, we present a new, general approach to studying the reaction intermediates applied for CO₂RR catalyzed by a series of cobalt complexes. The cobalt complexes were based on the TPA-ligands (TPA = tris(2-pyridylmethyl)amine) modified by amino groups in the secondary coordination sphere. By combining the electrochemical experiments, electrochemistry-coupled electrospray ionization mass spectrometry, with density functional theory (DFT) calculations, we identify and spectroscopically characterize the key reaction intermediates in the CO₂RR and the competing hydrogen-evolution reaction (HER). Additionally, the experiments revealed the rarely reported in situ changes in the secondary coordination sphere of the cobalt complexes by the CO₂-initiated transformation of the amino substituents to carbamates. This launched an even faster alternative HER pathway. The interplay of three catalytic cycles, as derived from the experiments and supported by the DFT calculations, explains the trends that cobalt complexes exhibit during the CO₂RR and HER. Additionally, this study demonstrates the need for a molecular perspective in the electrocatalytic activation of small molecules efficiently obtained by the EC-ESI-MS technique.

Herein, we present a practical strategy to enhance the performance of a multiphase system for bicarbonate hydrogenation based on a molecular Ru-PNP pincer catalyst. This study demonstrates that the use of organic antioxidants not only mitigates catalyst degradation but also significantly boosts its intrinsic activity. This enables efficient catalyst recycling at ultra-low concentrations. Systematic screening and optimization of a range of organic antioxidants has identified TDTBP (tris(2,4-di-tert-butylphenyl) phosphite) as being exceptionally efficient in stabilizing and enhancing the performance of the Ru-PNP catalyst. With the optimized system an unprecedented integral turnover frequency (TOF) of 115,000 h⁻¹ and a total turnover number (TTON) of 9.43×10⁶ across four recycling tests were demonstrated, conducted at a reaction temperature of 90 ºC and H₂ pressure of 50 bar. These findings represent a substantial advancement in sustainable formate/formic acid production, offering a scalable and highly efficient method suitable for industrial-scale application.

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and that selecting a catalyst that induces the desired reactivity or selectivity is a trivial task. Nonetheless, ligand engineering or selection for any new prochiral olefin remains an empirical trial-error exercise. In this study, we investigated whether machine learning techniques could be used to accelerate the identification of the most efficient chiral ligand. For this purpose, we used high throughput experimentation to build a large dataset consisting of results for Rh-catalyzed asymmetric olefin hydrogenation, specially designed for applications in machine learning. We showcased its alignment with existing literature while addressing observed discrepancies. Additionally, a computational framework for the automated and reproducible quantum-chemistry based featurization of catalyst structures was created. Together with less computationally demanding representations, these descriptors were fed into our machine learning pipeline for both out-of-domain and in-domain prediction tasks of selectivity and reactivity. For out-of-domain purposes, our models provided limited efficacy. It was found that even the most expensive descriptors do not impart significant meaning to the model predictions. The in-domain application, while partly successful for predictions of conversion, emphasizes the need for evaluating the cost-benefit ratio of computationally intensive descriptors and for tailored descriptor design. Challenges persist in predicting enantioselectivity, calling for caution in interpreting results from small datasets. Our insights underscore the importance of dataset diversity with broad substrate inclusion and suggest that mechanistic considerations could improve the accuracy of statistical models.

Supported rhenium (Re) catalysts are emerging as promising candidates for hydrogenation reactions, which are crucial in industrial processes such as biomass valorization, CO2 reduction, and petroleum refining. However, despite their broad application, the structural and mechanistic understanding of these systems remains limited. The strong oxophilic nature of Re, combined with its ability to adopt multiple oxidation states, complicates the characterization of the active species even with advanced experimental techniques. In this study, we employ density functional theory calculations, alongside ab initio thermodynamic analysis, to systematically explore the structural and electronic properties of single-atom Re catalysts on a TiO2 surface, providing insights that could inform the rational catalyst design. Our calculations reveal the formation of stable Re polyhydrides on the surface under hydrogen-rich conditions. Notably, even in highly reducing environments, Re species with low formal oxidation states are thermodynamically unfavorable. The stable Re species identified on TiO2 surfaces demonstrate high reactivity toward CO2 hydrogenation. The electronic properties and computed X-ray photoelectron spectroscopy (XPS) signatures of the feasible surface species are highly influenced by the ligand environment. This work highlights the limitation of routine interpretation of experimental XPS characterization data in terms of the formal oxidation state.

Organic electrochemistry is currently experiencing an era of renaissance, which is closely related to the possibility of carrying out organic transformations under mild conditions, with high selectivity, high yields, and without the use of toxic solvents. Combination of organic electrochemistry with alternative approaches, such as photo-chemistry was found to have great potential due to induced synergy effects. In this work, we propose for the first time utilization of plasmon triggering of enhanced and regio-controlled organic chemical transformation performed in photoelectrochemical regime. The advantages of the proposed route is demonstrated in the model amination reaction with formation of C−N bond between pyrazole and substituted benzene derivatives. Amination was performed in photo-electrochemical mode on the surface of plasmon active Au@Pt electrode with attention focused on the impact of plasmon triggering on the reaction efficiency and regio-selectivity. The ability to enhance the reaction rate significantly and to tune products regio-selectivity is demonstrated. We also performed density functional theory calculations to inquire about the reaction mechanism and potentially explain the plasmon contribution to electrochemical reaction rate and regioselectivity.