Highly Efficient and Selective Light-Driven Dry Reforming of Methane by a Carbon Exchange Mechanism.

Dry reforming of methane (DRM) is a promising technique for converting greenhouse gases (namely, CH4 and CO2) into syngas. However, traditional thermocatalytic processes require high temperatures and suffer from low selectivity and coke-induced instability. Here, we report high-entropy alloys loaded on SrTiO3 as highly efficient and coke-resistant catalysts for light-driven DRM without a secondary source of heating. This process involves carbon exchange between reactants (i.e., CO2 and CH4) and oxygen exchange between CO2 and the lattice oxygen of supports, during which CO and H2 are gradually produced and released. Such a mechanism deeply suppresses the undesired side reactions such as reverse water-gas shift reaction and methane deep dissociation. Impressively, the optimized CoNiRuRhPd/SrTiO3 catalyst achieves ultrahigh activity (15.6/16.0 mol gmetal-1 h-1 for H2/CO production), long-term stability (∼150 h), and remarkable selectivity (∼0.96). This work opens a new avenue for future energy-efficient industrial applications.

Continuous Flow System for Highly Efficient and Durable Photocatalytic Oxidative Coupling of Methane.

Photocatalytic oxidative coupling of methane (OCM) into value-added industrial chemicals offers an appealing green technique for achieving sustainable development, whereas it encounters double bottlenecks in relatively low methane conversion rate and severe overoxidation. Herein, we engineer a continuous gas flow system to achieve efficient photocatalytic OCM while suppressing overoxidation by synergizing the moderate active oxygen species, surface plasmon-mediated polarization, and multipoint gas intake flow reactor. Particularly, a remarkable CH4 conversion rate of 218.2 µmol h-1 with an excellent selectivity of ∼90% toward C2+ hydrocarbons and a remarkable stability over 240 h is achieved over a designed Au/TiO2 photocatalyst in our continuous gas flow system with a homemade three-dimensional (3D) printed flow reactor. This work provides an informative concept to engineer a high-performance flow system for photocatalytic OCM by synergizing the design of the reactor and photocatalyst to synchronously regulate the mass transfer, activation of reactants, and inhibition of overoxidation.

Constructing Asymmetric Charge Polarized NiCo Prussian Blue Analogue for Promoted Electrocatalytic Methanol to Formate Conversion.

The highly selective electrochemical conversion of methanol to formate is of great significance for various clean energy devices, but understanding the structure-to-property relationship remains unclear. Here, the asymmetric charge polarized NiCo prussian blue analogue (NiCo PBA-100) is reported to exhibit remarkable catalytic performance with high current density (210 mA cm-2 @1.65 V vs RHE) and Faraday efficiency (over 90%). Meanwhile, the hybrid water splitting and Zinc-methanol-battery assembled by NiCo PBA-100 display the promoted performance with decent stability. X-ray absorption spectroscopy (XAS) and operando Raman spectroscopy indicate that the asymmetric charge polarization in NiCo PBA leads to more unoccupied states of Ni and occupied states of Co, thereby facilitating the rapid transformation of the high-active catalytic centers. Density functional theory calculations combining operando Fourier transform infrared spectroscopy demonstrate that the final reconstructed catalyst derived by NiCo PBA-100 exhibits rearranged d band properties along with a lowered energy barrier of the rate-determining step and favors the desired formate production.

Operando Direct Observation of Stable Water-Oxidation Intermediates on Ca2-xIrO4 Nanocrystals for Efficient Acidic Oxygen Evolution.

We report Ca2-xIrO4 nanocrystals exhibit record stability of 300 h continuous operation and high iridium mass activity (248 A gIr-1 at 1.5 VRHE) that is about 62 times that of benchmark IrO2. Lattice-resolution images and surface-sensitive spectroscopies demonstrate the Ir-rich surface layer (evolved from one-dimensional connected edge-sharing [IrO6] octahedrons) with high relative content of Ir5+ sites, which is responsible for the high activity and long-term stability. Combining operando infrared spectroscopy with X-ray absorption spectroscopy, we report the first direct observation of key intermediates absorbing at 946 cm-1 (Ir6+═O site) and absorbing at 870 cm-1 (Ir6+OO- site) on iridium-based oxides electrocatalysts, and further discover the Ir6+═O and Ir6+OO- intermediates are stable even just from 1.3 VRHE. Density functional theory calculations indicate the catalytic activity of Ca2IrO4 is enhanced remarkably after surface Ca leaching, and suggest IrOO- and Ir═O intermediates can be stabilized on positive charged active sites of Ir-rich surface layer.

Breaking Local Charge Symmetry of Iron Single Atoms for Efficient Electrocatalytic Nitrate Reduction to Ammonia.

The electrochemical conversion of nitrate pollutants into value-added ammonia is a feasible way to achieve artificial nitrogen cycle. However, the development of electrocatalytic nitrate-to-ammonia reduction reaction (NO3 - RR) has been hampered by high overpotential and low Faradaic efficiency. Here we develop an iron single-atom catalyst coordinated with nitrogen and phosphorus on hollow carbon polyhedron (denoted as Fe-N/P-C) as a NO3 - RR electrocatalyst. Owing to the tuning effect of phosphorus atoms on breaking local charge symmetry of the single-Fe-atom catalyst, it facilitates the adsorption of nitrate ions and enrichment of some key reaction intermediates during the NO3 - RR process. The Fe-N/P-C catalyst exhibits 90.3 % ammonia Faradaic efficiency with a yield rate of 17980 µg h-1 mgcat -1 , greatly outperforming the reported Fe-based catalysts. Furthermore, operando SR-FTIR spectroscopy measurements reveal the reaction pathway based on key intermediates observed under different applied potentials and reaction durations. Density functional theory calculations demonstrate that the optimized free energy of NO3 - RR intermediates is ascribed to the asymmetric atomic interface configuration, which achieves the optimal electron density distribution. This work demonstrates the critical role of atomic-level precision modulation by heteroatom doping for the NO3 - RR, providing an effective strategy for improving the catalytic performance of single atom catalysts in different electrochemical reactions.

Identifying an Interfacial Stabilizer for Regeneration-Free 300 h Electrochemical CO2 Reduction to C2 Products.

The electrochemical CO2 reduction reaction (CO2RR) to produce high value-added hydrocarbons and oxygenates presents a sustainable and compelling approach toward a carbon-neutral society. However, uncontrollable migration of active sites during the electrochemical CO2RR limits its catalytic ability to simultaneously achieve high C2 selectivity and ultradurability. Here, we demonstrate that the generated interfacial CuAlO2 species can efficiently stabilize the highly active sites over the Cu-CuAlO2-Al2O3 catalyst under harsh electrochemical conditions without active sites regeneration for a long-term test. We show that this unique Cu-CuAlO2-Al2O3 catalyst exhibits ultradurable electrochemical CO2RR performance with an 85% C2 Faradaic efficiency for a 300 h test. Such a simple interfacial engineering design approach unveiled in this work would be adaptable to develop various ultradurable catalysts for industrial-scale electrochemical CO2RR.

Reversible Al Metal Anodes Enabled by Amorphization for Aqueous Aluminum Batteries.

Aqueous aluminum metal batteries (AMBs) are regarded as one of the most sustainable energy storage systems among post-lithium-ion candidates, which is attributable to their highest theoretical volumetric capacity, inherent safe operation, and low cost. Yet, the development of aqueous AMBs is plagued by the incapable aluminum plating in an aqueous solution and severe parasitic reactions, which results in the limited discharge voltage, thus making the development of aqueous AMBs unsuccessful so far. Here, we demonstrate that amorphization is an effective strategy to tackle these critical issues of a metallic Al anode by shifting the reduction potential for Al deposition. The amorphous aluminum (a-Al) interfacial layer is triggered by an in situ lithium-ion alloying/dealloying process on a metallic Al substrate with low strength. Unveiled by experimental and theoretical investigations, the amorphous structure greatly lowers the Al nucleation energy barrier, which forces the Al deposition competitive to the electron-stealing hydrogen evolution reaction (HER). Simultaneously, the inhibited HER mitigates the passivation, promoting interfacial ion transfer kinetics and enabling steady aluminum plating/stripping for 800 h in the symmetric cell. The resultant multiple full cells using Al@a-Al anodes deliver approximately a 0.6 V increase in the discharge voltage plateau compared to that of bare Al-based cells, which far outperform all reported aqueous AMBs. In both symmetric cells and full cells, the excellent electrochemical performances are achieved in a noncorrosive, low-cost, and fluorine-free Al2(SO4)3 electrolyte, which is ecofriendly and can be easily adapted for sustainable large-scale applications. This work brings an intriguing picture of the design of metallic anodes for reversible and high-voltage AMBs.

Well-Defined Cobalt Catalyst with N-Doped Carbon Layers Enwrapping: The Correlation between Surface Atomic Structure and Electrocatalytic Property.

Admittedly, the surface atomic structure of heterogenous catalysts toward the electrochemical oxygen reduction reaction (ORR) are accepted as the important features that can tune catalytic activity and even catalytic pathway. Herein, a surface engineering strategy to controllably synthesize a carbon-layer-wrapped cobalt-catalyst from 2D cobalt-based metal-organic frameworks is elaborately demonstrated. Combined with synchrotron radiation X-ray photoelectron spectroscopy, the soft X-ray absorption near-edge structure results confirmed that rich covalent interfacial CoNC bonds are efficiently formed between cobalt nanoparticles and wrapped carbon-layers during the polydopamine-assisted pyrolysis process. The X-ray absorption fine structure and corresponding extended X-ray absorption fine structure spectra further reveal that the wrapped cobalt with Co-N coordinations shows distinct surface distortion and atomic environmental change of Co-based active sites. In contrast to the control sample without coating layers, the 800 °C-annealed cobalt catalyst with N-doped carbon layers enwrapping achieves significantly enhanced ORR activity with onset and half-wave potentials of 0.923 and 0.816 V (vs reversible hydrogen electrode), highlighting the important correlation between surface atomic structure and catalytic property.

Ferromagnetism in CVT grown tungsten diselenide single crystals with nickel doping.

Two dimensional (2D) single crystal layered transition materials have had extensive consideration owing to their interesting magnetic properties, originating from their lattices and strong spin-orbit coupling, which make them of vital importance for spintronic applications. Herein, we present synthesis of a highly crystalline tungsten diselenide layered single crystal grown by chemical vapor transport technique and doped with nickel (Ni) to tailor its magnetic properties. The pristine WSe2 single crystal and Ni-doped crystal were characterized and analyzed for magnetic properties using both experimental and computational aspects. It was found that the magnetic behavior of the 2D layered WSe2 crystal changed from diamagnetic to ferromagnetic after Ni-doping at all tested temperatures. Moreover, first principle density functional theory (DFT) calculations further confirmed the origin of room temperature ferromagnetism of Ni-doped WSe2, where the d-orbitals of the doped Ni atom promoted the spin moment and thus largely contributed to the magnetism change in the 2D layered material.

Designing hierarchical hollow nanostructures of Cu2MoS4 for improved hydrogen evolution reaction.

Layered Cu2MoS4, consisting of earth-abundant elements, is regarded as a potential catalyst for the hydrogen evolution reaction (HER). Herein, we demonstrate a Cu2O-based template strategy to synthesise hierarchical hollow nanostructures of Cu2MoS4. The characterizations reveal that the electrochemically active surface of the hollow Cu2MoS4 is largely enhanced, in contrast to the nanosheet or nanoparticle structures. As the direct outcome, the designed hierarchical hollow structures display excellent HER activities with a low overvoltage and small Tafel slope. This study may provide new inspiration for the research of other ternary sulphide materials as well as subsequently accelerating their applications in the field of catalysis.

Growing and Etching MoS2 on Carbon Nanotube Film for Enhanced Electrochemical Performance.

In this work we directly synthesized molybdenum disulfide (MoS2) nanosheets on carbon nanotube film (MoS2@CNT) via a two-step chemical vapor deposition method (CVD). By etching the obtained MoS2@CNT into 10% wt HNO3, the morphology of MoS2 decorated on CNT bundles was modulated, resulting in more catalytic active MoS2 edges being exposed for significantly enhanced electrochemical performance. Our results revealed that an 8 h acid etching sample exhibited the best performance for the oxygen evolution reaction, i.e., the current density reached 10 mA/cm² under 375 mV over-potential, and the tafel slope was as low as 94 mV/dec. The enhanced behavior was mainly originated from the more catalytic sites in MoS2 induced by the acid etching treatment and the higher conductivity from the supporting CNT films. Our study provides a new route to produce two-dimensional layers on CNT films with tunable morphology, and thus may open a window for exploring its promising applications in the fields of catalytic-, electronic-, and electrochemical-related fields.

High-Throughput Screening of Sulfur Reduction Reaction Catalysts Utilizing Electronic Fingerprint Similarity.

The catalytic performance is determined by the electronic structure near the Fermi level. This study presents an effective and simple screening descriptor, i.e., the one-dimensional density of states (1D-DOS) fingerprint similarity, to identify potential catalysts for the sulfur reduction reaction (SRR) in lithium-sulfur batteries. The Δ1D-DOS in relation to the benchmark W2CS2 was calculated. This method effectively distinguishes and identifies 30 potential candidates for the SRR from 420 types of MXenes. Further analysis of the Gibbs free energy profiles reveals that MXene candidates exhibit promising thermodynamic properties for SRR, with the protocol achieving an accuracy rate exceeding 93%. Based on the crystal orbital Hamilton population (COHP) and differential charge analysis, it is confirmed that the Δ1D-DOS could effectively differentiate the interaction between MXenes and lithium polysulfide (LiPS) intermediates. This study underscores the importance of the electronic fingerprint in catalytic performance and thus may pave a new way for future high-throughput material screening for energy storage applications.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm - Deep Match. METHOD: Deep Match consists of four self-definable stages - training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10-15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). RESULTS: On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. CONCLUSION: Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Dosimetric predictors for genitourinary toxicity in MR-guided stereotactic body radiation therapy (SBRT): Substructure with fraction-wise analysis.

BACKGROUND: MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients. PURPOSE: (1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the trigone and urethra. METHODS: Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall, trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set. RESULTS: IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy), trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative trigone 15.9  and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance. CONCLUSION: Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.

Paired conditional generative adversarial network for highly accelerated liver 4D MRI.

Purpose.4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.Methods.Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3×, 6× and 10×) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer (RST), were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D +t) as temporal slices (2D +t). A total of 48 patients with 12 332 temporal slices were split into training (37 patients with 10 721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results.Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15, 0.16, and 120 s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3× Re-Con-GAN 80.98%; 3× CS 80.74%; 3× UNet 79.88%) of unprocessed under-sampled images (3× 69.61%).Conclusion.A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

Efficient Thermal Management with Selective Metamaterial Absorber for Boosting Photothermal CO2 Hydrogenation under Sunlight.

Photothermal catalytic CO2 hydrogenation is a prospective strategy to simultaneously reduce CO2 emission and generate value-added fuels. However, the demand of extremely intense light hinders its development in practical applications. Herein, this work reports the novel design of Ni-based selective metamaterial absorber and employs it as the photothermal catalyst for CO2 hydrogenation. The selective absorption property reduces the heat loss caused by radiation while possessing effectively solar absorption, thus substantially increasing local photothermal temperature. Notably, the enhancement of local electric field by plasmon resonance promotes the adsorption and activation of reactants. Moreover, benefiting from the ingenious morphology that Ni nanoparticles (NPs) are encapsulated by SiO2 matrix through co-sputtering, the greatly improved dispersion of Ni NPs enables enhancing the contact with reaction gas and preventing the agglomeration. Consequently, the catalyst exhibits an unprecedented CO2 conversion rate of 516.9 mmol gcat -1 h-1 under 0.8 W cm-2 irradiation, with near 90% CO selectivity and high stability. Significantly, this designed photothermal catalyst demonstrates the great potential in practical applications under sunlight. This work provides new sights for designing high-performance photothermal catalysts by thermal management.

Pyrolysis-free synthesis of a high-loading single-atom Cu catalyst for efficient electrocatalytic CO2-to-CH4 conversion.

Electrocatalytic CO2-to-CH4 conversion provides a promising means of addressing current carbon resource recycling and intermittent energy storage. Cu-based single-atom catalysts have attracted extensive attention owing to their high intrinsic activity toward CH4 production; however, they suffer from uncontrollable metal loading and aggregation during the conventional pyrolysis process of carbon-based substrates. Herein, we developed a pyrolysis-free method to prepare a single-atom Cu catalyst anchored on a formamide polymer substrate with a high loading amount and well atomic dispersion through a mild polycondensation reaction. Owing to the isolation of copper active sites, efficient CO2-to-CH4 conversion is achieved over the single-atom Cu catalyst, along with the significant suppression of C-C coupling. As a result, the optimal single-atom catalyst with 5.87 wt% of Cu offers high CH4 faradaic efficiencies (FEs) of over 70% in a wide current density range from 100 to 600 mA cm-2 in the flow cell, together with a maximum CH4 partial current density of 415.8 mA cm-2. Moreover, the CH4 FE can reach 74.2% under optimized conditions in a membrane electrode assembly electrolyzer. This work provides new insights into the subtle design of highly efficient electrocatalyst for CO2 reduction.

Light-driven flow synthesis of acetic acid from methane with chemical looping.

Oxidative carbonylation of methane is an appealing approach to the synthesis of acetic acid but is limited by the demand for additional reagents. Here, we report a direct synthesis of CH3COOH solely from CH4 via photochemical conversion without additional reagents. This is made possible through the construction of the PdO/Pd-WO3 heterointerface nanocomposite containing active sites for CH4 activation and C-C coupling. In situ characterizations reveal that CH4 is dissociated into methyl groups on Pd sites while oxygen from PdO is the responsible for carbonyl formation. The cascade reaction between the methyl and carbonyl groups generates an acetyl precursor which is subsequently converted to CH3COOH. Remarkably, a production rate of 1.5 mmol gPd-1 h-1 and selectivity of 91.6% toward CH3COOH is achieved in a photochemical flow reactor. This work provides insights into intermediate control via material design, and opens an avenue to conversion of CH4 to oxygenates.

Pushing the Performance Limit of Cu/CeO2 Catalyst in CO2 Electroreduction: A Cluster Model Study for Loading Single Atoms.

Pushing the performance limit of catalysts is a major goal of CO2 electroreduction toward practical application. A single-atom catalyst is recognized as a solution for achieving this goal, which is, however, a double-edged sword considering the limited loading amount and stability of single-atom sites. To overcome the limit, the loading of single atoms on supports should be well addressed, requiring a suitable model system. Herein, we report the model system of an ultrasmall CeO2 cluster (2.4 nm) with an atomic precise structure and a high surface-to-volume ratio for loading Cu single atoms. The combination of multiple characterizations and theoretical calculations reveals the loading location and limit of Cu single atoms on CeO2 clusters, determining an optimal configuration for CO2 electroreduction. The optimal catalyst achieves a maximum Faradaic efficiency (FE) of 67% and a maximum partial current density of -364 mA/cm2 for CH4, and can maintain high CH4 FE values over 50% in a wide range of applied current densities (-50 ∼ -600 mA/cm2), exceeding those of the reported catalysts.

Promoting Oxygen Reduction Reaction by Inducing Out-of-Plane Polarization in a Metal Phthalocyanine Catalyst.

Metal phthalocyanine (MPc) material with a well-defined MN4 moiety offers a platform for catalyzing the oxygen reduction reaction (ORR), while the practical performance is often limited by the insufficient O2 adsorption due to the planar MN4 configuration. Here, a design (called Gr-MG -O-MP Pc) is proposed, where the metal of MPc (MP ) is axially coordinated to a single metal atom in graphene (Gr-MG ) through a bridge-bonded oxygen atom (O), introducing effective out-of-plane polarization to promote O2 adsorption on MPc. Manipulating the out-of-plane polarization charge by varying types of MP and MG (MP  = Fe/Co/Ni, MG  = Ti/V/Cr/Mn/Fe/Co/Ni) in the axial coordination zone of -MG -O-MP - are examined by density functional theory simulations. Among them, the catalyst of Gr-V-O-FePc stands out with the highest calculated O2 adsorption energy, which is synthesized successfully and verified by systemic X-ray absorption spectroscopy measurements. Importantly, it delivers a remarkable ORR performance with half-wave potential of 0.925 V (versus reversible hydrogen electrode) and kinetic current density of 26.7 mA cm-2 . This thus demonstrates a new and simple way to pursue high catalytic performance by inducing out-of-plane polarization in catalysts.