MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.

Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images. These results allude to the possibility that such complex head-and-neck deformation may be beyond the capacity of a single network structure or a homogeneous smoothness regularization. To address the challenge of combined multi-scale musculoskeletal motion and soft tissue deformation in the head-and-neck region, we propose a MUsculo-Skeleton-Aware (MUSA) framework to anatomically guide DL-DIR by leveraging the explicit multiresolution strategy and the inhomogeneous deformation constraints between the bony structures and soft tissue. The proposed method decomposes the complex deformation into a bulk posture change and residual fine deformation. It can accommodate both inter- and intra- subject registration. Our results show that the MUSA framework can consistently improve registration accuracy and, more importantly, the plausibility of deformation for various network architectures. The code will be publicly available at https://github.com/HengjieLiu/DIR-MUSA.

Metric learning guided sinogram denoising for cone beam CT enhancement.

BACKGROUND: Cone beam computed tomography (CBCT) is a widely available modality, but its clinical utility has been limited by low detail conspicuity and quantitative accuracy. Convenient post-reconstruction denoising is subject to back projected patterned residual, but joint denoise-reconstruction is typically computationally expensive and complex. PURPOSE: In this study, we develop and evaluate a novel Metric-learning guided wavelet transform reconstruction (MEGATRON) approach to enhance image domain quality with projection-domain processing. METHODS: Projection domain based processing has the benefit of being simple, efficient, and compatible with various reconstruction toolkit and vendor platforms. However, they also typically show inferior performance in the final reconstructed image, because the denoising goals in projection and image domains do not necessarily align. Motivated by these observations, this work aims to translate the demand for quality enhancement from the quantitative image domain to the more easily operable projection domain. Specifically, the proposed paradigm consists of a metric learning module and a denoising network module. Via metric learning, enhancement objectives on the wavelet encoded sinogram domain data are defined to reflect post-reconstruction image discrepancy. The denoising network maps measured cone-beam projection to its enhanced version, driven by the learnt objective. In doing so, the denoiser operates in the convenient sinogram to sinogram fashion but reflects improvement in reconstructed image as the final goal. Implementation-wise, metric learning was formalized as optimizing the weighted fitting of wavelet subbands, and a res-Unet, which is a Unet structure with residual blocks, was used for denoising. To access quantitative reference, cone-beam projections were simulated using the X-ray based Cancer Imaging Simulation Toolkit (XCIST). In both learning modules, a data set of 123 human thoraxes, which was from Open-Source Imaging Consortium (OSIC) Pulmonary Fibrosis Progression challenge, was used. Reconstructed CBCT thoracic images were compared against ground truth FB and performance was assessed in root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). RESULTS: MEGATRON achieved RMSE in HU value, PSNR, and SSIM were 30.97 ± 4.25, 37.45 ± 1.78, and 93.23 ± 1.62, respectively. These values are on par with reported results from sophisticated physics-driven CBCT enhancement, demonstrating promise and utility of the proposed MEGATRON method. CONCLUSION: We have demonstrated that incorporating the proposed metric learning into sinogram denoising introduces awareness of reconstruction goal and improves final quantitative performance. The proposed approach is compatible with a wide range of denoiser network structures and reconstruction modules, to suit customized need or further improve performance.

Necessity and impact of specialization of large foundation model for medical segmentation tasks.

BACKGROUND: Large foundation models, such as the Segment Anything Model (SAM), have shown remarkable performance in image segmentation tasks. However, the optimal approach to achieve true utility of these models for domain-specific applications, such as medical image segmentation, remains an open question. Recent studies have released a medical version of the foundation model MedSAM by training on vast medical data, who promised SOTA medical segmentation. Independent community inspection and dissection is needed. PURPOSE: Foundation models are developed for general purposes. On the other hand, stable delivery of reliable performance is key to clinical utility. This study aims at elucidating the potential advantage and limitations of landing the foundation models in clinical use by assessing the performance of off-the-shelf medical foundation model MedSAM for the segmentation of anatomical structures in pelvic MR images. We also explore the simple remedies by evaluating the dependency on prompting scheme. Finally, we demonstrate the need and performance gain of further specialized fine-tuning. METHODS: MedSAM and its lightweight version LiteMedSAM were evaluated out-of-the-box on a public MR dataset consisting of 589 pelvic images split 80:20 for training and testing. An nnU-Net model was trained from scratch to serve as a benchmark and to provide bounding box prompts for MedSAM. MedSAM was evaluated using different quality bounding boxes, those derived from ground truth labels, those derived from nnU-Net, and those derived from the former two but with 5-pixel isometric expansion. Lastly, LiteMedSAM was refined on the training set and reevaluated on this task. RESULTS: Out-of-the-box MedSAM and LiteMedSAM both performed poorly across the structure set, especially for disjoint or non-convex structures. Varying prompt with different bounding box inputs had minimal effect. For example, the mean Dice score and mean Hausdorff distances (in mm) for obturator internus using MedSAM and LiteMedSAM were {0.251 ± 0.110, 0.101 ± 0.079} and {34.142 ± 5.196, 33.688 ± 5.306}, respectively. Fine-tuning of LiteMedSAM led to significant performance gain, improving Dice score and Hausdorff distance for the obturator internus to 0.864 ± 0.123 and 5.022 ± 10.684, on par with nnU-Net with no significant difference in evaluation of most structures. All segmentation structures benefited significantly from specialized refinement, at varying improvement margin. CONCLUSION: While our study alludes to the potential of deep learning models like MedSAM and LiteMedSAM for medical segmentation, it highlights the need for specialized refinement and adjudication. Off-the-shelf use of such large foundation models is highly likely to be suboptimal, and specialized fine-tuning is often necessary to achieve clinical desired accuracy and stability.

Operando identification of the oxide path mechanism with different dual-active sites for acidic water oxidation.

The microscopic reaction pathway plays a crucial role in determining the electrochemical performance. However, artificially manipulating the reaction pathway still faces considerable challenges. In this study, we focus on the classical acidic water oxidation based on RuO2 catalysts, which currently face the issues of low activity and poor stability. As a proof-of-concept, we propose a strategy to create local structural symmetry but oxidation-state asymmetric Mn4-δ-O-Ru4+δ active sites by introducing Mn atoms into RuO2 host, thereby switching the reaction pathway from traditional adsorbate evolution mechanism to oxide path mechanism. Through advanced operando synchrotron spectroscopies and density functional theory calculations, we demonstrate the synergistic effect of dual-active metal sites in asymmetric Mn4-δ-O-Ru4+δ microstructure in optimizing the adsorption energy and rate-determining step barrier via an oxide path mechanism. This study highlights the importance of engineering reaction pathways and provides an alternative strategy for promoting acidic water oxidation.

Robust localization of poorly visible tumor in fiducial free stereotactic body radiation therapy.

BACKGROUND AND PURPOSE: Effective respiratory motion management reduces healthy tissue toxicity and ensures sufficient dose delivery to lung cancer cells in pulmonary stereotactic body radiation therapy (SBRT) with high fractional doses. An articulated robotic arm paired with an X-ray imaging system is designed for real-time motion-tracking (RTMT) dose delivery. However, small tumors (<15 mm) or tumors at challenging locations may not be visible in the X-ray images, disqualifying patients with such tumors from RTMT dose delivery unless fiducials are implanted via an invasive procedure. To track these practically invisible lung tumors in SBRT, we hereby develop a deep learning-enabled template-free tracking framework, SAFE Track. METHODS: SAFE Track is a fully supervised framework that trains a generalizable prior for template-free target localization. Two sub-stages are incorporated in SAFE Track, including the initial pretraining on two large-scale medical image datasets (DeepLesion and Node21) followed by fine-tuning on our in-house dataset. A two-stage detector, Faster R-CNN, with a backbone of ResNet50, was selected as our detection network. 94 patients (415 fractions; 40,348 total frames) with low tumor visibility who thus had implanted fiducials were included. The cohort is categorized by the longest dimension of the tumor (<10 mm, 10-15 mm and > 15 mm). The patients were split into training (n = 66) and testing (n = 28) sets. We simulated fiducial-free tumors by removing the fiducials from the X-ray images. We classified the patients into two groups - fiducial implanted inside tumors and implanted outside tumors. To ensure the rigor of our experiment design, we only conducted fiducial removal simulation in training patients and utilized patients with fiducial implanted outside of the tumors for testing. Commercial Xsight Lung Tracking (XLT) and a Deep Match were included for comparison. RESULTS: SAFE Track achieves promising outcomes to as accurate as 1.23±1.32 mm 3D distance in testing patients with tumor size > 15 mm where Deep Match is at 4.75±1.67 mm and XLT is at 12.23±4.58 mm 3D distance. Even for the most challenging tumor size (<10 mm), SAFE Track maintains its robustness at 1.82 plus or minus 1.67 mm 3D distance, where Deep Match is at 5.32 plus or minus 2.32 mm, and XLT is at 24.83±12.95 mm 3D distance. Moreover, SAFE Track can detect some considerably challenging cases where the tumor is almost invisible or overlapped with dense anatomies. CONCLUSION: SAFE Track is a robust, clinically compatible, fiducial-free, and template-free tracking framework that is applicable to patients with small tumors or tumors obscured by overlapped anatomies in SBRT.

A setup for synchrotron infrared microspectroscopy and imaging under magnetic field and low temperature.

Infrared spectroscopy is a powerful spectroscopic technique for investigating the vibrational and electronic states of matter. Temperature and magnetic field provide important methods to manipulate these states by an external field. Recent advancements have underscored the necessity for investigating small samples like two-dimensional materials with high spatial resolution. In this article, we introduce a versatile setup at the synchrotron infrared beamline, which combines synchrotron infrared microspectroscopy and imaging techniques with the application of magnetic fields and low temperature conditions. This setup facilitates infrared microscopic imaging in magnetic fields up to 8 T and temperatures as low as 5 K, offering a distinctive tool for probing the physical properties of materials under magnetic field and temperature manipulation. This is particularly relevant for studying two-dimensional materials, single cells, and other small samples in geoscience and environmental science, as well as multi-component heterogeneous properties in quantum materials, polymer materials, energy materials, etc.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Asymmetric dinitrogen-coordinated nickel single-atomic sites for efficient CO2 electroreduction.

Developing highly efficient, selective and low-overpotential electrocatalysts for carbon dioxide (CO2) reduction is crucial. This study reports an efficient Ni single-atom catalyst coordinated with pyrrolic nitrogen and pyridinic nitrogen for CO2 reduction to carbon monoxide (CO). In flow cell experiments, the catalyst achieves a CO partial current density of 20.1 mA cmgeo-2 at -0.15 V vs. reversible hydrogen electrode (VRHE). It exhibits a high turnover frequency of over 274,000 site-1 h-1 at -1.0 VRHE and maintains high Faradaic efficiency of CO (FECO) exceeding 90% within -0.15 to -0.9 VRHE. Operando synchrotron-based infrared and X-ray absorption spectra, and theoretical calculations reveal that mono CO-adsorbed Ni single sites formed during electrochemical processes contribute to the balance between key intermediates formation and CO desorption, providing insights into the catalyst's origin of catalytic activity. Overall, this work presents a Ni single-atom catalyst with good selectivity and activity for CO2 reduction while shedding light on its underlying mechanism.

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.

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.

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.