CO Adsorption Energy Research Articles

Hierarchical NiO/Al2O3 nanostructure for highly effective smoke and toxic gases suppression of polymer Materials: Experimental and theoretical investigation

There is still a lack of in-depth investigation of the suppression mechanism for polymeric materials. Herein, based on the density functional theory (DFT) study, the flame retardancy, smoke, and toxic gases suppression mechanisms of hierarchical NiO/Al2O3 nanostructures in thermoplastic polyurethane (TPU) are in-depth revealed. The incorporation of NiO/Al2O3-S into TPU leads to a significant reduction in total smoke release by 41.9%, and peak CO production rate by 47.8% (Cone calorimetry test results). Besides, the NiO/Al2O3-S results in a remarkable decrease of 48% in Dsmax during the smoke density test. Most importantly, based on DFT calculations, incorporating catalysts enhances the adsorption energy of CO, O2, and CO2 on the surface of TPU nanocomposites, thereby facilitating CO oxidation reactions. This is conducive to reducing flue gas emissions and toxicity levels. This work introduces DFT into the flame retardancy research of polymer materials, thus providing reliable guidance for designing highly effective smoke suppressants.

Lattice Tuning of Copper Single‐Crystal Surface for Electrochemical Carbon Monoxide Coupling Reaction: A Density Functional Theory Simulation

AbstractCO is a key intermediate of electrocatalytic CO2 reduction reaction, which determines the product species. Understanding the effect of metal electrode structure on the adsorption ability and reaction activity of CO is helpful for the design of high selective catalysts. Herein, the DFT calculations are used to investigate the relationship between lattice structure and reaction mechanism of CO, with copper electrode of various lattice constants used as catalysts. By analyzing the adsorption and reaction energies of CO at different lattice constants of Cu(111) and Cu(100) surfaces, we found that the CO adsorption ability is proportional to the increase in lattice, and *CO dimerization to *COCO is the main reaction pathway on Cu(100) surface. Furthermore, the forecasted possible reaction mechanism and products suggest that the coupling of *CO and *CHO to *COCHO and then to C2 products is the preferred pathway on Cu(111) surface for the lattice contraction. Alternatively, reduction of *CO to C1 products through *CHO intermediate is more beneficial for the normal and expanded lattice of Cu(111). This work offers an example of the geometrical influence factor on the CO adsorption ability and reaction activity, and helps to understanding the fundamental role of lattice expansion and contraction in catalysis.

Synergistic effect of surface modification and effective interfacial charge transfer over faceted g-C3N4/ZnSe heterojunction to enhance CO2 photoreduction activity

Heterojunction and surface modification are promising methods to boost the photocatalytic CO2 conversion efficiency which help to optimize the redox ability of photocatalysts and enhance the separation efficiency of photogenerated charge carriers. Herein, a novel surface-modified and interfacial heterojunction of g-C3N4 (CN) over a faceted ZnSe (ZS) {CN/ZS} composite was developed to accelerate the transfer of photogenerated electrons (e−) and holes (h+) between the CN and ZS photocatalysts. This technology allows faster transfer of a larger quantity of excited reductive electrons to the surface of ZS with a lower CO2 adsorption energy. Based on photocatalytic CO2 reduction to CO and CH4, 3 mmol CN/ZS composite outperformed all as-prepared photocatalysts including pure CN and ZS, with yielding rates of 439 and 203 umolg−1, respectively, with 88 % CO2 selectivity. The improved photocatalytic activity and selectivity were attributed to enhanced visible-light absorption, improved CO2 activation and adsorption on the surface of the photocatalyst and facilitated charge transfer at the interface. This study offers a novel approach for precisely controlling the direction of photogenerated charge separation by constructing a heterostructure for CO2 photoreduction.

Explainable molecular simulation and machine learning for carbon dioxide adsorption on magnesium oxide

The effects of the adsorption energy of CO2 within MgO at different temperatures were investigated by molecular dynamics simulations and experimentally verified. The adsorption mechanism of CO2 within MgO was discussed and explained qualitatively. The results indicated that the diffusive adsorption of CO2 by MgO was divided into two stages, and the ability of CO2 capture by the cubic MgO performed better than that by spherical MgO. The adsorption of CO2 by the cubic MgO was mainly physical and received the inhibited adsorption behavior at the high-temperature stage (>505 K). Herein, we established a comprehensive dataset of adsorption energies and quantitatively analyzed an adsorption energy prediction model using machine learning techniques. The results demonstrated that Decision Tree Regression (DTR) and K-nearest neighbor (KNN) algorithms offer satisfactory accuracy based on root mean square error (RMSE) and R2 evaluations. This approach enables efficient and precise prediction of adsorption energies without the need for labor-intensive molecular dynamics calculations. Furthermore, we explored the influence of various features (Crystal structure, The number of Mg, The number of CO2, Temperature, Pressure, Volume, and Bond energy) on prediction performance. Lastly, we globally evaluated the relative contributions of each feature across four sets of relatively effective algorithms. This comprehensive analysis enhances our understanding of the adsorption mechanism of magnesium oxide on carbon dioxide and provides valuable insights to guide the design of the next generation of high-performance magnesium oxide materials for carbon capture and storage.

Surface characterization of cerium oxide catalysts using deep learning with infrared spectroscopy of CO

Characterization of material surfaces is crucial for understanding their properties and behavior. In this work, we utilized a deep learning technique, along with infrared (IR) spectrum of CO as a probe molecule, to explore the surface properties of cerium oxide (CeO2) catalysts. Through systematic density functional theory (DFT) investigation of CO-derived adspecies on various CeO2 facets, we obtained an extensive dataset containing vibrational frequencies, intensities, and adsorption energies of CO on CeO2. This dataset was used to synthesize large quantities of complex IR spectra to train deep learning models for predicting surface structures, including the distribution of CeO2 facets, CO-derived adspecies, and binding energies. These models were successful in analyzing experimental IR spectra of CO adsorbed on different types of CeO2, and their predictions were consistent with experimental observations in most cases. This work provides a machine learning approach in understanding the morphology, local environmental arrangement, interaction behavior of probe molecules, and catalytic characteristics of diverse CeO2 materials.

Adsorption of hazardous gases on Cyclo[18]carbon and its analogues

Density functional theory calculations were performed to study the adsorption behavior of hazardous gases (NH3, CO2, CO, and NO) on twelve adsorbents (cyclo[18]carbon (C18) and its analogues Al9N9, B9N9, C6B6N6, C12B3N3, C14B2N2, d-C14B2N2, C16BN, C17Si; cyclo[12]carbon (C12) and its analogues Al6N6 and B6N6). The molecular electrostatic potential (MESP) maps of C18 revealed its polyynic structure with alternating electron-rich and electron-deficient regions. The most negative-valued MESP point (Vmin) indicates that the replacement of carbon atoms of C18/C12 by BN/AlN/Si units increases the electron-rich environment in the molecules. In general, NH3 and CO2 are found to be physisorbed on C18/C12 and its analogues. An important result is that the Vmin of C18/C12 and its analogues is linearly correlated with the NH3 and CO2 adsorption energies. The quantum theory of atoms in molecules (QTAIM) results indicate that the interactions of NH3 and CO2 with C18/C12 and its analogues are non-covalent in nature. In general, CO and NO are found to be chemisorbed on C18/C12 and its analogues. In contrast to the cases of NH3 and CO2 adsorption, the Vmin of C18/C12 and its analogues is generally inversely related to the CO and NO adsorption energies. The QTAIM results indicate the strong covalent character of the bonding for CO/C18, CO/C6B6N6, CO/d-C14B2N2, CO/C12, NO/C18, NO/C17Si, and NO/C12 systems. The adsorption process significantly influenced the chemical potential and/or hardness, especially for CO– and NO-adsorbed C18/C12 and its analogues.

DFT Study of Potential Barriers and Trajectory of CO2 Adsorption/Desorption As Well As Dissociation on Clusters Simulating Fe (100), Fe (110), and Fe (111) Facets

Density functional theory calculations (DFT) were performed to investigate the Fe-facet effect on the CO2 potential barrier as well as trajectory of CO2 adsorption. It was found that potential barrier of CO2 adsorption on Fe (111) is almost absent (~0.01 eV). At the same time, potential barriers of CO2 adsorption on Fe (100) and Fe (110) are 0.10 and 0.26 eV, correspondingly. The most stable configuration of CO2 adsorption on different Fe facets under consideration is CO2 adsorbed on Fe (111) with heat effect –1.16 eV, whereas adsorption energies of CO2 on Fe (100) and Fe (110) are –0.87 and –0.15 eV, correspondingly. Found values are in good agreement with literature data. Most energetically favorable trajectory of the CO2 adsorption passes through 2-fold adsorption site (located near two neighbor Fe atoms) in case of flat Fe facets (100) and (110). Unexpectable tend to spontaneous dissociation of CO2 molecule on desorption stage was found at distance ~2.66 Å above Fe (100) surface. Analysis of electron spin distributions allows one to conclude that dissociation is caused by excitation of CO2 molecule accompanied with rearrangement of the spin density of the both CO2 molecule and surface Fe (100) atoms rather than charge transfer. CO2 dissociation on adsorption stage on Fe (100) facet was not found as well as it was not observed over other Fe facets both on desorption and on adsorption stages.

Lattice strain of Cu nanocrystals modulates CO adsorption energy

Lattice strain of Cu nanocrystals modulates CO adsorption energy

Atomically dispersed Fe sites on TiO2 for boosting photocatalytic CO2 reduction: Enhanced catalytic activity, DFT calculations and mechanistic insight

The design of photocatalysts for the stable and efficient photocatalytic reduction of CO2 without sacrificial agents remains challenging. In this study, Fe atoms were anchored on the surface of TiO2 with atomic-level dispersion using a novel negative-pressure encapsulation and pyrolysis strategy. The photoelectrochemical test results confirmed that the introduction of single Fe atoms accelerated the separation of photogenerated carriers and enhanced the TiO2 utilization rate of visible light. The optimal catalyst with atomically dispersed Fe showed excellent photocatalytic conversion of CO2 to CO (48.2 μmol·g−1·h−1) and CH4 (113.4 μmol·g−1·h−1), whereas the TiO2 system produced only trace amounts of CO (2.7 μmol·g−1·h−1). The increased CO2 adsorption energy and movement of the d-band center toward the Fermi level confirmed that single Fe sites were more favorable for the adsorption of CO2. The differential charge density distribution of CO2 adsorbed on the catalyst surface confirmed the rapid transfer of electrons along the Ti-O-Fe-C path, and the Gibbs free energy calculation further confirmed that the Fe sites were conducive to reducing the energy barrier required for the reaction. In addition, the key intermediate (*COOH) of CO2 conversion to CH4 was detected by in situ diffuse reflectance infrared Fourier transform spectroscopy, and a possible reaction pathway was proposed. This work provides an effective strategy for designing single-atom catalysts that can efficiently reduce CO2 to high-value-added products.

Corn-straw-derived, pyridine-nitrogen-rich NCQDs modified Cu0.05Zn2.95In2S6 promoted directional electrons transfer and boosted adsorption and activation of CO2 for efficient photocatalytic reduction of CO2 to CO

In this study, biomass-derived carbon quantum dots (CQDs) doped with different nitrogen sources were loaded on Cu0.05Zn2.95In2S6 (CZIS) to achieve efficient and highly selective CO2 photocatalytic reduction. Results of various characterizations demonstrated that CZIS@CQDs-T with the highest pyridine nitrogen content (30.4%) exhibited the best light utilization, the most efficient carriers’ separation. In-situ XPS demonstrated that NCQDs were conducive to promoting electron transferred directly from the Zn3In2S6 to NCQDs under light illumination. The CZIS@CQDs-T achieved a CO yield of 70.691 μmol·g−1·h−1 with selectivity of 100%, and the yield was 4.77 times higher than that of the CZIS. CO2-TPD showed that the CZIS@CQDs-T displayed the strongest adsorption capacity for CO2, and a weak adsorption capacity for CO. In-situ FTIR analysis showed COOH* was the key intermediates during CO2 photoreduction process. DFT calculation revealed that the limb-joined pyridine nitrogen of the CZIS had denser charge density when adsorbing CO2, and the adsorption energy of CO2 and desorption energy of CO were significantly enhanced compared with the CZIS. Based on the above analysis, possible pathways and mechanisms of CO2 reduction were inferred. This study provides inspiration for designing efficient photocatalysts for highly selective reduction of CO2.

Insights into influence of CH4 on CO2/N2 injection into bituminous coal matrix with biaxial compression strain in molecular terms

Under the influence of strong mining disturbances in coal mines, the coal gradually deforms, which affects the extraction of coalbed methane (CBM). In this study, the microscopic mechanism of CH4 affecting CO2/N2 injection into a bituminous coal matrix with biaxial compression strain was investigated by analyzing its microporous structure, adsorption isotherm, CH4 content effect, strain effect, adsorption density, intermolecular adsorption potential energy and adsorption energy using grand canonical Monte Carlo simulation, molecular dynamic simulation and density functional theory. The results show that with increasing (negative) compression strain (0.00 → −0.20), the distribution range of the gas adsorption potential energy field decreases, with a deviation order of CO2 > CH4 > N2. Also the Henry constant of CO2 increases while those of CH4 and N2 stabilize. The orders of influence of CH4 content and strain on adsorption are N2 > CO2 > CO2/N2 and CO2 > CO2/N2 > N2, respectively. Additionally, the potential energy field distribution ranges of CO2 and N2 injections decrease with increasing CH4 content. More importantly, CH4 is adsorbed in the Up orientation, while CO2 and N2 are adsorbed parallel to the aromatic groups on the pore surface of coal matrix. Also, pre-adsorbed CH4 increases the negative adsorption energy of CO2 and N2 by 45.37–50.26%, thereby effectively inhibiting their injections. This research lays the foundation for improving CBM recovery efficiency in the deformation zone of CBM reservoirs with realistic exploitation.

Insights into CO2 activation and charge transfer in photocatalytic reduction of CO2 on pure and metal single atom modified TiO2 surfaces

Large amounts of CO2 emissions associated with expanded fossil fuel consumption have caused global warming and energy crisis. The photocatalytic reduction of CO2 is one of the most promising ways to solve these problems. However, some basic issues in CO2 photocatalytic reduction over pure and modified TiO2 surfaces, such as the transfer of photogenerated electrons, CO2 adsorption configuration with excess electrons, the mechanism of CO2 activation, still lack in-depth investigation and analysis. In this work, using density functional theory (DFT) calculations, we systematically calculated the transfer of photogenerated electrons, workfunction, CO2 adsorption energy, charge density difference, density of states (DOS), and CO2 dissociation energy barrier on different pure and metal single atom modified TiO2 surfaces. Based on these results, we further analyzed the effect of photogenerated electrons and metal single atoms on CO2 adsorption and activation, and identified the different behavior of photogenerated electrons on pure and metal single atom modified TiO2 surfaces. Finally, a synergetic effect of photogenerated electrons and loaded metal atoms is proposed, which is crucial in understanding CO2 photocatalytic reduction and designing efficient new photocatalytic materials.

Palladium and Ruthenium Dual‐Single‐Atom Sites on Porous Ionic Polymers for Acetylene Dialkoxycarbonylation: Synergetic Effects Stabilize the Active Site and Increase CO Adsorption

AbstractHeterogeneous single‐metal‐site catalysts usually suffer from poor stability, thereby limiting industrial applications. Dual Pd1−Ru1 single‐atom‐sites supported on porous ionic polymers (Pd1−Ru1/PIPs) were constructed using a wetness impregnation method. The two isolated metal species in the form of a binuclear complex were immobilized on the cationic framework of PIPs through ionic bonds. Compared to the single Pd‐ or Ru‐site catalyst, the dual single‐atom system exhibits higher activity with 98 % acetylene conversion and near 100 % selectivity to dialkoxycarbonylation products, as well as better cycling stability for ten cycles without obvious decay. Based on DFT calculations, it was found that the single‐Ru site exhibited a strong CO adsorption energy of −1.6 eV, leading to an increase in the local CO concentration of the catalyst. Notably, the Pd1−Ru1/PIPs catalyst had a much lower energy barrier of 2.49 eV compared to 3.87 eV of Pd1/PIPs for the rate‐determining step. The synergetic effect between neighboring single sites Pd1 and Ru1 not only enhanced the overall activity, but also stabilized PdII active sites. The discovery of synergetic effects between single sites can deepen our understanding of single‐site catalysts at the molecular level.

Palladium and Ruthenium Dual-Single-Atom Sites on Porous Ionic Polymers for Acetylene Dialkoxycarbonylation: Synergetic Effects Stabilize the Active Site and Increase CO Adsorption.

Heterogeneous single-metal-site catalysts usually suffer from the poor stability, limiting its industrial applications. Herein, the dual-sites Pd1-Ru1 supported on porous ionic polymers (Pd1-Ru1/PIPs) was constructed using wetnessimpregnation method. The two isolated metal species in the form of binuclear complex was immobilized on the cationic framework of PIPs through ionic bonds. Compared to the single-Pd or Ru-site catalyst, it exhibits higher activity with 98% acetylene conversion and near 100% selectivity of dialkoxycarbonylation products as well as better cycling stability for ten times cycles without obvious decay. Based on DFT calculations, it was found that the single-Ru-site exhibited a strong CO adsorption energy of -1.6 eV, leading to an increase in the local CO concentration of the catalyst. Notably, the Pd1-Ru1/PIPs catalyst had a much lower energy barrier of 2.49 eV compared to 3.87 eV of Pd1/PIPs for the rate-determining step. The synergetic effect between neighbouring single-sites Pd1 and Ru1 not only enhanced the overall activity, but also stabilized Pd(II) active sites. The discovery of synergetic effect between single-sites can deepen our understanding of single-site catalysts at the molecular level.

Calcium decorated g-CN nanoporous material for high capacity CO2 capture: A DFT and GCMC simulations study

With the increasing severity of global warming, reducing carbon emissions has become a worldwide concern. To effectively capture carbon dioxide, the development of high capacity storage materials is the current core content. In this study, the ability of metal-embedded CN nanosheets to capture carbon dioxide was systematically evaluated by Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulations. Ca entities can be evenly adsorbed in the center of CN holes and Ca-embedded CN nanosheets can capture six CO2, with a capture amount of 57.52 wt% showed by the computation results. The average adsorption energy of CO2 ranges from −0.204 eV to −0.452 eV. Therefore, the current findings indicate that the Ca@CN monolayers are ideal materials for CO2 capture.

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides.

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.

Substitutional Coinage Metals as Promising Defects for Adsorption and Detection of Gases on MoS2 Monolayers: A Computational Approach.

Defective molybdenum disulfide (MoS2) monolayers (MLs) modified with coinage metal atoms (Cu, Ag and Au) embedded in sulfur vacancies are studied at a dispersion-corrected density functional level. Atmospheric constituents (H2, O2 and N2) and air pollutants (CO and NO), known as secondary greenhouse gases, are adsorbed on up to two atoms embedded into sulfur vacancies in MoS2 MLs. The adsorption energies suggest that the NO (1.44 eV) and CO (1.24 eV) are chemisorbed more strongly than O2 (1.07 eV) and N2 (0.66 eV) on the ML with a cooper atom substituting for a sulfur atom. Therefore, the adsorption of N2 and O2 does not compete with NO or CO adsorption. Besides, NO adsorbed on embedded Cu creates a new level in the band gap. In addition, it was found that the CO molecule could directly react with the pre-adsorbed O2 molecule on a Cu atom, forming the complex OOCO, via the Eley-Rideal reaction mechanism. The adsorption energies of CO, NO and O2 on Au2S2, Cu2S2 and Ag2S2 embedded into two sulfur vacancies were competitive. Charge transference occurs from the defective MoS2 ML to the adsorbed molecules, oxidizing the later ones (NO, CO and O2) since they act as acceptors. The total and projected density of states reveal that a MoS2 ML modified with copper, gold and silver dimers could be used to design electronic or magnetic devices for sensing applications in the adsorption of NO, CO and O2 molecules. Moreover, NO and O2 molecules adsorbed on MoS2-Au2s2 and MoS2-Cu2s2 introduce a transition from metallic to half-metallic behavior for applications in spintronics. These modified monolayers are expected to exhibit chemiresistive behavior, meaning their electrical resistance changes in response to the presence of NO molecules. This property makes them suitable for detecting and measuring NO concentrations. Also, modified materials with half-metal behavior could be beneficial for spintronic devices, particularly those that require spin-polarized currents.

Achieving arbitrary CO2 adsorption energy on χ3 borophene surface by applying electric field or strain: DFT and RSM approaches

The study of χ3 borophene modification was considered finding an applicable technique to achieve more efficient approaches in CO2 capturing. In the framework of density functional theory (DFT), two surface modification methods were described, external electric field or strain application on χ3 borophene, and the effect of them was investigated on CO2 adsorption. All these methods affected the deformation charge density of the surface, which led to an increase in the CO2 binding energy, in the studied range. The turning point in CO2 capturing and structural changes appeared by applying an electric field of −0.030 a.u. strength with −0.48 eV adsorption energy. The uniaxial and biaxial compressive strain resulted in proper adsorption energy of −0.73 to −0.85 eV, although it caused a significant change in the planar structure of the surface. The structural and electronic properties, in addition to the charge density difference analysis, confirmed the chemisorption of CO2 on modified surfaces. Lastly, for the first time, the response surface methodology in the DFT framework was used to evaluate the simultaneous effect of applying an external electric field and strain as well as charge injection on CO2 adsorption. The generated quadratic equation predicts the CO2 adsorption energy with high accuracy as a function of the modification parameters. This result would be very helpful in adjusting the modification parameters to achieve the desired CO2 adsorption energy with lower experimental costs, where the χ3 surface is used for a special application such as CO2 removal or sensing.

Machine Learning Prediction of CO Adsorption Energies and Properties of Layered Alloys Using an Improved Feature Selection Algorithm

Layered alloys are widely studied as designable catalysts. As a surface probe molecule, CO adsorption energy is not only employed to characterize surface properties but also used as the catalytic activity descriptor in various reactions. With the aid of high-throughput computing technology, we calculated CO adsorption energies on 3729layered alloy surfaces. To obtain CO adsorption energies, the d-band center and d-band skewness, and the stability of all the remaining layered alloys (8415) of 23 transition metals, we collected 91 features that do not require time-consuming quantum chemistry calculations (non-QC features) and 40 features from quantum chemistry calculations (QC features). To reduce the feature dimension and overcome overfitting problems, we proposed a modified sequential feature selection (SFS) wrapper method to identify (sub)optimal subsets. Two supervised light gradient boosting machine regression (LGBMR) machine learning (ML) regression models were established using the identified subsets. It is demonstrated that the size of the feature subset converges rapidly, and the performance of the model with size nine is already quite satisfactory. The ML model of the non-QC features outperforms that of QC features. Using the ML models established with non-QC features, we predicted the CO adsorption energies and the electronic structure–properties (d-band center, d-band skewness) and stability of 8415 layered alloys. Based on the four conditions (CO adsorption energy, stability, price, and surface segregation), potential alloy catalysts for CO2 to methanol were screened out of 12144 layered alloys.

Oxygen vacancy regulation of microenviroment of Cu/ZnO catalyst for syngas conversion

Syngas conversion into methanol or ethanol over Cu/ZnO catalyst has attracted great interest due to its low energy consumption and high value-added. Here, syngas conversion over perfect and defective Cu4/ZnO(0001) catalysts were investigated via combining density functional theory (DFT) and microkinetic simulations. Our calculations show a clear change in the reaction mechanism when going from perfect Cu4/ZnO(0001) surface to defective Cu4/ZnO(0001) with 5% oxygen vacancy (Ov) surface. The existence of Ov hinders the formation of methanol by increasing the activation barrier of CHO hydrogenation to CH2O, but promotes the coupling between CHO and CO and the formation of C-C-O precursors that are favorable to form ethanol. In essence, the surface localized microenviroment charge redistribution owing to the presence of Ov adjusts the product selectivity. Detailed Bader charge results for the first time quantitatively identify that Ov promotes the electron transfer between adsorbates and Cu4/ZnO(0001) surface, and then influences their electrostatic interaction and spatial structure, resulting in syngas conversion into different products. Microkinetic simulations show that the adsorption energies of CO and H on the Cu4/ZnO(0001)-5%Ov surface has a notable effect on product selectivity. In addition, we verify that the hydrogenation ability of Cu4/ZnO(0001) is not well with the presence or absence of Ov.