Colloidal copper nanospheres boost propanol electrosynthesis from CO

Colloidal copper nanospheres boost propanol electrosynthesis from CO | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Physical Sciences - Article Colloidal copper nanospheres boost propanol electrosynthesis from CO Min Wang, Anna Loiudice, Enric Ibáñez Alé, Krishna Kumar, Dragos Stoian, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4544481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Achieving a carbon neutral manufacturing of chemicals is imperative to accelerate the transition towards a sustainable future. Propanol electrosynthesis from CO electroreduction represents a promising alternative to the current manufacturing of this chemical. Yet, the catalyst features driving propanol formation are poorly understood, which limits further advancement in the performance. Herein, we report on a comprehensive mapping of the sensitivity of the CO electroreduction to the catalyst structure exploiting well-defined copper nanocrystals (NCs) with tunable shape and size synthesized via colloidal chemistry. In addition to clarify the dependence from the exposed surfaces, we discover that spheres uniquely promote n-propanol selectivity, which we explain mostly with strain effects. Driven by this novel insight, we achieve unprecedent n-propanol production via electrosynthesis with a copper catalyst. We demonstrate that colloidal copper nanospheres with a diameter of 4 nm deliver n-propanol faradaic efficiency of 39.6±1.4% at 119±4.2 mA/cm2 production rate, the latter being ten times the current state of the art for copper catalysts. Physical sciences/Chemistry/Catalysis/Electrocatalysis Physical sciences/Chemistry/Chemical synthesis/Nanoparticle synthesis Physical sciences/Chemistry/Green chemistry/Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Propanol is used in the food industry, in healthcare, in packaging, and is appealing as a transportation fuel additive. 1-3 Today, the manufacturing of this C 3 alcohol is based on the hydrogenation of propionaldehyde, which is produced by hydroformylation of ethylene with a rhodium phosphine catalyst. 4 This process occurs at high temperature and pressure, is highly energy demanding, relies on cracking and petroleum, and emits greenhouse gases. 1-4 The electrochemical CO reduction reaction (CORR) is an appealing process to produce chemicals at room temperature and ambient pressure while storing excess renewable electricity. 5,6 Technoeconomic analysis and experimental evidences suggest that the two-step electrosynthesis CO 2 to CO followed by CO to multi-carbon products outperform the one-step process in terms of efficiency and stability. 2,3,7-11 This attractive scenario has fostered an increase effort to overcome the selectivity challenge of CORR and to improve its efficiency towards one single chemical product. 12-24 The achievement of a significant faradaic efficiency (FE, i.e. electrons converted into chemicals) for C 3 products is particularly interesting. 16-24 However, further performance enhancement is needed to render n-propanol electrosynthesis a valuable process which operates with high selectivity at hundreds of milliamperes per square centimeters. A puzzling scenario currently exists in understanding how the structure of the catalyst impacts the selectivity of CORR under practical reaction conditions (i.e. high current densities). All the catalysts reported to produce n-propanol are Cu-based materials. 16-24 The presence of “adparticles” with low coordinated sites and combined strain with ligand effects have been used to explain the observed selectivity towards n-propanol in Cu only and in Cu doped with noble metals, respectively. 16-24 Studies on Cu single crystals have shown a structural sensitivity of CORR wherein (111) and (100) facets have a preference to form methane and ethylene, respectively. 25,26 Yet, such facet-dependent relationships have not been demonstrated at high current density. Furthermore, evidences indicate that CO adsorption is not sensitive to certain structural features of the catalyst (i.e. coordination numbers). 27 Overall, solving this puzzle is important to design catalysts which further increase selectivity in CORR, specifically for n-propanol electrosynthesis. Here, we exploit well defined Cu nanocrystals (NCs) with different shapes and sizes to provide a comprehensive mapping of catalyst structure/selectivity relationships in CORR. We reveal that facet-dependent selectivity does exist in CORR under practical reaction conditions. Through the combination of various state of the art experimental and computational data, we propose the lattice strain of spherical Cu NCs as one of the key features to boost the CORR towards alcohols and, more specifically, n-propanol. Based on this idea, we design a catalyst which achieves a production rate for n-propanol which is ten times the current state of the art for copper catalysts. Results And Discussion Cu NCs with different shapes and size were synthesized via colloidal methods ( Fig. 1a, Supplementary Fig. 1, Methods ). Cu cubes (Cu-Cube) and Cu octahedra (Cu-Octa) were chosen because they expose mostly (100) and (111) facets, respectively, whereas Cu sphere (Cu-Sph) exhibit a mixture of both facets on their surface. 28 Cu-Cube with edge length of ~40 nm, Cu-Octa with edge length of ~80 nm and Cu-Sph with diameter of ~80 nm (Cu-S80) have a comparable surface-to-volume ratio ( Supplementary Table 1 ). Cu spheres with diameter of ~34 nm (Cu-S34) and of ~7 nm (Cu-S7) were chosen to investigate the effect of size. All catalysts are mostly selective for C 2+ products ( Fig. 1b,c and Supplementary Table 2 ). The low C 1 production is in line with reported results under similar operating conditions. 12-24 The ethylene FE remains constant at ~20% for all catalysts. Cu-Cube produces the highest amount of acetate (FE ~ 40%). Acetate remains the main product for Cu-Octa, yet the selectivity towards this product drops (FE ~26%) and, concomitantly, methane appears (FE ~6%). An intriguing switch in selectivity from acetate to alcohols occurs when moving to the Cu-Sph. Cu-S80 shows high selectivity for C 2+ alcohols (FE ~50%) with n-propanol FE ~20% and ethanol FE ~29%. Alcohols increase further while acetate gets reduced as the size of the spheres decreases, reaching C 2+ alcohols (FE ~57%) with ~23% for n-propanol and ~34% for ethanol for the Cu-S7. Potential dependent product selectivity does not explain the changes in selectivity as only minor variation of the electrode potential occurs among the different systems ( Supplementary Table 2 , Supplementary Fig. 2 ). 31 pH effects were proposed to promote the switch in selectivity from acetate, at high pH, to n-propanol, at lower pH. 10 Our system operates at high pH; furthermore, the testing conditions are the same for all catalysts, thus pH effects are unlikely to play a role. CO coverage was shown to affect the CORR mechanism, and the selectivity of ethylene vs oxygenated (i.e. low CO coverage to ethylene and high CO coverage to oxygenated). 12,13,32,33 To explore eventual CO coverage effects, we tested our well-defined Cu NCs at different CO concentration in an inert N 2 carrier ( Fig. 1d, Supplementary Fig. 3 ). The decrease of acetate at lower CO % in both Cu-Cube and Cu-Octa, is accompanied by the increase of ethylene and methane, respectively. No change of the product distribution is observed for the Cu-S7. With regards to the preferential ethylene selectivity and the CO coverage product dependence/switch to acetate ( Fig. 1, Supplementary Fig. 2,3 ), the behavior of Cu-Cube is consistent with what observed in the literature and is in line with the idea that most polycrystalline copper exposes (100) facets during CORR. 12,25,26 The observation that methane selectivity is higher on Cu-Octa exposing the (111) surface compared to the Cu-Cube also agrees with the structure-dependence observed on single crystal. 25,26 The fact that this structural sensitivity becomes more evident at lower CO coverage and that methane and acetate might share a reaction intermediate on the (111) surface in CORR are new insights. A mixture of (111) and (100) facets was previously used to explain the propanol selectivity of Cu “adparticles”. 17 However, the insensitivity of the product distribution of the Cu-S7 to the CO coverage highlights that structural effects other than the exposed facets must be considered to explain the unique nature of their propanol-selective active sites. The formation of grain boundaries and possible residual oxide were previously proposed as catalyst features accounting for alcohol production in both CO 2 RR and CORR. 34-37 X-Ray diffraction and electron microscopy reveal that the morphology of the catalysts does not change after the collected CORR data ( Supplementary Fig. 4 , Supplementary Note 1 ), thus the grain boundaries forming during operation can be excluded. We also excluded any major impact on selectivity of residual oxidized copper during CORR by performing operando X-ray absorption spectroscopy and other control experiments ( Supplementary Fig. 5-8 , Supplementary Note 1 ). Differences in local environment and/or mass transport among the samples can also generate different distribution of multi-carbon products. 10 A similar distribution of all the catalysts through the gas diffusion layer also excluded these factors as a major contributor ( Supplementary Fig. 9 ). The native ligands used for the synthesis of the catalysts also do not appear to influence the product distribution ( Supplementary Fig. 10 ). Lattice strain can alter the binding energy of adsorbates and, thus, impact selectivity of a given catalytic reaction. 38,39 Convoluted strain and ligand effects were proposed to explain the n-propanol selectivity observed in Cu catalysts doped with Ag and Ag-Ru. 20,21 Strain effect in Cu catalyst have been recently suggested as a mean to lower the activation barrier for CORR. 40 Size and shape dependence of strain in nanoparticles of noble metals more commonly used in catalysis, such as Pt, have been largely explored and different strategies implemented to modulate it, including doping/alloying and support epitaxial interactions. 41- 43 Yet, how shape and size modify the strain in Cu NCs is an under investigated topic. We used Neural Network potential 44 and Molecular Dynamics (NN-MD, NVT ensemble) to obtain the average strain of surface atoms from models of Cu cubes, octahedra and sphere at three different sizes (2.5 nm, 5.0 nm, 7.5 nm) ( Fig . 2a,b , Methods and Supplementary Information). The spheres possess the highest average strain among the shapes at 5.0 nm and 7.5 nm ( Fig . 2b ), which results in a wider distribution of the Cu-Cu bond length histograms ( Fig . 2c ). The same trends were observed when testing effects of surface reconstruction (i.e. randomly removing 5% of total atoms and of surface atoms) and surface oxidation (i.e. replacing the removed 5% Cu atoms with oxygen atoms) ( Supplementary Figs. 11-17 ), which corroborate the experimental conclusion that possible minor surface reconstruction and oxidation do not explain the observed CORR selectivity. To investigate how the strain impacts the selectivity, we created computationally affordable active site models from the large NN-MD simulation ( Fig. 2d , Supplementary Note 2 and Supplementary Figs. 18-20 ). These active site models allowed us to assess the adsorption energies of key reaction intermediates to acetate and to alcohols ( Fig. 2e ). 45,46 Thus, we compared the adsorption energies of intermediates of competing mechanisms ( Fig. 2f,g ), on 30 active site models extracted from the spheres (10 for each size) and on Cu(211) step sites, as representative of cubes and octahedra. All the active site models of the spheres show preference for alcohols over acetate compared to cubes and octahedra, which is indicated by the stronger binding towards *OCHCH compared to the step sites ( Fig. 2f ). Furthermore, 28 out of 30 active sites for the spheres exhibit stronger binding energy for *OCHCHCOH compared to step sites ( Fig. 2g ). Among these sites, most of those derived from the smaller spheres (5 nm and 2.5 nm) possess a similar (or even weaker) binding energy for *OCHCH 2 than Cu(211), which points at a higher boost of n-propanol selectivity over ethanol selectivity for these sizes which have the highest local strain ( Supplementary Fig. 21 and Supplementary Table 3 ). In addition, the sites extracted from the spheres show a linear correlation between local strain and d-band center energy ( Supplementary Fig. 22 ), which indicate that strain impacts both electronic and structural local properties on the active sites. Following the insight from the theory, we analyzed eventual strain differences in the as-synthesized catalysts and during operation ( Fig. 3 , Supplementary Note 3 ). Two-dimensional strain maps based on the HR-TEM images of single particles ( Fig. 3a and Supplementary Fig. 23 ) and X-Ray diffraction at the ensemble level ( Supplementary Fig. 24 ) for the as-synthesized NCs indicate a higher degree of strain for the Cu-Sph catalysts, with the Cu-S7 showing a higher overall strain (higher density of strained regions in blue color). EXAFS analysis of the as-prepared Cu NCs ( Supplementary Fig. S25 ) and during CORR ( Fig. 3b-d, Supplementary Fig. S26, Supplementary Table 4 ) agree with this observation. All samples are metallic Cu during operation. A shift of the Cu-Cu bond length (R) is observed in the Cu-Sph compared to Cu-Cube and Cu-Octa ( Fig. 3b ). The Cu-Cu bond length of Cu-Cube and Cu-Octa closely matches the one of the bulk Cu foil (2.541 Å). A deviation from this value occurs for all the Cu-Sph samples with an elongated bond length of 2.545 Å for Cu-S80 and Cu-S34 and a contracted bond length of 2.533 Å for Cu-S7 ( Fig. 3c , Supplementary Table 4 ). This change results in a bigger relative strain (DR/R) for the Cu-Sph compared to the Cu-Octa and Cu-Cube, with the highest value for Cu-S7 ( Fig. 3d ). The lowering intensity and increasing broadening of the signal in the k-R Wavelet transformed-EXAFS (WT-EXAFS) contour maps going from Cu-Octa and Cu-Cube to Cu-Sph7 indicates a larger distribution of the Cu-Cu bond length ( Fig. 3e ), in agreement with the histograms from NN-MD in Fig. 2c. Overall, the differences among the samples are certainly subtle, due to the substantial contribution from the bulk in the size regime of the Cu NCs used in this work. In-situ and operando strain measurements are experimentally challenging, especially for nanomaterials. 41 Yet, the experimental data corroborates the theory simulations pointing at a higher strain in the propanol-selective Cu-Sph catalysts compared to Cu-Cube and Cu-Octa even during CORR. Inspired by this new insight, we hypothesized that smaller Cu NCs (i.e. more strained) would exhibit an even higher selectivity for n-propanol. Thus, we designed a synthesis for Cu spheres with a diameter of ~4.0 nm in size (Cu-S4, Fig. 4a) . Cu-S4 exhibited a FE for n-propanol of 34.8±1.42% stable for at least 12 hours ( Fig. 4b , Supplementary Fig. 27 ). This FE for n-propanol is 1.5 times higher compared to Cu-S7 when tested under the same conditions at 100 mA/cm 2 , which corroborates a clear trend of increasing selectivity towards n-propanol as the size decreases ( Fig. 4b , Supplementary Table 4) . Having excluded a major impact of other factors (i.e. catalyst reconstruction, surface oxidation, potential and mass transport effects), this size-dependent trend strengthens the hypothesis that strain effects are key in boosting n-propanol production in CORR. Comparison with commercial Cu NCs in a similar size regime indicates a unique behavior of the colloidally synthesized Cu NCs ( Fig. 4b , Supplementary Fig. 28 ). Strain induced by the synthesis method itself or the wider size distribution mitigating size-dependent effects can both explain the lower n-propanol selectivity of the commercial Cu NCs. The Cu-S4 exhibit an increase of the FE for n-propanol to 39.6±1.4 % with a corresponding partial current density of 118±4.2 mA/cm 2 when tested at 300 mA/cm 2 ( Fig. 4c, Supplementary Fig. 27 ). This n-propanol production rate is ten times higher than the best reported Cu catalyst to date ( Fig. 4c and Supplementary Table 5 ) and superior to some of those reported for Cu doped with noble metals ( Supplementary Fig. 29 , and Supplementary Table 5) . Cu-S4 exhibits also the highest ratio between the FE of n-propanol and of C 2 ( Fig. 4d and Supplementary Fig. 29 ), further indicating that copper with the highest strain can promote the coupling reaction between C 1 and C 2 towards C 3 compounds. This insight might open new reactivity pathways towards other multi-carbon products in the future. Overall, this work demonstrates that a precise control on the size and morphology of Cu nanostructures enables an enhanced n-propanol electrosynthesis without the use of noble metals. The results highlight the importance of catalyst design and inspire future work in catalyst engineering towards carbon neutral manufacturing of chemicals. Methods Synthesis of colloidal Cu NCs Cu NCs were synthesized following reported protocols. 47,48 Cu-Cube: Tri-n-octylphosphine oxide (TOPO, 24 mmol, 9.37 g) was mixed with oleylamine (OLAM, 117 ml) in a 3-neck flask and degassed under vacuum at room temperature. Copper(I) bromide (CuBr, 5 mmol, 0.71 g) was then added to the solution under N 2 flow. The resulting solution was heated to 260 °C and held at this temperature for 1 h. Cu-S80 and Cu-S34: TOPO was substituted by tri-n-octylphosphine (TOP) in the same synthetic protocol used for the Cu-Cube. For Cu-S34, TOP (0.450 mL) was added together with CuBr (86 mg) and OLAM (7 mL) in a 3-neck flask and the reaction mixture was degassed. The resulting solution was heated to 270 °C and held at this temperature for 1 h. By varying the amounts of the same reagents, Cu-S80 could be obtained. Specifically, TOP (3.5 mL), CuBr (0.92 mg) and OLAM (117 mL) were mixed in a 3-neck flask and the rest of the procedure was kept the same as described for Cu-S34. Cu-Octa: TOP (0.450 mL), CuBr (116 mg) and OLAM (15 mL) were added to a 3-neck flask and degassed. The rest of the procedure is the same of the one reported for Cu-S34 and Cu-S80. Cu-S7 and Cu-S4: trioctylamine (20 mL) was added to a 3-neck flask and degassed under vacuum at 130 ºC for 1 hour. The flask was then refilled with N 2 gas and cooled to 50 ºC. Tetradecylphosphonic acid (270 mg, 1 mmol) and copper(I) acetate (245 mg, 2 mmol) were added in the flask, and the mixture was heated to 180 °C and held for 30 minutes. After 30 minutes, the mixture was heated to 270 °C and held for 30 min to obtain Cu-S7. The temperature was set to 230 °C to obtain Cu-S4 and the reaction quenched by removing the heating mantle as soon as the set temperature was reached. Additional details on the Cu NCs synthesis can be found in the Supplementary Information. Electrochemical measurements Electrodes were prepared by air brushing dispersions of Cu NCs in hexane solvent on gas diffusion layer (GDL) with area of 1.33 cm 2 . Additional details on the electrode preparation are reported in the Supplementary Information. A flow cell electrolyzer was used for the electrocatalytic testing. 28 Ag/AgCl electrode (leak free series, Innovative Instruments, Inc.) were used as the reference electrode, and anionic exchange membrane (Fumasep FAB-PK-130) was interposed between the anolyte and the catholyte compartments, as counter electrode a nickel mesh (Mc Master–Carr, 100x100 mesh size) was used. Chronopotentiometry measurements were performed with a potentiostat Biologic SP-300. A typical experiment was run for around 75 min. A gas chromatograph (GC 8610C, SRI instruments) equipped with a HayeSep D porous polymer column, thermal conductivity detector, and flame ionization detector was used for the analysis of gaseous products. Gas sampling was performed at regular intervals of 10 minutes. High-performance liquid chromatography (HPLC) or NMR were used for the analysis of the liquid products. Further experimental details can be found in the Supplementary Information. Material Characterization Low resolution TEM images were acquired on a FEI Tecnai-Spirit at 120 kV. The as-synthesized materials were drop-casted on a copper TEM grid (Ted Pella, Inc.) prior to imaging. HRTEM images were acquired on a double Cs-corrected FEI Titan Themis 60 – 300 operated at 300 kV. GPA was performed in Digital Micrograph version 3.52 using a GPA plugin available at https://er-c.org/index.php/software/stem-data-analysis/gpa/ . 49,50,51 XAS experiments were performed at the Swiss-Norwegian beamlines BM31 at the European Synchrotron Radiation Facility in Grenoble, France. The samples were prepared in the same way of the sample used for the electrocatalytic testing with a loading of 100 mg/cm 2 and with a similar cell. Only the gas compartment was modified so that a Kapton foil could be introduced to allow X-ray penetration. The standards Cu 2 O and CuO were prepared by using pressed pellets with the sample diluted in a light matrix such as boron nitride or cellulose to obtain an appropriate thickness. The measurements were carried out in fluorescence mode at an incident angle of approximately 45°. A Si(111) double crystal monochromator was used to condition the beam from the bending magnet source to a size of 3 mm (horizonatal) and 300 mm (vertical). Fluorescence X-ray absorption near edge structure (XANES) spectra were acquired using a Vortex single-element silicon drift detector with XIA-Mercury digital electronics and a time resolution of 60 sec per spectrum. Cu foil standard XAS spectrum was collected in transmission using ionization chambers for transmission detection. The resulting XAS data were reduced and normalized using Athena and Larch package. 52,53 Computational details Neutral-network potentials are employed in classical molecular dynamics (NN-MD) for Cu NCs of different shapes (cube, octahedra, sphere) and sizes (2.5, 5.0, and 7.6 nm) were performed using Large-scale Atomic/ Molecular Massively Parallel Simulator (LAMMPS) code 54 with the NN interface from a neural network potential package (n2p2). 55,56 The NN-MDs were run for 100 ps for reach equilibrium in the canonical ensemble (NVT) using our constructed Behler-Parrinello-type HDNNP. 44,57 The sites of interest for reactivity study were selected considering coordination number and strain analysis (see Supplementary Information), and cut from NN-MD final structure as a 12x12x6 Å 3 -edge cube with the active site centered on the top facet. They were further placed as adclusters on top of Cu(100) and Cu(111) 3-layer surfaces using DockOnSurf 58 to avoid electronic structure embedding instabilities. DFT simulations were carried out with Vienna Ab Initio Simulation Package (VASP 5.4.4) 59,60 to compute adsorption energies of key intermediates. The exchange functional used was the Perdew-Burke-Emzerhof (PBE). 61 Dispersion was included though the DFT-D2 method 62,63 with our reparametrized C 6 coefficients for Cu atoms. 64 Inner electrons were represented by Projector Augment Wave (PAW), 65,66 while the valence monoelectronic states were expanded as plane waves with a kinetic energy cutoff of 450 eV. In the final slabs, the vacuum along the z direction was at least 10 Å. We sampled the Brillouin zone by a Γ-centered k-points mesh from the Monkhorst-Pack method 67 with a reciprocal grid size smaller than 0.03·2π Å –1 . The slab models were asymmetric, and thus the dipole correction was applied. 68 For all the investigated systems, structures were relaxed using convergence criteria of 0.03 eV/Å and 10 −5 eV for the ionic and electronic steps, respectively. Reported adsorption energies are obtained using CO 2 (g), H 2 (g), H 2 O(g) and clean surfaces as energy references. The Computational Hydrogen Electrode (CHE) was used for obtaining the relative energy between H + and gas-phase H 2 at U = 0.0 V and pH = 0. 69,70 Declarations Conflicts of interest There are no conflicts to declare. Acknowledgements This publication was created as part of NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation. E.I.A., Z.L. and N.L. acknowledge financial support from the Spanish Ministry of Science and Innovation (PRE2021-097615, PID2021-122516OB-I00, Severo Ochoa Centre of Excellence CEX2019-000925-S 10.13039/501100011033) and the Barcelona Supercomputing Centre-Mare Nostrum (BSC-RES) for providing generous computational resources. The authors thank Aurélien Bornet, Pascal A. Schouwink and Emad Oveisi for their help with the NMR, XRD and TEM measurements, respectively. The staff at the BM28 (XmaS) beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble are acknowledged for their support. Access to the beamline was granted through the ESRF under proposal a311213. Data availability All data are available in the main text or the Supplementary Information. The data of the main text will be available in Zenodo upon acceptance. Supporting DFT datasets are available in ioChem-BD 71 at https://iochem-bd.iciq.es/browse/review-collection/100/68301/2ee66f560d478ddb190e6e82 . All other raw data are available from the corresponding author upon reasonable request. Author contributions M.W. and A.L. designed and performed the experiments related to catalyst synthesis and electrocatalysis. A.L performed all the material characterization, both ex-situ and operando. E.I.A, Z.L. and N.L. designed and carried out the theoretical part of the work. K.K. and D.S. provided assistance with the data collection and contributed to the analysis of electron microscopy and XAS, respectively. P.A, L.Z, J.L. helped with ICP, NC synthesis and NMR liquid product analysis respectively and contributed with daily discussions. R. B. conceived the idea and coordinated the project. All the authors contributed to discussions and the writing of the manuscript. References 1 Mosselman, C. & Dekker, H. Enthalpies of Formation of N-Alkan-1-Ols. J. Chem. Soc. Faraday Trans. 1 Phys. Chem. Condens. Phases 71 (0), 417– 424 (1975). 2 Jouny, M., Luc, W. & Jiao F. General Techno-Economic Analysis of CO2 Electrolysis Systems. Ind. Eng. Chem. Res. 57 (6), 2165– 2177 (2018). 3 Li, C., Ciston, J. & Kanan, M. Electroreduction of carbon monoxide to liquid fuel on oxide-derived nanocrystalline copper. Nature 508, 504–507 (2014). 4 Klabunde, J., Bischoff, C. & Papa, A. Propanols. In Ullmann’s Encyclopedia of Industrial Chemistry; Wiley: 1– 14, (2018). 5 Jouny, M., Luc, W. & Jiao, F. High-rate electroreduction of carbon monoxide to multi-carbon products. Nat. Catal. 1, 748-755 (2018). 6 Jouny, M., Hutchings, G.S. & Jiao, F. Carbon monoxide electroreduction as an emerging platform for carbon utilization. Nat. Catal. 2, 1062-1070 (2019). 7 Siler, J., Khan, S., Ip, A.H., Schreiber, M.W., Jaffer, S.A., Bobicki, E.R., Dinh, C.-T., & Sargent, E.H. Etylene electrosynthesis: a comparative techno-economic analysis of alkaline vs membrane electrode assembly vs CO2-CO-C2H4. ACS Energy Lett. 6, 3, 997–1002 (2021). 8 Cuellar, R.N.S, Scherer, C., Kaçkar, B., Eisenreich, W., Huber, C., Wiesner-Fleischer, K., Fleischer, M., Hinrichsen, O. Two-step electrochemical reduction of CO2 towards multi-carbon products at high current densities. J. CO2 util. 36, 263, 220 (2020). 9 Wang, L., Nitopi, S., Wong, A.B., Snider, J.L., Nielander, A.C., Morales-Guio, C.G., Orazov, M., Higgins, D.C., Hahn, C., & Jaramillo, T. Electrochemically converting carbon monoxide to liquid fuels by directing selectivity with electrode surface area. Nat. Catal. 2, 702-708 (2019). 10 Kim, J.Y., Sellers, C., Hao, S. et al. Different distributions of multi-carbon products in CO2 and CO electroreduction under practical reaction conditions. Nat. Catal. 6, 1115–1124 (2023). 11 Xu, Q., Garg, S., Moss, A.B. et al. Identifying and alleviating the durability challenges in membrane-electrode-assembly devices for high-rate CO electrolysis. Nat. Catal. 6, 1042–1051 (2023). 12 Li, J., Wang, Z., McCallum, C. et al. Constraining CO coverage on copper promotes high-efficiency ethylene electroproduction. Nat. Catal. 2, 1124–1131 (2019). 13 Jin, J., Wicks, J., Min, Q. et al. Constrained C2 adsorbate orientation enables CO-to-acetate electroreduction. Nature 617, 724–729 (2023). 14 Ji, Y., Chen, Z., Wei, R. et al. Selective CO-to-acetate electroreduction via intermediate adsorption tuning on ordered Cu–Pd sites. Nat Catal 5, 251–258 (2022). 15 Luc, W., Fu, X., Shi, J. et al. Two-dimensional copper nanosheets for electrochemical reduction of carbon monoxide to acetate. Nat. Catal. 2, 423–430 (2019) 16 Zhu, P., Xia, C., Liu, C.-Y., Jiang, K., Gao, G., Zhang, X., Xia, Y., Lei, Y., Husam, L. N. A., Senftle, T.P. & Wang, H. Direct and continuous generation of pure acetic acid solutions via electrocatalytic carbon monoxide reduction. PNAS 118, 1-10 (2021). 17 Li, J., Che, F., Pang, Y. et al. Copper adparticle enabled selective electrosynthesis of n-propanol. Nat. Commun. 9, 4614 (2018). 18 Pang, Y., Li, J., Wang, Z. et al. Efficient electrocatalytic conversion of carbon monoxide to propanol using fragmented copper. Nat. Catal. 2, 251–258 (2019). 19 Zhuang, T.T., Pang, Y., Liang, Z.Q. et al. Copper nanocavities confine intermediates for efficient electrosynthesis of C3 alcohol fuels from carbon monoxide. Nat Catal 1, 946–951 (2018). 20 Wang, X., Ou, P., Ozden, A. et al. Efficient electrosynthesis of n-propanol from carbon monoxide using a Ag–Ru–Cu catalyst. Nat. Energy 7, 170–176 (2022). 21 Duong, H.P., Rivera de la Cruz, J.G., Tran, N.-H. et al. Silver and Copper Nitride Cooperate for CO Electroreduction to Propanol. Angew. Chem. Int. Ed. 62, e202310788 (2023). 22 Guo, S., Liu, Y., Huang, Y., Wang, H., Murphy, E., Delafontaine, L., Chen, J., Zenyuk, I.V. & Atanassov, P. Promoting Electrolysis of Carbon Monoxide toward Acetate and 1-Propanol in Flow Electrolyzer. ACS Energy Lett. 8, 2, 935–942 (2023). 23 Raciti, D. et al. Low-Overpotential Electroreduction of Carbon Monoxide Using Copper Nanowires. ACS Catal. 7, 4467–4472 (2017). 24 Wang, X., Wang, Z., Zhuang, TT. et al. Efficient upgrading of CO to C3 fuel using asymmetric C-C coupling active sites. Nat. Commun. 10, 5186 (2019). 25 Schouten, K.J.P., Qin, Z., Pérez Gallent, E. & Koper, M.T.M. Two Pathways for the Formation of Ethylene in CO Reduction on Single-Crystal Copper Electrodes. J. Am. Chem. Soc. 134 (24), 9864-9867 (2012). 26 Schouten, K.J.P., Pérez Gallent, E. & Koper, M.T.M. Structure Sensitivity of the Electrochemical Reduction of Carbon Monoxide on Copper Single Crystals. ACS Catalysis 3 (6), 1292-1295 (2013). 27 You, F., Xi, S., Ying Ho, J.J., Calle-Vallejo, F. & Yeo, B.S. Influence of Copper Sites with Different Coordination on the Adsorption and Electroreduction of CO2 and CO. ACS Catalysis 13 (16), 11136-11143 (2023). 28 De Gregorio, G.L., Burdyny, T., Loiudice, A., Iyengar, P., Smith, W.A. & Buonsanti, R. Facet-Dependent Selectivity of Cu Catalysts in Electrochemical CO2 Reduction at Commercially Viable Current Densities. ACS Catalysis 10 (9), 4854-4862 (2020). 29 Bertheussen, E. et al. Acetaldehyde as an Intermediate in the Electroreduction of Carbon Monoxide to Ethanol on Oxide-Derived Copper. Angew. Chem. Int. Ed. 55, 1450 –1454, (2016). 30 Qiao, Y., Hochfilzer, D., Kibsgaard, J., Chorkendorff, I. & Seger, B. Real-Time Detection of Acetaldehyde in Electrochemical CO Reduction on Cu Single Crystals. ACS Energy Letters 9 (3), 880-887, (2024). 31 Nitopi, S., Bertheussen, E., Scott, S.B. et al. Progress and Perspectives of Electrochemical CO2 Reduction on Copper in Aqueous Electrolyte. Chemical Reviews 119 (12), 7610-7672, (2019). 32 da Silva, A.H.M., Lenne, Q., Vos, R.E. & Koper, M.T.M. Competition of CO and Acetaldehyde Adsorption and Reduction on Copper Electrodes and Its Impact on n-Propanol Formation. ACS Catalysis 13 (7), 4339-4347 (2023). 33 Wei, P., Gao, D., Liu, T. et al. Coverage-driven selectivity switch from ethylene to acetate in high-rate CO2/CO electrolysis. Nat. Nanotechnol. 18, 299–306 (2023). 34 Verdaguer-Casadevall, A.; Li, C. W.; Johansson, T. P.; Scott, S. B.; McKeown, J. T.; Kumar, M.; Stephens, I. E.; Kanan, M. W.; Chorkendorff, I. Probing the Active Surface Sites for CO Reduction on Oxide-Derived Copper Electrocatalysts. J. Am. Chem. Soc. 137, 9808– 9811 (2015). 35 Feng, X., Jiang, K., Fan, S. & Kanan, M.W. A Direct Grain-Boundary-Activity Correlation for CO Electroreduction on Cu Nanoparticles. ACS Central Science 2 (3), 169-174 (2016). 36 Rahaman, M., Dutta, A., Zanetti,A. & Broekmann, P. Electrochemical Reduction of CO2 into Multicarbon Alcohols on Activated Cu Mesh Catalysts: An Identical Location (IL) Study. ACS Catalysis 7 (11), 7946-7956 (2017). 37 Mariano, R.G., McKelvey, K., White,H.S., Kanan, M.W. Selective increase in CO2 electroreduction activity at grain-boundary surface terminations. Science 358, 1187–1192 (2017). 38 Khorshidi, A., Violet, J., Hashemi, J. et al. How strain can break the scaling relations of catalysis. Nat Catal 1, 263–268 (2018). 39 Mavrikakis, M., Hammer,B., & Nørskov, J. K., Effect of Strain on the Reactivity of Metal Surfaces. Phys. Rev. Lett. 81, 2819-2822 (1998). 40 Ma, W., Xie, S., Zhang, B. et al. Copper lattice tension boosts full-cell CO electrolysis to multi-carbon olefins and oxygenates. Chem 9, 2161–2177 (2023). 41 Atlan, C., Chatelier, C., Martens, I. et al. Imaging the strain evolution of a platinum nanoparticle under electrochemical control. Nat. Mater. 22, 754–761 (2023). 42 He, T., Wang, W., Shi, F. et al. Mastering the surface strain of platinum catalysts for efficient electrocatalysis. Nature 598, 76–81 (2021). 43 Escudero-Escribano, M., Malacrida, P., Hansen, M.H., Vej-Hansen, U. G., Velázquez-Palenzuela, A., Tripkovic, V., Schiøtz, J., Rossmeisl, J., Stephens, I. E. & Chorkendorff, I. Tuning the activity of Pt alloy electrocatalysts by means of the lanthanide contraction. Science 352, 73–76 (2016). 44 Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 134, 074106 (2011). 45 Cheng, D., Zhao, ZJ., Zhang, G. et al. The nature of active sites for carbon dioxide electroreduction over oxide-derived copper catalysts. Nat Commun 12, 395 (2021). 46 Peng, HJ., Tang, M.T., Halldin Stenlid, J. et al. Trends in oxygenate/hydrocarbon selectivity for electrochemical CO2 reduction to C2 products. Nat Commun 13, 1399 (2022). 47 Strach, M. et al. Insights into Reaction Intermediates to Predict Synthetic Pathways for Shape-Controlled Metal Nanocrystals. J. Am. Chem. Soc. 141, 41, 16312–16322 (2019). 48 Mantella, V. et al. Polymer lamellae as reaction intermediates in the formation of copper nanospheres as evidenced by in situ X-ray studies. Angew. Int. Chem. Ed. Engl. 59, 11627–11633 (2020). 49 https://er-c.org/index.php/software/stem-data-analysis/gpa/ - accessed January 25 (2024). 50 Du, H. GPA – geometrical phase analysis software, (2018). 51 Hÿtch, M. J., Snoeck, E. & Kilaas, R. Ultramicroscopy. 74, 131–146 (1998). 52 B. Ravel & M. Newville, ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT, J. of Synchr. Rad. 12, 537–541 (2005). 53 M. Newville, An Analysis Package For XAFS And Related Spectroscopies. Journal of Physics: Conference Series 430, 012007 (2013). 54 Thompson, A.P., Aktulga, H.M., Berger, R. et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm. 271, 108171-108205 (2022). 55 Singraber, A., Morawietz, T., Behler, J. & Dellago, C. Parallel Multistream Training of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 15 (5), 3075-3092 (2019). 56 Singraber, A., Behler, J. & Dellago, C. Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 15, 3, 1827–1840 (2019). 57 Lian, Z., Dattila, F. & López, N. Stability and lifetime of diffusion-trapped oxygen in oxide-derived copper CO2 reduction electrocatalysts. Nat Catal 7, 401–411 (2024). 58 Martí, C., Blanck, S., Staub, R., Loehlé, S., Michel, C. & Steinmann, S.N. DockOnSurf: A Python Code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces. J. Chem. Info. Mod. 61 (7), 3386-3396 (2021). 59 Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mat. Sci. 6, 1, 15-50 (1996). 60 Kresse, G. & Furthmüller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169, (1996). 61 Perdew, J.P., Burke, K. & Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 78, 1396 (1997). 62 Grimme, S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J. Comput. Chem. 27, 15, 1769-1897, (2006). 63 Bučko, T., Hafner, J., Lebègue, S. & Ángyán, J.G. Improved Description of the Structure of Molecular and Layered Crystals: Ab Initio DFT Calculations with van der Waals Corrections. J. Phys. Chem. A 114, 43, 11814–11824, (2010). 64 Almora-Barrios, N., Carchini, G., Błoński, P. & López, N. Costless Derivation of Dispersion Coefficients for Metal Surfaces. J. Chem. Theory Comput. 10, 11, 5002–5009, (2014). 65 Blöchl, P.E. Projector augmented-wave method. Phys. Rev. B 50, 17953, (1994) 66 Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758, (1999). 67 Monkhorst, H.J. & Pack, J.D. Special points for Brillouin-zone integrations. Phys. Rev. B 13, 5188, (1976). 68 Makov, G. & Payne. M. C. Periodic boundary conditions in ab initio calculations. Phys. Rev. B 51, 4014, (1995). 69 Nørskov, J.K., Rossmeisl, J., Logadottir, A., Lindqvist, L., Kitchin, J.R., Bligaard, T. & Jónsson, H. Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode. J. Phys. Chem. B 108, 46, 17886–17892, (2004). 70 Peterson, A.A., Abild-Pedersen, F., Studt, F., Rossmeisla, J. & Nørskov, J.K. How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels. Energy Environ. Sci. 3, 1311-1315, (2010). 71 Álvarez-Moreno, M. et al. Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform. J. Chem. Inf. Model. 55, 95-103, (2015). Additional Declarations There is NO Competing Interest. Supplementary Files CORRSIFINAL.pdf Colloidal copper nanospheres boost propanol electrosynthesis from CO Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4544481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":314583729,"identity":"09615b9f-5196-46e1-9e03-14bbee0fcdcb","order_by":0,"name":"Min Wang","email":"","orcid":"","institution":"EPFL","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Wang","suffix":""},{"id":314583730,"identity":"2820ebce-a2ed-4bfd-9aad-6610172533e0","order_by":1,"name":"Anna Loiudice","email":"","orcid":"","institution":"EPFL","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Loiudice","suffix":""},{"id":314583731,"identity":"160cc987-8212-4587-80ab-7fff00ce0658","order_by":2,"name":"Enric Ibáñez Alé","email":"","orcid":"","institution":"ICIQ","correspondingAuthor":false,"prefix":"","firstName":"Enric","middleName":"Ibáñez","lastName":"Alé","suffix":""},{"id":314583732,"identity":"f361c559-0f91-47ec-b759-646354d95c6d","order_by":3,"name":"Krishna Kumar","email":"","orcid":"","institution":"EPFL","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"","lastName":"Kumar","suffix":""},{"id":314583733,"identity":"675f9fc1-d219-4058-b5d4-9a0edadf8689","order_by":4,"name":"Dragos Stoian","email":"","orcid":"","institution":"European Synchrotron Radiation Facility","correspondingAuthor":false,"prefix":"","firstName":"Dragos","middleName":"","lastName":"Stoian","suffix":""},{"id":314583734,"identity":"4325d423-a5a5-4c66-85d2-29ebf5419e75","order_by":5,"name":"Zan Lian","email":"","orcid":"","institution":"ICIQ","correspondingAuthor":false,"prefix":"","firstName":"Zan","middleName":"","lastName":"Lian","suffix":""},{"id":314583735,"identity":"d12272a6-4c19-4dec-9cb5-92f04794c215","order_by":6,"name":"Petru Albertini","email":"","orcid":"","institution":"École polytechnique fédérale de Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Petru","middleName":"","lastName":"Albertini","suffix":""},{"id":314583736,"identity":"8d73ef41-7dde-41f8-816f-04ae21b2532e","order_by":7,"name":"Ludovic Zaza","email":"","orcid":"https://orcid.org/0000-0002-8186-6185","institution":"EPFL","correspondingAuthor":false,"prefix":"","firstName":"Ludovic","middleName":"","lastName":"Zaza","suffix":""},{"id":314583737,"identity":"0fa890c1-3167-493a-9638-e65f4de97878","order_by":8,"name":"Jari Leemans","email":"","orcid":"","institution":"EPFL","correspondingAuthor":false,"prefix":"","firstName":"Jari","middleName":"","lastName":"Leemans","suffix":""},{"id":314583738,"identity":"7836419b-8e8c-43f0-9e5f-dadcd129b83f","order_by":9,"name":"Núria López","email":"","orcid":"https://orcid.org/0000-0001-9150-5941","institution":"Institut Català d'Investigacio Quimica","correspondingAuthor":false,"prefix":"","firstName":"Núria","middleName":"","lastName":"López","suffix":""},{"id":314583728,"identity":"4320aa66-4d46-4321-9b14-7fac0ed1b907","order_by":10,"name":"Raffaella Buonsanti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGUlEQVRIie3Rv0vDQBTA8fcIpMtLs14xJP9CgmAVFP8VS8BJdHDpICEhcC52N/9FxrhFAsly7opLpKtDs1Ww4oWKLZIWujncd7sfH3jHAahU/zKsZwCHFgD97kQ1gL2+8yfNuwdgtLqgY+wC7NMuBHS2jQxvCwznY0ZmaJSsyQJn6MR83GQuQe8p7yKW8DG6E4xY3j8fJKLwHjjyl0RIQpdnXYTBVVUbXA6W08GewXNMS+SvBv8kOa3bScwpRosvRs6SBKctuV5wdzNhPsZGKE+XRBu1RMOtZIqxVTLyir5/lPDCT8tRPJjIt+h0sWEw+fz3m2PbriaPzw0PTtKiept9ZK5t9kQnWaWtLzBs/2enWqJSqVSqn74BoCpXb+GV4PkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6592-1869","institution":"École Polytechnique Fédérale de Lausanne","correspondingAuthor":true,"prefix":"","firstName":"Raffaella","middleName":"","lastName":"Buonsanti","suffix":""}],"badges":[],"createdAt":"2024-06-07 07:50:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4544481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4544481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61806434,"identity":"0f44c786-bb3d-46a9-95d0-364f6f11dec1","added_by":"auto","created_at":"2024-08-05 19:21:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":698318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFacet- and size- dependent selectivity of CORR\u003c/strong\u003e. \u003cstrong\u003ea,\u003c/strong\u003e TEM images of the Cu catalysts: Cu-Cube, Cu-Octa, Cu-S80, Cu-S34\u003csub\u003e \u003c/sub\u003eand Cu-S7\u003csub\u003e \u003c/sub\u003efrom left to right. Scale bar is 200 nm. \u003cstrong\u003eb,\u003c/strong\u003e Total FEs for all catalysts\u003cstrong\u003e \u003c/strong\u003eat 100 mA/cm\u003csup\u003e2\u003c/sup\u003e in 0.5 M KOH as electrolyte. The reported values are an average of three independent experiments with error bars indicating the standard deviations (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e). Deviations from 100% FE in the acetate selective samples can be attributed to the difficulty in detecting the acetaldehyde, being a CORR product which transforms into acetate via a rapid non-Faradaic chemical oxidation in alkaline environments.\u003csup\u003e29,30\u003c/sup\u003e \u003cstrong\u003ec\u003c/strong\u003e, Partial current density for ethylene, acetate, ethanol and n-propanol for the tested catalysts in (b). \u003cstrong\u003ed\u003c/strong\u003e, Normalized FE of CORR products at different CO concentration % in an inert N\u003csub\u003e2\u003c/sub\u003e carrier gas at 100 mA/cm\u003csup\u003e2\u003c/sup\u003e in 0.5 M KOH for Cu-Cube, Cu-Octa and Cu-S7. Mass transport effects and increased hydrogen generation dominate for all catalysts below 40% CO (\u003cstrong\u003eSupplementary Fig. 3\u003c/strong\u003e). Continuous lines in c and d, are guides for the eyes. The loading on the gas diffusion electrodes was constant at 100 mg/cm\u003csup\u003e2\u003c/sup\u003e for all the catalysts.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4544481/v1/33e1144eda10fdb903edecff.png"},{"id":61806436,"identity":"e4739a55-f91e-4b58-bdb0-3ed1d774ade1","added_by":"auto","created_at":"2024-08-05 19:21:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":384777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStrain modelling of Cu NCs via NN-MD and strain effect on reactivity towards acetate and alcohols via DFT\u003c/strong\u003e. \u003cstrong\u003ea,\u003c/strong\u003e Final structures for cube, octahedra and sphere models obtained with NN-MD. \u003cstrong\u003eb,\u003c/strong\u003e Average strain of surface Cu atoms for 9 models. Spheres show the highest average strain among the shapes at 5.0 nm and 7.5 nm The decrease in strain with size is more pronounced for the cube and octahedra as their highly strained edges become more significant as the size decreases. \u003cstrong\u003ec,\u003c/strong\u003e Histograms of Cu-Cu bond length for the three 2.5 nm models along the 334 NN-MD steps; bulk Cu-Cu distance is represented by dashed line. The spheres exhibit the wider bond length distribution \u003cstrong\u003ed,\u003c/strong\u003e Scheme of the site extraction and re-building on periodic slabs to generate structures computationally affordable in DFT. These models were created from the last snapshot of the NN-MD simulation by extracting surface regions (12x12x6 Å\u003csup\u003e3\u003c/sup\u003e) around the most abundant surface sites on spheres (\u003cstrong\u003eSupplementary Figs. S13-17)\u003c/strong\u003e and optimizing the corresponding adclusters+Cu(100) using the neural network potential (NN) (\u003cstrong\u003eSupplementary Figs 18-20\u003c/strong\u003e). Cu atoms are in light brown for NCs and extracted sites and in brown for slabs; while C, O, and H atoms are in gray, red and white, respectively. \u003cstrong\u003ee,\u003c/strong\u003e Scheme of the studied reaction pathways leading to acetate (AcO\u003csup\u003e–\u003c/sup\u003e), ethanol (EtOH), and propanol (n-PrOH), following literature.\u003csup\u003e45,46\u003c/sup\u003e \u003cstrong\u003ef,\u003c/strong\u003e Adsorption energies (D\u003cem\u003eE\u003c/em\u003e) of *COCH\u003csub\u003e2\u003c/sub\u003e vs. *OCHCH compared with their energies on Cu(211) steps (black dashed lines) for 30 active sites extracted for the spheres (gray dots, 10 for each size).\u003cstrong\u003e \u003c/strong\u003eA more negative D\u003cem\u003eE\u003c/em\u003e value compared to steps indicates that the intermediate is more stable, favoring the corresponding product. \u003cstrong\u003eg,\u003c/strong\u003e D\u003cem\u003eE\u003c/em\u003e of *OCHCHCOH vs. *OCHCH\u003csub\u003e2\u003c/sub\u003e for the active sites. The intensity in the color bar corresponds to the different sphere sizes. Most of the active sites corresponding to 2.5 nm and 5 nm spheres are in the area where n-PrOH is favored (i.e. more exothermic energies than steps sites towards *OCHCHCOH and similar or weaker binding energy than step sites towards *OCHCH\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4544481/v1/c4681d3aaef3b23af207c36e.png"},{"id":61806435,"identity":"807285f3-ed01-4516-b0f9-a9c1c8e36482","added_by":"auto","created_at":"2024-08-05 19:21:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1018660,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4544481/v1/af1ccb88fcfd897ce5346d51.png"},{"id":61806437,"identity":"ae0303f9-f594-4483-ade7-ea888bc0fe1c","added_by":"auto","created_at":"2024-08-05 19:21:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCORR performance of colloidal Cu nanospheres with diameter of 4.0 nm compared with the literature results. a,\u003c/strong\u003e TEM image and corresponding size histogram of Cu-S4. \u003cstrong\u003eb,\u003c/strong\u003e FE for n-propanol (n-PrOH) in Cu-Sph of different sizes tested\u003cstrong\u003e \u003c/strong\u003eat 100 mA/cm\u003csup\u003e2\u003c/sup\u003e in 0.5 M KOH as electrolyte. The reported values are an average of three independent experiments with error bars indicating the standard deviations. \u003cstrong\u003ec,\u003c/strong\u003e FE and partial current density for n-PrOH of Cu-S4, Cu-Cube, Cu-Octa, Cu-Commercial (Cu-Comm.) tested in this work at an applied current of 300 mA/cm\u003csup\u003e2\u003c/sup\u003e in 1 M KOH compared with the best copper catalyst for n-PrOH from CORR reported to date\u003csup\u003e17\u003c/sup\u003e.\u003cstrong\u003e d,\u003c/strong\u003e Comparison of the n-PrOH to C\u003csub\u003e2\u003c/sub\u003e FE ratio of the same catalysts in \u003cstrong\u003ec\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4544481/v1/fa8aaf008831ca2b943fbee7.png"},{"id":61811773,"identity":"ad1cb45f-3fc0-4bf5-b988-bef137438ac0","added_by":"auto","created_at":"2024-08-05 20:30:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3003830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4544481/v1/29ff9645-8b19-473b-81fd-61049847bf0a.pdf"},{"id":61806773,"identity":"849dadaa-218f-4ecc-8a23-7653636f9d16","added_by":"auto","created_at":"2024-08-05 19:29:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7647707,"visible":true,"origin":"","legend":"Colloidal copper nanospheres boost propanol electrosynthesis from CO","description":"","filename":"CORRSIFINAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4544481/v1/f15b5ea7db57ede8a7414194.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Colloidal copper nanospheres boost propanol electrosynthesis from CO","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePropanol is used in the food industry, in healthcare, in packaging, and is appealing as a transportation fuel additive.\u003csup\u003e1-3\u0026nbsp;\u003c/sup\u003eToday, the manufacturing of this C\u003csub\u003e3\u003c/sub\u003e alcohol is based on the hydrogenation of propionaldehyde, which is produced by hydroformylation of ethylene with a rhodium phosphine catalyst.\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eThis process occurs at high temperature and pressure, is highly energy demanding, relies on cracking and petroleum, and emits greenhouse gases.\u003csup\u003e1-4\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe electrochemical CO reduction reaction (CORR) is an appealing process to produce chemicals at room temperature and ambient pressure while storing excess renewable electricity.\u003csup\u003e5,6\u003c/sup\u003e Technoeconomic analysis and experimental evidences suggest that the two-step electrosynthesis CO\u003csub\u003e2\u003c/sub\u003e to CO followed by CO to multi-carbon products outperform the one-step process in terms of efficiency and stability.\u003csup\u003e2,3,7-11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis attractive scenario has fostered an increase effort to overcome the selectivity challenge of CORR and to improve its efficiency towards one single chemical product.\u003csup\u003e12-24\u003c/sup\u003e The achievement of a significant faradaic efficiency (FE, i.e. electrons converted into chemicals) for C\u003csub\u003e3\u003c/sub\u003e products is particularly interesting.\u003csup\u003e16-24\u003c/sup\u003e However, further performance enhancement is needed to render n-propanol electrosynthesis a valuable process which operates with high selectivity at hundreds of milliamperes per square centimeters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA puzzling scenario currently exists in understanding how the structure of the catalyst impacts the selectivity of CORR under practical reaction conditions (i.e. high current densities). All the catalysts reported to produce n-propanol are Cu-based materials.\u003csup\u003e16-24\u003c/sup\u003e The presence of \u0026ldquo;adparticles\u0026rdquo; with low coordinated sites and combined strain with ligand effects have been used to explain the observed selectivity towards n-propanol in Cu only and in Cu doped with noble metals, respectively.\u003csup\u003e16-24\u003c/sup\u003e Studies on Cu single crystals have shown a structural sensitivity of CORR wherein (111) and (100) facets have a preference to form methane and ethylene, respectively.\u003csup\u003e25,26\u003c/sup\u003e Yet, such facet-dependent relationships have not been demonstrated at high current density. Furthermore, evidences indicate that CO adsorption is not sensitive to certain structural features of the catalyst (i.e. coordination numbers).\u003csup\u003e27\u003c/sup\u003e Overall, solving this puzzle is important to design catalysts which further increase selectivity in CORR, specifically for n-propanol electrosynthesis.\u003c/p\u003e\n\u003cp\u003eHere, we exploit well defined Cu nanocrystals (NCs) with different shapes and sizes to provide a comprehensive mapping of catalyst structure/selectivity relationships in CORR. We reveal that facet-dependent selectivity does exist in CORR under practical reaction conditions. Through the combination of various state of the art experimental and computational data, we propose the lattice strain of spherical Cu NCs as one of the key features to boost the CORR towards alcohols and, more specifically, n-propanol. Based on this idea, we design a catalyst which achieves a production rate for n-propanol which is ten times the current state of the art for copper catalysts. \u0026nbsp;\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003eCu NCs with different shapes and size were synthesized via colloidal methods (\u003cstrong\u003eFig. 1a, Supplementary Fig. 1, Methods\u003c/strong\u003e). Cu cubes (Cu-Cube) and Cu octahedra (Cu-Octa) were chosen because they expose mostly (100) and (111) facets, respectively, whereas Cu sphere (Cu-Sph) exhibit a mixture of both facets on their surface.\u003csup\u003e28\u003c/sup\u003e Cu-Cube with edge length of ~40 nm, Cu-Octa with edge length of ~80 nm and Cu-Sph with diameter of ~80 nm (Cu-S80)\u003csub\u003e\u0026nbsp;\u003c/sub\u003ehave a comparable surface-to-volume ratio (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e). Cu spheres with diameter of ~34 nm (Cu-S34) and of ~7 nm (Cu-S7) were chosen to investigate the effect of size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll catalysts are mostly selective for C\u003csub\u003e2+\u003c/sub\u003e products (\u003cstrong\u003eFig. 1b,c and Supplementary Table 2\u003c/strong\u003e). The low C\u003csub\u003e1\u003c/sub\u003e production is in line with reported results under similar operating conditions.\u003csup\u003e12-24\u003c/sup\u003e The ethylene FE remains constant at ~20% for all catalysts. Cu-Cube\u003csub\u003e\u0026nbsp;\u003c/sub\u003eproduces the highest amount of acetate (FE ~ 40%). Acetate remains the main product for Cu-Octa, yet the selectivity towards this product drops (FE ~26%) and, concomitantly, methane appears (FE ~6%). An intriguing switch in selectivity from acetate to alcohols occurs when moving to the Cu-Sph. Cu-S80 shows high selectivity for C\u003csub\u003e2+\u003c/sub\u003e alcohols (FE ~50%) with n-propanol FE ~20% and ethanol FE ~29%. \u0026nbsp;Alcohols increase further while acetate gets reduced as the size of the spheres decreases, reaching C\u003csub\u003e2+\u003c/sub\u003e alcohols (FE ~57%) with ~23% for n-propanol and ~34% for ethanol for the Cu-S7. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePotential dependent product selectivity does not explain the changes in selectivity as only minor variation of the electrode potential occurs among the different systems (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e, \u003cstrong\u003eSupplementary Fig. 2\u003c/strong\u003e).\u003csup\u003e31\u003c/sup\u003e pH effects were proposed to promote the switch in selectivity from acetate, at high pH, to n-propanol, at lower pH.\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eOur system operates at high pH; furthermore, the testing conditions are the same for all catalysts, thus pH effects are unlikely to play a role. CO coverage was shown to affect the CORR mechanism, and the selectivity of ethylene vs oxygenated (i.e. low CO coverage to ethylene and high CO coverage to oxygenated).\u003csup\u003e12,13,32,33\u003c/sup\u003e To explore eventual CO coverage effects,\u003csup\u003e\u0026nbsp;\u003c/sup\u003ewe tested\u0026nbsp;our well-defined Cu NCs at different CO concentration in an inert N\u003csub\u003e2\u003c/sub\u003e carrier (\u003cstrong\u003eFig. 1d, Supplementary Fig. 3\u003c/strong\u003e). The decrease of acetate at lower CO % in both Cu-Cube and Cu-Octa, is accompanied by the increase of ethylene and methane, respectively. No change of the product distribution is observed for the Cu-S7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith regards to the preferential ethylene selectivity and the CO coverage product dependence/switch to acetate (\u003cstrong\u003eFig. 1, Supplementary Fig. 2,3\u003c/strong\u003e), the behavior of Cu-Cube is consistent with what observed in the literature and is in line with the idea that most polycrystalline copper exposes (100) facets during CORR.\u003csup\u003e12,25,26\u003c/sup\u003e The observation that methane selectivity is higher on Cu-Octa exposing the (111) surface compared to the Cu-Cube also agrees with the structure-dependence observed on single crystal.\u003csup\u003e25,26\u003c/sup\u003e The fact that this structural sensitivity becomes more evident at lower CO coverage and that methane and acetate might share a reaction intermediate on the (111) surface in CORR are new insights.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA mixture of (111) and (100) facets was previously used to explain the propanol selectivity of Cu \u0026ldquo;adparticles\u0026rdquo;.\u003csup\u003e17\u0026nbsp;\u003c/sup\u003eHowever,\u0026nbsp;the insensitivity of the product distribution of the Cu-S7\u003csub\u003e\u0026nbsp;\u003c/sub\u003eto the CO coverage highlights that structural effects other than the exposed facets must be considered to explain the unique nature of their propanol-selective active sites.\u003c/p\u003e\n\u003cp\u003eThe formation of grain boundaries and possible residual oxide were previously proposed as catalyst features accounting for alcohol production in both CO\u003csub\u003e2\u003c/sub\u003eRR and CORR.\u003csup\u003e34-37\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eX-Ray diffraction and electron microscopy reveal that the morphology of the catalysts does not change after the collected CORR data (\u003cstrong\u003eSupplementary Fig. 4\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Note 1\u003c/strong\u003e), thus the grain boundaries forming during operation can be excluded. We also excluded any major impact on selectivity of residual oxidized copper during CORR by performing operando X-ray absorption spectroscopy and other control experiments (\u003cstrong\u003eSupplementary Fig. 5-8\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Note 1\u003c/strong\u003e). Differences in local environment and/or mass transport among the samples can also generate different distribution of multi-carbon products.\u003csup\u003e10\u0026nbsp;\u003c/sup\u003eA similar distribution of all the catalysts through the gas diffusion layer also excluded these factors as a major contributor (\u003cstrong\u003eSupplementary Fig. 9\u003c/strong\u003e). \u0026nbsp;The native ligands used for the synthesis of the catalysts also do not appear to influence the product distribution (\u003cstrong\u003eSupplementary Fig. 10\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eLattice strain can alter the binding energy of adsorbates and, thus, impact selectivity of a given catalytic reaction.\u003csup\u003e38,39\u003c/sup\u003e Convoluted strain and ligand effects were proposed to explain the n-propanol selectivity observed in Cu catalysts doped with Ag and Ag-Ru.\u003csup\u003e20,21\u0026nbsp;\u003c/sup\u003eStrain effect in Cu catalyst have been recently suggested as a mean to lower the activation barrier for CORR.\u003csup\u003e40\u003c/sup\u003e\u0026nbsp; Size and shape dependence of strain in nanoparticles of noble metals more commonly used in catalysis, such as Pt, have been largely explored and different strategies implemented to modulate it, including doping/alloying and support epitaxial interactions.\u003csup\u003e41- 43\u003c/sup\u003e Yet, how shape and size modify the strain in Cu NCs is an under investigated topic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used Neural Network potential\u003csup\u003e44\u003c/sup\u003e and Molecular Dynamics (NN-MD, NVT ensemble) to obtain the average strain of surface atoms from models of Cu cubes, octahedra and sphere at three different sizes (2.5 nm, 5.0 nm, 7.5 nm) (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2a,b\u003c/strong\u003e, Methods and Supplementary Information). The spheres possess the highest average strain among the shapes at 5.0 nm and 7.5 nm (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2b\u003c/strong\u003e), which results in a wider distribution of the Cu-Cu bond length histograms (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2c\u003c/strong\u003e). The same trends were observed when testing effects of surface reconstruction (i.e. randomly removing 5% of total atoms and of surface atoms) and surface oxidation (i.e. replacing the removed 5% Cu atoms with oxygen atoms) (\u003cstrong\u003eSupplementary Figs. 11-17\u003c/strong\u003e), which corroborate the experimental conclusion that possible minor surface reconstruction and oxidation do not explain the observed CORR selectivity.\u003c/p\u003e\n\u003cp\u003eTo investigate how the strain impacts the selectivity, we created computationally affordable active site models from the large NN-MD simulation (\u003cstrong\u003eFig. 2d\u003c/strong\u003e, \u003cstrong\u003eSupplementary Note 2\u003c/strong\u003e and \u003cstrong\u003eSupplementary Figs. 18-20\u003c/strong\u003e). These active site models allowed us to assess\u0026nbsp;the adsorption energies\u0026nbsp;of key reaction intermediates to acetate and to alcohols (\u003cstrong\u003eFig. 2e\u003c/strong\u003e).\u003csup\u003e45,46\u003c/sup\u003e Thus, we compared the adsorption energies of intermediates of competing mechanisms (\u003cstrong\u003eFig. 2f,g\u003c/strong\u003e), on 30 active site models extracted from the spheres (10 for each size) and on Cu(211) step sites, as representative of cubes and octahedra. All the active site models of the spheres show preference for alcohols over acetate compared to cubes and octahedra, which is indicated by the stronger binding towards *OCHCH compared to the step sites (\u003cstrong\u003eFig. 2f\u003c/strong\u003e). Furthermore, 28 out of 30 active sites for the spheres exhibit stronger binding energy for *OCHCHCOH compared to step sites (\u003cstrong\u003eFig. 2g\u003c/strong\u003e). Among these sites, most of those derived from the smaller spheres (5 nm and 2.5 nm) possess a similar (or even weaker) binding energy for *OCHCH\u003csub\u003e2\u003c/sub\u003e than Cu(211), which points at a higher boost of n-propanol selectivity over ethanol selectivity for these sizes which have the highest local strain (\u003cstrong\u003eSupplementary Fig. 21 and Supplementary Table 3\u003c/strong\u003e). In addition, the sites extracted from the spheres show a linear correlation between local strain and d-band center energy (\u003cstrong\u003eSupplementary Fig. 22\u003c/strong\u003e), which indicate that strain impacts both electronic and structural local properties on the active sites.\u003c/p\u003e\n\u003cp\u003eFollowing the insight from the theory, we analyzed eventual strain differences in the as-synthesized catalysts and during operation (\u003cstrong\u003eFig. 3\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Note 3\u003c/strong\u003e). Two-dimensional strain maps based on the HR-TEM images of single particles (\u003cstrong\u003eFig. 3a and Supplementary Fig. 23\u003c/strong\u003e) and X-Ray diffraction at the ensemble level (\u003cstrong\u003eSupplementary Fig. 24\u003c/strong\u003e) for the as-synthesized NCs indicate a higher degree of strain for the Cu-Sph catalysts, with the Cu-S7 showing a higher overall strain (higher density of strained regions in blue color). EXAFS analysis of the as-prepared Cu NCs (\u003cstrong\u003eSupplementary Fig. S25\u003c/strong\u003e) and during CORR (\u003cstrong\u003eFig. 3b-d, Supplementary Fig. S26, Supplementary Table 4\u003c/strong\u003e) agree with this observation. All samples are metallic Cu during operation. A shift of the Cu-Cu bond length (R) is observed in the Cu-Sph compared to Cu-Cube and Cu-Octa (\u003cstrong\u003eFig. 3b\u003c/strong\u003e). The Cu-Cu bond length of Cu-Cube and Cu-Octa closely matches the one of the bulk Cu foil (2.541 \u0026Aring;). A deviation from this value occurs for all the Cu-Sph samples with an elongated bond length of 2.545 \u0026Aring; for Cu-S80 and Cu-S34 and a contracted bond length of 2.533 \u0026Aring; for Cu-S7 (\u003cstrong\u003eFig. 3c\u003c/strong\u003e, \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e). This change results in a bigger relative strain (DR/R) for the Cu-Sph compared to the Cu-Octa and Cu-Cube, with the highest value for Cu-S7 (\u003cstrong\u003eFig. 3d\u003c/strong\u003e). The lowering intensity and increasing broadening of the signal in the k-R Wavelet transformed-EXAFS (WT-EXAFS) contour maps going from Cu-Octa and Cu-Cube to Cu-Sph7 indicates a larger distribution of the Cu-Cu bond length (\u003cstrong\u003eFig. 3e\u003c/strong\u003e), in agreement with the histograms from NN-MD in Fig. 2c.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the differences among the samples are certainly subtle, due to the substantial contribution from the bulk in the size regime of the Cu NCs used in this work. In-situ and operando strain measurements are experimentally challenging, especially for nanomaterials.\u003csup\u003e41\u003c/sup\u003e Yet, the experimental data corroborates the theory simulations pointing at a higher strain in the propanol-selective Cu-Sph catalysts compared to Cu-Cube and Cu-Octa even during CORR.\u003c/p\u003e\n\u003cp\u003eInspired by this new insight, we hypothesized that smaller Cu NCs (i.e. more strained) would exhibit an even higher selectivity for n-propanol. Thus, we designed a synthesis for Cu spheres\u003csub\u003e\u0026nbsp;\u003c/sub\u003ewith a diameter of ~4.0 nm in size (Cu-S4, \u003cstrong\u003eFig. 4a)\u003c/strong\u003e. Cu-S4 exhibited a FE for n-propanol of 34.8\u0026plusmn;1.42% stable for at least 12 hours (\u003cstrong\u003eFig. 4b\u003c/strong\u003e, \u003cstrong\u003eSupplementary Fig. 27\u003c/strong\u003e). This FE for n-propanol is 1.5 times higher compared to Cu-S7 when tested under the same conditions at 100 mA/cm\u003csup\u003e2\u003c/sup\u003e, which corroborates a clear trend of increasing selectivity towards n-propanol as the size decreases (\u003cstrong\u003eFig. 4b\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Table 4)\u003c/strong\u003e. Having excluded a major impact of other factors (i.e. catalyst reconstruction, surface oxidation, potential and mass transport effects), this size-dependent trend strengthens the hypothesis that\u0026nbsp;strain effects are key in boosting n-propanol production in CORR.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eComparison with commercial Cu NCs in a similar size regime indicates a unique behavior of the colloidally synthesized Cu NCs (\u003cstrong\u003eFig. 4b\u003c/strong\u003e, \u003cstrong\u003eSupplementary Fig. 28\u003c/strong\u003e). Strain induced by the synthesis method itself or the wider size distribution mitigating size-dependent effects can both explain the lower n-propanol selectivity of the commercial Cu NCs.\u003c/p\u003e\n\u003cp\u003eThe Cu-S4 exhibit an increase of the FE for n-propanol to 39.6\u0026plusmn;1.4 % with a corresponding partial current density of 118\u0026plusmn;4.2 mA/cm\u003csup\u003e2\u003c/sup\u003e when tested at 300 mA/cm\u003csup\u003e2\u003c/sup\u003e (\u003cstrong\u003eFig. 4c, Supplementary Fig. 27\u003c/strong\u003e). This n-propanol production rate is ten times higher than the best reported Cu catalyst to date\u0026nbsp;(\u003cstrong\u003eFig. 4c\u003c/strong\u003e and \u003cstrong\u003eSupplementary Table 5\u003c/strong\u003e) and superior to some of those reported for Cu doped with noble metals (\u003cstrong\u003eSupplementary Fig. 29\u003c/strong\u003e, and \u003cstrong\u003eSupplementary Table 5)\u003c/strong\u003e. Cu-S4 exhibits also the highest ratio between the FE of n-propanol and of C\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e(\u003cstrong\u003eFig. 4d and Supplementary Fig. 29\u003c/strong\u003e), further indicating that copper with the highest strain can promote the coupling reaction between C\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e towards C\u003csub\u003e3\u003c/sub\u003e compounds. This insight might open new reactivity pathways towards other multi-carbon products in the future.\u003c/p\u003e\n\u003cp\u003eOverall, this work demonstrates that a precise control on the size and morphology of Cu nanostructures enables an enhanced n-propanol electrosynthesis without the use of noble metals. The results highlight the importance of catalyst design and inspire future work in catalyst engineering towards carbon neutral manufacturing of chemicals. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eSynthesis of colloidal Cu NCs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCu NCs were synthesized following reported protocols.\u003csup\u003e47,48\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCu-Cube:\u0026nbsp;Tri-n-octylphosphine oxide\u0026nbsp;(TOPO, 24 mmol, 9.37 g) was mixed with oleylamine (OLAM, 117 ml) in a 3-neck flask and degassed under vacuum at room temperature.\u0026nbsp;Copper(I) bromide\u0026nbsp;(CuBr,\u0026nbsp;5 mmol, 0.71 g) was then added to the solution under N\u003csub\u003e2\u003c/sub\u003e flow. The resulting solution was heated to 260 \u0026deg;C and held at this temperature for 1 h.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCu-S80 and Cu-S34: TOPO was substituted by tri-n-octylphosphine (TOP) in the same synthetic protocol used for the Cu-Cube. For Cu-S34, TOP (0.450 mL) was added together with CuBr (86 mg) and OLAM (7 mL) in a 3-neck flask and the reaction mixture was degassed. The resulting solution was heated to 270 \u0026deg;C and held at this temperature for 1 h. By varying the amounts of the same reagents, Cu-S80 could be obtained. Specifically, TOP (3.5 mL), CuBr (0.92 mg) and OLAM (117 mL) were mixed in a 3-neck flask and the rest of the procedure was kept the same as described for Cu-S34.\u003c/p\u003e\n\u003cp\u003eCu-Octa: TOP (0.450 mL), CuBr (116 mg) and OLAM (15 mL) were added to a 3-neck flask and degassed. The rest of the procedure is the same of the one reported for Cu-S34 and Cu-S80.\u003c/p\u003e\n\u003cp\u003eCu-S7 and Cu-S4: trioctylamine (20 mL) was added to a 3-neck flask and degassed under vacuum at 130 \u0026ordm;C for 1 hour. The flask was then refilled with N\u003csub\u003e2\u003c/sub\u003e gas and cooled to 50 \u0026ordm;C.\u0026nbsp;Tetradecylphosphonic acid\u0026nbsp;(270 mg, 1 mmol) and\u0026nbsp;copper(I) acetate\u0026nbsp;(245 mg, 2 mmol) were added in the flask, and the mixture was heated to 180 \u0026deg;C and held for 30 minutes. After 30 minutes, the mixture was heated to 270 \u0026deg;C and held for 30 min to obtain Cu-S7. The temperature was set to 230 \u0026deg;C to obtain Cu-S4 and the reaction quenched by removing the heating mantle as soon as the set temperature was reached.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional details on the Cu NCs synthesis can be found in the Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eElectrochemical measurements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eElectrodes were prepared by air brushing dispersions of Cu NCs in hexane solvent on gas diffusion layer (GDL) with area of 1.33 cm\u003csup\u003e2\u003c/sup\u003e. Additional details on the electrode preparation are reported in the Supplementary Information. A flow cell electrolyzer was used for the electrocatalytic testing.\u003csup\u003e28\u003c/sup\u003e Ag/AgCl electrode\u0026nbsp;(leak free series, Innovative Instruments, Inc.) were used as the reference electrode, and\u0026nbsp;anionic exchange membrane (Fumasep FAB-PK-130) was interposed between the anolyte and\u0026nbsp;the catholyte compartments, as counter electrode a nickel mesh (Mc Master\u0026ndash;Carr, 100x100\u0026nbsp;mesh size) was used. Chronopotentiometry measurements were performed with a potentiostat Biologic SP-300. A typical experiment was run for around 75 min. A gas chromatograph (GC 8610C, SRI instruments) equipped with a HayeSep D porous polymer column, thermal conductivity detector, and flame ionization detector was used for the analysis of gaseous products. Gas sampling was performed at regular intervals of 10 minutes. High-performance liquid chromatography (HPLC) or NMR were used for the analysis of the liquid products. Further experimental details can be found in the Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMaterial Characterization\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLow resolution TEM images were acquired on a FEI Tecnai-Spirit at 120 kV. The as-synthesized materials were drop-casted on a copper TEM grid (Ted Pella, Inc.) prior to imaging. HRTEM images were acquired on a double Cs-corrected FEI Titan Themis 60 \u0026ndash; 300 operated at 300 kV. GPA was performed in Digital Micrograph version 3.52 using a GPA plugin available at\u0026nbsp;\u003ca href=\"https://er-c.org/index.php/software/stem-data-analysis/gpa/\"\u003ehttps://er-c.org/index.php/software/stem-data-analysis/gpa/\u003c/a\u003e.\u003csup\u003e49,50,51\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eXAS experiments were performed at the Swiss-Norwegian beamlines BM31 at the European Synchrotron Radiation Facility in Grenoble, France. The samples were prepared in the same way of the sample used for the electrocatalytic testing with a loading of 100\u0026nbsp;mg/cm\u003csup\u003e2\u003c/sup\u003e and with a similar cell. Only the gas compartment was modified so that a Kapton foil could be introduced to allow X-ray penetration. The standards Cu\u003csub\u003e2\u003c/sub\u003eO and CuO were prepared by using pressed pellets with the sample diluted in a light matrix such as boron nitride or cellulose to obtain an appropriate thickness. The measurements were carried out in fluorescence mode at an incident angle of approximately 45\u0026deg;. A Si(111) double crystal monochromator was used to condition the beam from the bending magnet source to a size of 3 mm (horizonatal) and 300\u0026nbsp;mm (vertical). Fluorescence X-ray absorption near edge structure (XANES) spectra were acquired using a Vortex single-element silicon drift detector with XIA-Mercury digital electronics and a time resolution of 60 sec per spectrum. Cu foil standard XAS spectrum was collected in transmission using ionization chambers for transmission detection. The resulting XAS data were reduced and normalized using Athena and Larch package.\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComputational details\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNeutral-network potentials are employed in classical molecular dynamics (NN-MD) for Cu NCs of different shapes (cube, octahedra, sphere) and sizes (2.5, 5.0, and 7.6 nm) were performed using Large-scale Atomic/ Molecular Massively Parallel Simulator (LAMMPS) \u0026nbsp; code\u003csup\u003e54\u003c/sup\u003e with the NN interface from a neural network potential package (n2p2).\u003csup\u003e55,56\u003c/sup\u003e The NN-MDs were run for 100 ps for reach equilibrium in the canonical ensemble (NVT) using our constructed Behler-Parrinello-type HDNNP.\u003csup\u003e44,57\u003c/sup\u003e The \u0026nbsp;sites of interest for reactivity study were selected considering coordination number and strain analysis (see Supplementary Information), and cut from NN-MD final structure as a 12x12x6 \u0026Aring;\u003csup\u003e3\u003c/sup\u003e-edge cube with the active site centered on the top facet. They were further placed as adclusters on top of Cu(100) and Cu(111) 3-layer surfaces using DockOnSurf\u003csup\u003e58\u003c/sup\u003e to avoid electronic structure embedding instabilities. DFT simulations were carried out with Vienna Ab Initio Simulation Package (VASP 5.4.4)\u003csup\u003e59,60\u003c/sup\u003e to compute adsorption energies of key intermediates.\u0026nbsp;The exchange functional used was the Perdew-Burke-Emzerhof (PBE).\u003csup\u003e61\u003c/sup\u003e Dispersion was included though the DFT-D2 method\u003csup\u003e62,63\u0026nbsp;\u003c/sup\u003ewith our reparametrized C\u003csub\u003e6\u003c/sub\u003e coefficients for Cu atoms.\u003csup\u003e64\u003c/sup\u003e Inner electrons were represented by Projector Augment Wave (PAW),\u003csup\u003e\u0026nbsp;65,66\u0026nbsp;\u003c/sup\u003ewhile the valence monoelectronic states were expanded as plane waves with a kinetic energy cutoff of 450 eV. In the final slabs, the vacuum along the \u003cem\u003ez\u003c/em\u003e direction was at least 10 \u0026Aring;. We sampled the Brillouin zone by a \u0026Gamma;-centered k-points mesh from the Monkhorst-Pack method\u003csup\u003e67\u003c/sup\u003e with a reciprocal grid size smaller than 0.03\u0026middot;2\u0026pi; \u0026Aring;\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. The slab models were asymmetric, and thus the dipole correction was applied.\u003csup\u003e68\u0026nbsp;\u003c/sup\u003eFor all the investigated systems, structures were relaxed using convergence criteria of 0.03 eV/\u0026Aring;\u0026nbsp;and 10\u003csup\u003e\u0026minus;5\u003c/sup\u003e eV for the ionic and electronic steps, respectively. Reported adsorption energies are obtained using CO\u003csub\u003e2\u003c/sub\u003e(g), H\u003csub\u003e2\u003c/sub\u003e(g), H\u003csub\u003e2\u003c/sub\u003eO(g) and clean surfaces as energy references. The Computational Hydrogen Electrode (CHE) was used for obtaining the relative energy between H\u003csup\u003e+\u003c/sup\u003e and gas-phase H\u003csub\u003e2\u003c/sub\u003e at \u003cem\u003eU\u003c/em\u003e = 0.0 V and pH = 0.\u003csup\u003e69,70\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication was created as part of NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation. E.I.A., Z.L. and N.L. acknowledge financial support from the Spanish Ministry of Science and Innovation (PRE2021-097615, PID2021-122516OB-I00, Severo Ochoa Centre of Excellence CEX2019-000925-S 10.13039/501100011033) and the Barcelona Supercomputing Centre-Mare Nostrum (BSC-RES) for providing generous computational resources. The authors thank Aur\u0026eacute;lien Bornet, Pascal A. Schouwink and Emad Oveisi for their help with the NMR, XRD and TEM measurements, respectively. The staff at the BM28 (XmaS) beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble are acknowledged for their support. Access to the beamline was granted through the ESRF under proposal a311213.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available in the main text or the Supplementary Information. The data of the main text will be available in Zenodo upon acceptance.\u003c/p\u003e\n\u003cp\u003eSupporting DFT datasets are available in ioChem-BD\u003csup\u003e71\u0026nbsp;\u003c/sup\u003eat\u0026nbsp;\u003ca href=\"https://iochem-bd.iciq.es/browse/review-collection/100/66902/50633244bba6473158257e22\" target=\"_blank\"\u003e\u003cbr\u003e\u0026nbsp;https://iochem-bd.iciq.es/browse/review-collection/100/68301/2ee66f560d478ddb190e6e82\u003c/a\u003e. All other raw data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eM.W. and A.L. designed and performed the experiments related to catalyst synthesis and electrocatalysis. A.L performed all the material characterization, both ex-situ and operando. E.I.A, Z.L. and N.L. designed and carried out the theoretical part of the work. K.K. and D.S. provided assistance with the data collection and contributed to the analysis of electron microscopy and XAS, respectively. P.A, L.Z, J.L. helped with ICP, NC synthesis and NMR liquid product analysis respectively and contributed with daily discussions. R. B. conceived the idea and coordinated the project. All the authors contributed to discussions and the writing of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1 \u0026nbsp;Mosselman, C. \u0026amp; Dekker, H. Enthalpies of Formation of N-Alkan-1-Ols. J. Chem. Soc. Faraday Trans. 1 Phys. Chem. Condens. Phases 71 (0), 417\u0026ndash; 424 (1975).\u003c/p\u003e\n\u003cp\u003e2 Jouny, M., Luc, W. \u0026amp; Jiao F. General Techno-Economic Analysis of CO2 Electrolysis Systems. Ind. Eng. Chem. Res. 57 (6), 2165\u0026ndash; 2177 (2018).\u003c/p\u003e\n\u003cp\u003e3 Li, C., Ciston, J. \u0026amp; Kanan, M. Electroreduction of carbon monoxide to liquid fuel on oxide-derived nanocrystalline copper. Nature 508, 504\u0026ndash;507 (2014).\u003c/p\u003e\n\u003cp\u003e4 Klabunde, J., Bischoff, C. \u0026amp; Papa, A. Propanols. In Ullmann\u0026rsquo;s Encyclopedia of Industrial Chemistry; Wiley: 1\u0026ndash; 14, (2018).\u003c/p\u003e\n\u003cp\u003e5 Jouny, M., Luc, W. \u0026amp; Jiao, F. High-rate electroreduction of carbon monoxide to multi-carbon products. Nat. Catal. 1, 748-755 (2018).\u003c/p\u003e\n\u003cp\u003e6 Jouny, M., Hutchings, G.S. \u0026amp; Jiao, F. Carbon monoxide electroreduction as an emerging platform for carbon utilization. Nat. Catal. 2, 1062-1070 (2019).\u003c/p\u003e\n\u003cp\u003e7 Siler, J., Khan, S., Ip, A.H., Schreiber, M.W., Jaffer, S.A., Bobicki, E.R., Dinh, C.-T., \u0026amp; Sargent, E.H. Etylene electrosynthesis: a comparative techno-economic analysis of alkaline vs membrane electrode assembly vs CO2-CO-C2H4. ACS Energy Lett. 6, 3, 997\u0026ndash;1002 (2021).\u003c/p\u003e\n\u003cp\u003e8 Cuellar, R.N.S, Scherer, C., Ka\u0026ccedil;kar, B., Eisenreich, W., Huber, C., Wiesner-Fleischer, K., Fleischer, M., Hinrichsen, O. Two-step electrochemical reduction of CO2 towards multi-carbon products at high current densities. J. CO2 util. 36, 263, 220 (2020).\u003c/p\u003e\n\u003cp\u003e9 Wang, L., Nitopi, S., Wong, A.B., Snider, J.L., Nielander, A.C., Morales-Guio, C.G., Orazov, M., Higgins, D.C., Hahn, C., \u0026amp; Jaramillo, T. Electrochemically converting carbon monoxide to liquid fuels by directing selectivity with electrode surface area. Nat. Catal. 2, 702-708 (2019).\u003c/p\u003e\n\u003cp\u003e10 Kim, J.Y., Sellers, C., Hao, S. et al. Different distributions of multi-carbon products in CO2 and CO electroreduction under practical reaction conditions. Nat. Catal. 6, 1115\u0026ndash;1124 (2023).\u003c/p\u003e\n\u003cp\u003e11 Xu, Q., Garg, S., Moss, A.B. et al. Identifying and alleviating the durability challenges in membrane-electrode-assembly devices for high-rate CO electrolysis. Nat. Catal. 6, 1042\u0026ndash;1051 (2023).\u003c/p\u003e\n\u003cp\u003e12 Li, J., Wang, Z., McCallum, C. et al. Constraining CO coverage on copper promotes high-efficiency ethylene electroproduction. Nat. Catal. 2, 1124\u0026ndash;1131 (2019).\u003c/p\u003e\n\u003cp\u003e13 Jin, J., Wicks, J., Min, Q. et al. Constrained C2 adsorbate orientation enables CO-to-acetate electroreduction. Nature 617, 724\u0026ndash;729 (2023).\u003c/p\u003e\n\u003cp\u003e14 Ji, Y., Chen, Z., Wei, R. et al. Selective CO-to-acetate electroreduction via intermediate adsorption tuning on ordered Cu\u0026ndash;Pd sites. Nat Catal 5, 251\u0026ndash;258 (2022).\u003c/p\u003e\n\u003cp\u003e15 Luc, W., Fu, X., Shi, J. et al. Two-dimensional copper nanosheets for electrochemical reduction of carbon monoxide to acetate. Nat. Catal. 2, 423\u0026ndash;430 (2019)\u003c/p\u003e\n\u003cp\u003e16 Zhu, P., Xia, C., Liu, C.-Y., Jiang, K., Gao, G., Zhang, X., Xia, Y., Lei, Y., Husam, L. N. A., Senftle, T.P. \u0026amp; Wang, H. \u0026nbsp;Direct and continuous generation of pure acetic acid solutions via electrocatalytic carbon monoxide reduction. PNAS 118, 1-10 (2021).\u003c/p\u003e\n\u003cp\u003e17 Li, J., Che, F., Pang, Y. et al. Copper adparticle enabled selective electrosynthesis of n-propanol. Nat. Commun. 9, 4614 (2018).\u003c/p\u003e\n\u003cp\u003e18 Pang, Y., Li, J., Wang, Z. et al. Efficient electrocatalytic conversion of carbon monoxide to propanol using fragmented copper. Nat. Catal. 2, 251\u0026ndash;258 (2019).\u003c/p\u003e\n\u003cp\u003e19 Zhuang, T.T., Pang, Y., Liang, Z.Q. et al. Copper nanocavities confine intermediates for efficient electrosynthesis of C3 alcohol fuels from carbon monoxide. Nat Catal 1, 946\u0026ndash;951 (2018).\u003c/p\u003e\n\u003cp\u003e20 Wang, X., Ou, P., Ozden, A. et al. Efficient electrosynthesis of n-propanol from carbon monoxide using a Ag\u0026ndash;Ru\u0026ndash;Cu catalyst. Nat. Energy 7, 170\u0026ndash;176 (2022).\u003c/p\u003e\n\u003cp\u003e21 Duong, H.P., Rivera de la Cruz, J.G., Tran, N.-H. et al. Silver and Copper Nitride Cooperate for CO Electroreduction to Propanol. Angew. Chem. Int. Ed. \u0026nbsp;62, e202310788 (2023).\u003c/p\u003e\n\u003cp\u003e22 Guo, S., Liu, Y., Huang, Y., Wang, H., Murphy, E., Delafontaine, L., Chen, J., Zenyuk, I.V. \u0026amp; Atanassov, P. Promoting Electrolysis of Carbon Monoxide toward Acetate and 1-Propanol in Flow Electrolyzer. ACS Energy Lett. 8, 2, 935\u0026ndash;942 (2023).\u003c/p\u003e\n\u003cp\u003e23 Raciti, D. et al. Low-Overpotential Electroreduction of Carbon Monoxide Using Copper Nanowires. ACS Catal. 7, 4467\u0026ndash;4472 (2017).\u003c/p\u003e\n\u003cp\u003e24 Wang, X., Wang, Z., Zhuang, TT. et al. Efficient upgrading of CO to C3 fuel using asymmetric C-C coupling active sites. Nat. Commun. 10, 5186 (2019).\u003c/p\u003e\n\u003cp\u003e25 Schouten, K.J.P., Qin, Z., P\u0026eacute;rez Gallent, E. \u0026amp; Koper, M.T.M. Two Pathways for the Formation of Ethylene in CO Reduction on Single-Crystal Copper Electrodes. J. Am. Chem. Soc. 134 (24), 9864-9867 (2012).\u003c/p\u003e\n\u003cp\u003e26 Schouten, K.J.P., P\u0026eacute;rez Gallent, E. \u0026amp; Koper, M.T.M. Structure Sensitivity of the Electrochemical Reduction of Carbon Monoxide on Copper Single Crystals. ACS Catalysis 3 (6), 1292-1295 (2013).\u003c/p\u003e\n\u003cp\u003e27 You, F., Xi, S., Ying Ho, J.J., Calle-Vallejo, F. \u0026amp; Yeo, B.S. Influence of Copper Sites with Different Coordination on the Adsorption and Electroreduction of CO2 and CO. ACS Catalysis 13 (16), 11136-11143 (2023).\u003c/p\u003e\n\u003cp\u003e28 De Gregorio, G.L., Burdyny, T., Loiudice, A., Iyengar, P., Smith, W.A. \u0026amp; Buonsanti, R. Facet-Dependent Selectivity of Cu Catalysts in Electrochemical CO2 Reduction at Commercially Viable Current Densities. ACS Catalysis 10 (9), 4854-4862 (2020).\u003c/p\u003e\n\u003cp\u003e29 Bertheussen, E. et al. Acetaldehyde as an Intermediate in the Electroreduction of Carbon Monoxide to Ethanol on Oxide-Derived Copper. Angew. Chem. Int. Ed. 55, 1450 \u0026ndash;1454, (2016).\u003c/p\u003e\n\u003cp\u003e30 Qiao, Y., Hochfilzer, D., Kibsgaard, J., Chorkendorff, I. \u0026amp; Seger, B. Real-Time Detection of Acetaldehyde in Electrochemical CO Reduction on Cu Single Crystals. ACS Energy Letters 9 (3), 880-887, (2024).\u003c/p\u003e\n\u003cp\u003e31 Nitopi, S., Bertheussen, E., Scott, S.B. et al. Progress and Perspectives of Electrochemical CO2 Reduction on Copper in Aqueous Electrolyte. Chemical Reviews 119 (12), 7610-7672, (2019).\u003c/p\u003e\n\u003cp\u003e32 da Silva, A.H.M., Lenne, Q., Vos, R.E. \u0026amp; Koper, M.T.M. Competition of CO and Acetaldehyde Adsorption and Reduction on Copper Electrodes and Its Impact on n-Propanol Formation. ACS Catalysis 13 (7), 4339-4347 (2023).\u003c/p\u003e\n\u003cp\u003e33 Wei, P., Gao, D., Liu, T. et al. Coverage-driven selectivity switch from ethylene to acetate in high-rate CO2/CO electrolysis. Nat. Nanotechnol. 18, 299\u0026ndash;306 (2023).\u003c/p\u003e\n\u003cp\u003e34 Verdaguer-Casadevall, A.; Li, C. W.; Johansson, T. P.; Scott, S. B.; McKeown, J. T.; Kumar, M.; Stephens, I. E.; Kanan, M. W.; Chorkendorff, I. Probing the Active Surface Sites for CO Reduction on Oxide-Derived Copper Electrocatalysts. J. Am. Chem. Soc. 137, 9808\u0026ndash; 9811 (2015).\u003c/p\u003e\n\u003cp\u003e35 Feng, X., Jiang, K., Fan, S. \u0026amp; Kanan, M.W. A Direct Grain-Boundary-Activity Correlation for CO Electroreduction on Cu Nanoparticles. ACS Central Science 2 (3), 169-174 (2016).\u003c/p\u003e\n\u003cp\u003e36 Rahaman, M., Dutta, A., Zanetti,A. \u0026amp; Broekmann, P. Electrochemical Reduction of CO2 into Multicarbon Alcohols on Activated Cu Mesh Catalysts: An Identical Location (IL) Study. ACS Catalysis 7 (11), 7946-7956 (2017).\u003c/p\u003e\n\u003cp\u003e37 Mariano, R.G., McKelvey, K., White,H.S., Kanan, M.W. Selective increase in CO2 electroreduction activity at grain-boundary surface terminations. Science 358, 1187\u0026ndash;1192 (2017).\u003c/p\u003e\n\u003cp\u003e38 Khorshidi, A., Violet, J., Hashemi, J. et al. How strain can break the scaling relations of catalysis. Nat Catal 1, 263\u0026ndash;268 (2018).\u003c/p\u003e\n\u003cp\u003e39 Mavrikakis, M., Hammer,B., \u0026amp; N\u0026oslash;rskov, J. K., Effect of Strain on the Reactivity of Metal Surfaces. Phys. Rev. Lett. 81, 2819-2822 (1998).\u003c/p\u003e\n\u003cp\u003e40 Ma, W., Xie, S., Zhang, B. et al. Copper lattice tension boosts full-cell CO electrolysis to multi-carbon olefins and oxygenates. Chem 9, 2161\u0026ndash;2177 (2023).\u003c/p\u003e\n\u003cp\u003e41 Atlan, C., Chatelier, C., Martens, I. et al. Imaging the strain evolution of a platinum nanoparticle under electrochemical control. Nat. Mater. 22, 754\u0026ndash;761 (2023).\u003c/p\u003e\n\u003cp\u003e42 He, T., Wang, W., Shi, F. et al. Mastering the surface strain of platinum catalysts for efficient electrocatalysis. Nature 598, 76\u0026ndash;81 (2021).\u003c/p\u003e\n\u003cp\u003e43 Escudero-Escribano, M., Malacrida, P., Hansen, M.H., Vej-Hansen, U. G., Vel\u0026aacute;zquez-Palenzuela, A., Tripkovic, V., Schi\u0026oslash;tz, J., Rossmeisl, J., Stephens, I. E. \u0026amp; Chorkendorff, I. Tuning the activity of Pt alloy electrocatalysts by means of the lanthanide contraction. Science 352, 73\u0026ndash;76 (2016).\u003c/p\u003e\n\u003cp\u003e44 Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 134, 074106 (2011).\u003c/p\u003e\n\u003cp\u003e45 Cheng, D., Zhao, ZJ., Zhang, G. et al. The nature of active sites for carbon dioxide electroreduction over oxide-derived copper catalysts. Nat Commun 12, 395 (2021).\u003c/p\u003e\n\u003cp\u003e46 Peng, HJ., Tang, M.T., Halldin Stenlid, J. et al. Trends in oxygenate/hydrocarbon selectivity for electrochemical CO2 reduction to C2 products. Nat Commun 13, 1399 (2022).\u003c/p\u003e\n\u003cp\u003e47 Strach, M. et al. Insights into Reaction Intermediates to Predict Synthetic Pathways for Shape-Controlled Metal Nanocrystals. J. Am. Chem. Soc. \u0026nbsp;141, 41, 16312\u0026ndash;16322 (2019).\u003c/p\u003e\n\u003cp\u003e48 Mantella, V. et al. Polymer lamellae as reaction intermediates in the formation of copper nanospheres as evidenced by in situ X-ray studies. Angew. Int. Chem. Ed. Engl. 59, 11627\u0026ndash;11633 (2020).\u003c/p\u003e\n\u003cp\u003e49 https://er-c.org/index.php/software/stem-data-analysis/gpa/ - accessed January 25 (2024).\u003c/p\u003e\n\u003cp\u003e50 Du, H. GPA \u0026ndash; geometrical phase analysis software, (2018).\u003c/p\u003e\n\u003cp\u003e51 H\u0026yuml;tch, M. J., Snoeck, E. \u0026amp; Kilaas, R. Ultramicroscopy. 74, 131\u0026ndash;146 (1998).\u003c/p\u003e\n\u003cp\u003e52 B. Ravel \u0026amp; M. Newville, ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT, J. of Synchr. Rad. 12, 537\u0026ndash;541 (2005).\u003c/p\u003e\n\u003cp\u003e53 M. Newville, An Analysis Package For XAFS And Related Spectroscopies. Journal of Physics: Conference Series 430, 012007 (2013).\u003c/p\u003e\n\u003cp\u003e54 Thompson, A.P., Aktulga, H.M., Berger, R. et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm. 271, 108171-108205 (2022).\u003c/p\u003e\n\u003cp\u003e55 Singraber, A., Morawietz, T., Behler, J. \u0026amp; Dellago, C. Parallel Multistream Training of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 15 (5), 3075-3092 (2019).\u003c/p\u003e\n\u003cp\u003e56 Singraber, A., Behler, J. \u0026amp; Dellago, C. Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 15, 3, 1827\u0026ndash;1840 (2019).\u003c/p\u003e\n\u003cp\u003e57 Lian, Z., Dattila, F. \u0026amp; L\u0026oacute;pez, N. Stability and lifetime of diffusion-trapped oxygen in oxide-derived copper CO2 reduction electrocatalysts. Nat Catal 7, 401\u0026ndash;411 (2024).\u003c/p\u003e\n\u003cp\u003e58 Mart\u0026iacute;, C., Blanck, S., Staub, R., Loehl\u0026eacute;, S., Michel, C. \u0026amp; Steinmann, S.N. DockOnSurf: A Python Code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces. J. Chem. Info. Mod. 61 (7), 3386-3396 (2021).\u003c/p\u003e\n\u003cp\u003e59 Kresse, G. \u0026amp; Furthm\u0026uuml;ller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mat. Sci. 6, 1, 15-50 (1996).\u003c/p\u003e\n\u003cp\u003e60 Kresse, G. \u0026amp; Furthm\u0026uuml;ller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169, (1996).\u003c/p\u003e\n\u003cp\u003e61 Perdew, J.P., Burke, K. \u0026amp; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 78, 1396 (1997).\u003c/p\u003e\n\u003cp\u003e62 Grimme, S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J. Comput. Chem. 27, 15, 1769-1897, (2006).\u003c/p\u003e\n\u003cp\u003e63 Bučko, T., Hafner, J., Leb\u0026egrave;gue, S. \u0026amp; \u0026Aacute;ngy\u0026aacute;n, J.G. Improved Description of the Structure of Molecular and Layered Crystals: Ab Initio DFT Calculations with van der Waals Corrections. J. Phys. Chem. A 114, 43, 11814\u0026ndash;11824, (2010).\u003c/p\u003e\n\u003cp\u003e64 Almora-Barrios, N., Carchini, G., Błoński, P. \u0026amp; L\u0026oacute;pez, N. Costless Derivation of Dispersion Coefficients for Metal Surfaces. J. Chem. Theory Comput. 10, 11, 5002\u0026ndash;5009, (2014).\u003c/p\u003e\n\u003cp\u003e65 Bl\u0026ouml;chl, P.E. Projector augmented-wave method. Phys. Rev. B 50, 17953, (1994)\u003c/p\u003e\n\u003cp\u003e66 Kresse, G. \u0026amp; Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758, (1999).\u003c/p\u003e\n\u003cp\u003e67 Monkhorst, H.J. \u0026amp; Pack, J.D. Special points for Brillouin-zone integrations. Phys. Rev. B 13, 5188, (1976).\u003c/p\u003e\n\u003cp\u003e68 Makov, G. \u0026amp; Payne. M. C. Periodic boundary conditions in ab initio calculations. Phys. Rev. B 51, 4014, (1995).\u003c/p\u003e\n\u003cp\u003e69 N\u0026oslash;rskov, J.K., Rossmeisl, J., Logadottir, A., Lindqvist, L., Kitchin, J.R., Bligaard, T. \u0026amp; J\u0026oacute;nsson, H. Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode. J. Phys. Chem. B 108, 46, 17886\u0026ndash;17892, (2004).\u003c/p\u003e\n\u003cp\u003e70 Peterson, A.A., Abild-Pedersen, F., Studt, F., Rossmeisla, J. \u0026amp; N\u0026oslash;rskov, J.K. How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels. Energy Environ. Sci. 3, 1311-1315, (2010).\u003c/p\u003e\n\u003cp\u003e71 \u0026Aacute;lvarez-Moreno, M. et al. Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform. J. Chem. Inf. Model. 55, 95-103, (2015).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4544481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4544481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Achieving a carbon neutral manufacturing of chemicals is imperative to accelerate the transition towards a sustainable future. Propanol electrosynthesis from CO electroreduction represents a promising alternative to the current manufacturing of this chemical. Yet, the catalyst features driving propanol formation are poorly understood, which limits further advancement in the performance. Herein, we report on a comprehensive mapping of the sensitivity of the CO electroreduction to the catalyst structure exploiting well-defined copper nanocrystals (NCs) with tunable shape and size synthesized via colloidal chemistry. In addition to clarify the dependence from the exposed surfaces, we discover that spheres uniquely promote n-propanol selectivity, which we explain mostly with strain effects. Driven by this novel insight, we achieve unprecedent n-propanol production via electrosynthesis with a copper catalyst. We demonstrate that colloidal copper nanospheres with a diameter of 4 nm deliver n-propanol faradaic efficiency of 39.6±1.4% at 119±4.2 mA/cm2 production rate, the latter being ten times the current state of the art for copper catalysts.","manuscriptTitle":"Colloidal copper nanospheres boost propanol electrosynthesis from CO","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 19:21:16","doi":"10.21203/rs.3.rs-4544481/v1","editorialEvents":[],"status":"published","journal":{"display":false,"email":"[email protected]","identity":"nature","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nature","sideBox":"Learn more about [Nature](http://www.nature.com/nature/)","snPcode":"","submissionUrl":"","title":"Nature","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7955e444-4d1f-4916-9331-14754b7fe168","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":33274283,"name":"Physical sciences/Chemistry/Catalysis/Electrocatalysis"},{"id":33274284,"name":"Physical sciences/Chemistry/Chemical synthesis/Nanoparticle synthesis"},{"id":33274285,"name":"Physical sciences/Chemistry/Green chemistry/Sustainability"}],"tags":[],"updatedAt":"2024-08-15T22:15:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 19:21:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4544481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4544481","identity":"rs-4544481","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}