Recent advances in the routes and catalysts for ethanol synthesis from syngas

In Situ Surfaced Mn-Mn Dimeric Sites Dictate CO Hydrogenation Activity and C2 Selectivity over MnRh Binary Catalysts

Massive ethanol production has long been a dream of human society. Despite extensive research in past decades, only a few systems have the potential of industrialization: specifically, Mn-promoted Rh (MnRh) binary heterogeneous catalysts were shown to achieve up to 60% C2 oxygenates selectivity in converting syngas (CO/H2) to ethanol. However, the active site of the binary system has remained poorly characterized. Here, large-scale machine-learning global optimization is utilized to identify the most stable Mn phases on Rh metal surfaces under reaction conditions by exploring millions of likely structures. We demonstrate that Mn prefers the subsurface sites of Rh metal surfaces and is able to emerge onto the surface forming MnRh surface alloy once the oxidative O/OH adsorbates are present. Our machine-learning-based transition state exploration further helps to resolve automatedly the whole reaction network, including 74 elementary reactions on various MnRh surface sites, and reveals that the Mn-Mn dimeric site at the monatomic step edge is the true active site for C2 oxygenate formation. The turnover frequency of the C2 product on the Mn-Mn dimeric site at MnRh steps is at least 107 higher than that on pure Rh steps from our microkinetic simulations, with the selectivity to the C2 product being 52% at 523 K. Our results demonstrate the key catalytic role of Mn-Mn dimeric sites in allowing C-O bond cleavage and facilitating the hydrogenation of O-terminating C2 intermediates, and rule out Rh metal by itself as the active site for CO hydrogenation to C2 oxygenates.

On the nature of Cu-carbon interaction through N-modification for enhanced ethanol synthesis from syngas and methanol

Direct conversion of syngas into ethanol is an attractive process because of its short route and high-added value, but remains an enormous challenge due to the low selectivity caused by unclear active sites. Here, the Cu(111) supported N-modified graphene fragments C13-mNm/Cu(111) (m = 0-2) are demonstrated to be an efficient catalyst for fabricating ethanol from syngas and methanol. Our results suggest that the Cu-carbon interaction not only facilitates CO activation, but also significantly affects the adsorption stability of C2 intermediates and finally changes the fundamental reaction mechanism. The impeded hydrogenation performance of C13/Cu(111) due to the introduced Cu-carbon interaction is dramatically improved by N-doping. Multiple analyses reveal that the promoted electron transfer and the enhanced electron endowing ability of C13-mNm/Cu(111) (m = 1-2) to the co-adsorbed CH3CHxOH (x = 0-1) and H are deemed to be mainly responsible for the remarkable enhancement in hydrogenation ability. From the standpoint of the frontier molecular orbital, the decreased HOMO-LUMO gap and the increased overlap extent of HOMO and LUMO with the doping of N atoms also further verify the more facile hydrogenation reactions. Clearly, the Cu-carbon interaction through N-modification is of critical importance in ethanol formation. The final hydrogenation reaction during ethanol formation is deemed to be the rate-controlling step. The insights gained here could shed new light on the nature of Cu-carbon interaction in carbon material modified Cu-based catalysts for ethanol synthesis, which could be extended to design and modify other metal-carbon catalysts.

Active learning streamlines development of high performance catalysts for higher alcohol synthesis

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h-1 gcat-1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.

Tandem Acidic CO2 Electrolysis Coupled with Syngas Fermentation: A Two-Stage Process for Producing Medium-Chain Fatty Acids

The tandem application of CO2 electrolysis with syngas fermentation holds promise for achieving heightened production rates and improved product quality. However, the significant impact of syngas composition on mixed culture-based microbial chain elongation remains unclear. Additionally, effective methods for generating syngas with an adjustable composition from acidic CO2 electrolysis are currently lacking. This study successfully demonstrated the production of medium-chain fatty acids from CO2 through tandem acidic electrolysis with syngas fermentation. CO could serve as the sole energy source or as the electron donor (when cofed with acetate) for caproate generation. Furthermore, the results of gas diffusion electrode structure engineering highlighted that the use of carbon black, either alone or in combination with graphite, enabled consistent syngas generation with an adjustable composition from acidic CO2 electrolysis (pH 1). The carbon black layer significantly improved the CO selectivity, increasing from 0% to 43.5% (0.05 M K+) and further to 92.4% (0.5 M K+). This enhancement in performance was attributed to the promotion of K+ accumulation, stabilizing catalytically active sites, rather than creating a localized alkaline environment for CO2-to-CO conversion. This research contributes to the advancement of hybrid technology for sustainable CO2 reduction and chemical production.

Unsupervised machine learning reveals eigen reactivity of metal surfaces

The reactivity of metal surfaces is a cornerstone concept in chemistry, as metals have long been used as catalysts to accelerate chemical reactions. Although fundamentally important, the reactivity of metal surfaces has hitherto not been explicitly defined. For example, in order to compare the activity of two metal surfaces, a particular probe adsorbate, such as O, H, or CO, has to be specified, as comparisons may vary from probe to probe. Here we report that the metal surfaces actually have their own intrinsic/eigen reactivity, independent of any probe adsorbate. By employing unsupervised machine learning algorithms, specifically, principal component analysis (PCA), two dominant eigenvectors emerged from the binding strength dataset formed by 10 commonly used probes on 48 typical metal surfaces. According to their chemical characteristics revealed by vector decomposition, these two eigenvectors can be defined as the covalent reactivity and the ionic reactivity, respectively. Whereas the ionic reactivity turns out to be related to the work function of the metal surface, the covalent reactivity cannot be indexed by simple physical properties, but appears to be roughly connected with the valence-electron number normalized density of states at the Fermi level. Our findings expose that the metal surface reactivity is essentially a two-dimensional vector rather than a scalar, opening new horizons for understanding interactions at the metal surface.

Spectroscopic characterization of carbon monoxide activation by neutral chromium carbides

Spectroscopic characterization of carbon monoxide activation by neutral metal carbides is of essential importance for understanding the structure-reactivity relationships of catalytic sites, but has been proven to be very challenging owing to the difficulty in size selection. Here, we report a size-specific infrared-vacuum ultraviolet spectroscopic study of the reactions between carbon monoxide with neutral chromium carbides. Quantum chemical calculations were carried out to identify the low-lying structures and to interpret the experimental features. The results reveal that the most stable structure of CrC3(CO)2 consists of a CCO ketenylidene unit and that of CrC4(CO)2 has a semi-bridging CO with a very low CO stretching vibrational frequency at 1821 cm-1. The electron structure analyses show that this semi-bridging CO is highly activated through the delocalized Cr-C-C three-center two-electron (3c-2e) interaction between the antibonding orbitals of CO and the metal carbide skeleton. The formation of these metal carbide carbonyls is found to be both thermodynamically exothermic and kinetically facile in the gas phase. The present findings have important implications for the mechanical understanding of the catalytic processes with isolated metal atoms/clusters dispersed on supports.

A thorough mechanistic study of ethanol, acetaldehyde, and ethylene adsorption on Cu-MOR via DFT analysis

A comprehensive study combining the density functional theory (DFT) and ab initio thermodynamic analysis was conducted to unravel the active sites and adsorption mechanisms of ethanol, acetaldehyde, and ethylene on various copper-modified mordenite (Cu-MOR) configurations, including Cu3/MOR, Cu3O3/MOR, and Cu6/MOR. This research involved an exhaustive exploration of structural and formation energies, revealing that the formation energies of these structures are temperature-dependent. Despite all three structures thermodynamically accommodating ethanol adsorption, their respective adsorption mechanisms differ significantly. In Cu3/MOR, weak van der Waals interactions predominate, while strong Cu-OOH interactions in Cu6/MOR facilitate ethanol dehydration. Conversely, Cu3O3/MOR exhibits pronounced Cu3O3-HOH interactions that favor ethanol dehydrogenation. Notably, Cu3O3/MOR displays robust ethylene adsorption, which enhances the potential for further ethylene activation. In-depth Bader charge and density of states analyses underscore the varying strengths and electronic characteristics of these interactions. This research provides a theoretical foundation for the design of highly efficient Cu-MOR catalysts tailored for the selective conversion of ethanol.

Fabrication of a PdCu@SiO2@Cu core-shell-satellite catalyst for the selective hydrogenation of acetylene

Pd25Cu75@SiO2 core-shell and PdCu@SiO2@Cu core-shell-satellite architectures were fabricated by silica-coating of Pd25Cu75 colloids in a reverse microemulsion. Hydrolysis of tetraethylorthosilicate in the reverse microemulsion containing hydrazine and ammonia yielded a core-shell structure, while the use of ammonia only, instead of a mixture of hydrazine and ammonia, formed a core-shell-satellite structure. The ammonia-leached copper species migrated onto the developing silica shell and formed smaller Cu clusters. Air-calcination at 673 K followed by H2-reduction at 773 K of the as-synthesized samples removed the organic surfactants and generated the permeable porous silica shells. The core-shell catalyst consisted of a metal core (8.5 nm) and a silica shell (7.8 nm), while the core-shell-satellite catalyst was composed by a metal core (7.0 nm), a silica shell (8.0 nm), and satellite Cu clusters (1.4 nm) on the silica shell. When used to catalyze the selective hydrogenation of acetylene to ethylene, the core-shell-satellite catalyst showed substantially enhanced activity and stability because of the synergetic catalysis between the metal core and the surrounding Cu clusters.

Improved catalytic performance in gas-phase dimethyl ether carbonylation over facile NH4F etched ferrierite

Gas-phase dimethyl ether (DME) carbonylation to methyl acetate (MA) initiates a promising route for producing ethanol from syngas. Ferrierite (FER, ZSM-35) has received considerable attention as it displays excellent stability in the carbonylation reaction and its modification strategy is to improve its catalytic activity on the premise of maintaining its stability as much as possible. However, conventional post-treatment methods such as dealumination and desilication usually selectively remove framework Al or Si atoms, ultimately altering the intrinsic composition, crystallinity, and acidity of zeolites inevitably. In this study, we successfully prepared a series of hierarchical ZSM-35 materials through post-treatment with NH4F etching, which dissolved framework Al and Si at similar rates and preferentially attacked the defective sites. Interestingly, the produced pore systems effectively penetrated the [100] plane, offering elevated access to both the 8-membered ring (8-MR) and 10-membered ring (10-MR) channels. The physicochemical and acid properties of the pristine and NH4F etched ZSM-35 samples were comprehensively characterized using various techniques, including XRD, XRF, FESEM, HRTEM, Nitrogen adsorption-desorption, NH3-TPD, Py-IR, 27Al MAS NMR, and 29Si MAS NMR. Under moderate treatment conditions, the intrinsic microporous structure, acid properties, and crystallinity of zeolite were retained, leading to superior catalytic activity and stability with respect to the pristine sample. Nonetheless, severe NH4F etching disrupted the crystalline framework and created additional defective sites, bringing about faster deposition of coke precursors on the interior Brønsted acid sites (BAS) and decreased catalytic performance. This technique provides a novel and efficient method to slightly enhance the micropore and mesopore volume of industrially pertinent zeolites through a straightforward post-treatment, thus elevating the catalytic performance of these zeolites.

Hierarchical Dual Single-Atom Catalysts with Coupled CoN4 and NiN4 Moieties for Industrial-Level CO2 Electroreduction to Syngas

Renewable-driven electrochemical CO2 reduction reaction (CO2RR) to syngas is an encouraging alternative strategy to traditional fossil fuel-based syngas production, and the development of industrial-level electrocatalysts is vital. Herein, based on theoretical optimization of metal species, hierarchical CoxNi1-x-N-C dual single-atom catalyst (DSAC) with individual NiN4 (CO preferential) and CoN4 (H2 preferential) moieties was constructed by a two-step pyrolysis route. The Co0.5Ni0.5-N-C exhibits a stable CO Faradaic efficiency of 50 ± 5% and an industrial-level current density of 101-365 mA cm-2 in an ultrawide potential window of -0.5 to -1.1 V. The CO/H2 ratio of syngas can be conveniently tuned by regulating the Co/Ni ratio. The coupled effect of NiN4 and CoN4 moieties under a local high-pH microenvironment is responsible for the regulation of the CO/H2 selectivity and yield for the CoxNi1-x-N-C catalyst, which is not present in the mixed Co-N-C and Ni-N-C catalyst. This study provides a promising DSAC strategy for achieving industrial-level syngas production via CO2RR.

ZrO2-Promoted Cu-Co, Cu-Fe and Co-Fe Catalysts for Higher Alcohol Synthesis

The development of efficient catalysts for the direct synthesis of higher alcohols (HA) via CO hydrogenation has remained a prominent research challenge. While modified Fischer-Tropsch synthesis (m-FTS) systems hold great potential, they often retain limited active site density under operating conditions for industrially relevant performance. Aimed at improving existing catalyst architectures, this study investigates the impact of highly dispersed metal oxides of Co-Cu, Cu-Fe, and Co-Fe m-FTS systems and demonstrates the viability of ZrO2 as a general promoter in the direct synthesis of HA from syngas. A volcano-like composition-performance relationship, in which 5-10 mol % ZrO2 resulted in maximal HA productivity, governs all catalyst families. The promotional effect resulted in a 2.5-fold increase in HA productivity for the optimized Cu1Co4@ZrO2-5 catalyst (Cu:Co = 1:4, 5 mol % ZrO2) compared to its ZrO2-free counterpart and placed Co1Fe4@ZrO2-10 among the most productive systems (345 mgHA h-1 gcat-1) reported in this category under comparable operating conditions, with stable performance for at least 300 h. ZrO2 assumes an amorphous and defective nature on the catalysts, leading to enhanced H2 and CO activation, facilitated formation of metallic and carbide phases, and structural stabilization.

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.

Highly selective hydrogenation of dimethyl oxalate to methyl glycolate and ethylene glycol over an amino-assisted Ru-based catalyst

A Ru/NH₂-MCM-41 catalyst was prepared via a coordination-assisted strategy for chemoselective hydrogenation of dimethyl oxalate with a high selectivity of methyl glycolate (ca. 100%) and ethylene glycol (>90%) at reaction temperatures of 343 K and 433 K, respectively. The amino groups help to anchor and form stable electron-rich Ru active sites, which accounts for the excellent CO bond activation and hydrogenation selectivity.