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Register a new userEfficient electrochemical reduction of CO2 to CO by soft functional materials
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Electrochemical reduction of CO2 to CO is a promising strategy. However, achieving high Faradaic efficiency with high current density using ILs electrolyte remains a challenge. In this study, the IL N octyltrimethyl 1,2,4 triazole ammonium shows outstanding performance for electrochemical reduction of CO2 to CO on the commercial Ag electrode, and the current density can be up to 50.8 mA cm-2 with a Faradaic efficiency of 90.6%. The current density of CO is much higher than those reported in the ILs electrolyte. In addition, the density functional theory calculation further proved that IL interacts with CO2 to form IL CO2 complex which played a key role in reducing the activation energy of CO2. According to the molecular orbital theory, the electrons obtained from ILs was filled in the anti bonding orbit of the CO2, resulting in reducing the C=O bond energy. This work provides a new strategy to design novel ILs for high efficiency electrochemical reduction of CO2 to CO.
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The search for earth abundant, efficient and stable electrocatalysts that can enable the chemical reduction of CO2 to value-added chemicals and fuels at an industrially relevant scale, is a high priority for the development of a global network of renewable energy conversion and storage systems that can meaningfully impact greenhouse gas induced climate change. Here we introduce a straightforward, low cost, scalable and technologically relevant method to manufacture an all-carbon, electroactive, nitrogen-doped nanoporous carbon-carbon nanotube composite membrane, dubbed HNCM-CNT. The membrane is demonstrated to function as a binder-free, high-performance electrode for the electrocatalytic reduction of CO2 to formate. The Faradaic efficiency for the production of formate is 81%. Furthermore, the robust structural and electrochemical properties of the membrane endow it with excellent long-term stability.
The CO_{2} electro-reduction reaction (CORR) is a promising avenue to convert greenhouse gases into high-value fuels and chemicals, in addition to being an attractive method for storing intermittent renewable energy. Although polycrystalline Cu surfaces have long known to be unique in their capabilities of catalyzing the conversion of CO_{2} to higher-order C1 and C2 fuels, such as hydrocarbons (CH_{4}, C_{2}H_{4} etc.) and alcohols (CH_{3}OH, C_{2}H_{5}OH), product selectivity remains a challenge. In this study, we select three metal catalysts (Pt, Au, Cu) and apply in situ surface enhanced infrared absorption spectroscopy (SEIRAS) and ambient-pressure X-ray photoelectron spectroscopy (APXPS), coupled to density-functional theory (DFT) calculations, to get insight into the reaction pathway for the CORR. We present a comprehensive reaction mechanism for the CORR, and show that the preferential reaction pathway can be rationalized in terms of metal-carbon (M-C) and metal-oxygen (M-O) affinity. We show that the final products are determined by the configuration of the initial intermediates, C-bound and O-bound, which can be obtained from CO_{2} and (H)CO_{3}, respectively. C1 hydrocarbons are produced via OCH_{3, ad} intermediates obtained from O-bound CO_{3, ad} and require a catalyst with relatively high affinity for O-bound intermediates. Additionally, C2 hydrocarbon formation is suggested to result from the C-C coupling between C-bound CO_{ad} and (H)CO_{ad}, which requires an optimal affinity for the C-bound species, so that (H)CO_{ad} can be further reduced without poisoning the catalyst surface. Our findings pave the way towards a design strategy for CORR catalysts with improved selectivity, based on this experimental/theoretical reaction mechanisms that have been identified.
Combining first-principles density functional theory simulations with IR and Raman experiments, we determine the frequency shift of vibrational modes of CO2 when physiadsorbed in the iso-structural metal organic framework materials Mg-MOF74 and Zn-MOF74. Surprisingly, we find that the resulting change in shift is rather different for these two systems and we elucidate possible reasons. We explicitly consider three factors responsible for the frequency shift through physiabsorption, namely (i) the change in the molecule length, (ii) the asymmetric distortion of the CO$_2$ molecule, and (iii) the direct influence of the metal center. The influence of each factor is evaluated separately through different geometry considerations, providing a fundamental understanding of the frequency shifts observed experimentally.
Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By an unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables to make precise predictions for targeted functional materials.
Pairing of $pi$ electronic state structures with functional or metallic atoms makes them possible to engineer physical and chemical properties. Herein, we predict the reorientation of magnetization of Co on hexagonal BN (h-BN) and graphene multilayers. The driving mechanism is the formation of the tetrahedral bonding between sp$^3$ and d orbitals at the interface. More specifically, the intrinsic $pi$-bonding of h-BN and graphene is transformed to sp$^3$ as a result of strong hybridization with metallic $d_{z^2}$ orbital. The different features of these two tetrahedral bondings, sp$^2$ and sp$^3$, are well manifested in charge density and density of states in the vicinity of the interface, along with associated band structure near the $bar{K}$ valley. Our findings provide a novel approach to tailoring magnetism by means of degree of the interlayer hybrid bonds in 2D layered materials.
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