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A unique prospect of using halides as charge carriers is the possibility of the halides undergoing anodic redox behaviors when serving as charge carriers for the charge-neutrality compensation of electrodes. However, the anodic conversion of halides to neutral halogen species has often been irreversible at room temperature due to the emergence of diatomic halogen gaseous products. Here, we report that chloride ions can be reversibly converted to near-neutral atomic chlorine species in the Mn3O4 electrode at room temperature in a highly concentrated chloride-based aqueous electrolyte. Notably, the Zn2+ cations inserted in the first discharge and trapped in the Mn3O4 structure create an environment to stabilize the converted chlorine atoms within the structure. Characterization results suggest that the Cl/Cl- redox is responsible for the observed large capacity, as the oxidation state of Mn barely changes upon charging. Computation results corroborate that the converted chlorine species exist as polychloride monoanions, e.g., [Cl3]- and [Cl5]-, inside the Zn2+-trapped Mn3O4, and the presence of polychloride species is confirmed experimentally. Our results point to the halogen plating inside electrode lattices as a new charge-storage mechanism.

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn3O4, MnO, Mn2O3, and MnO2. The accurate decomposition of Mn3O4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

Cubic bi-magnetic hard-soft core-shell nanoarchitectures were prepared starting from cobalt ferrite nanoparticles, prevalently with cubic shape, as seeds to grow a manganese ferrite shell. The combined use of direct (nanoscale chemical mapping via STEM-EDX) and indirect (DC magnetometry) tools was adopted to verify the formation of the heterostructures at the nanoscale and bulk level, respectively. The results showed the obtainment of core-shell NPs (CoFe2O4@MnFe2O4) with a thin shell (heterogenous nucleation). In addition, manganese ferrite was found to homogeneously nucleate to form a secondary nanoparticle population (homogenous nucleation). This study shed light on the competitive formation mechanism of homogenous and heterogenous nucleation, suggesting the existence of a critical size, beyond which, phase separation occurs and seeds are no longer available in the reaction medium for heterogenous nucleation. These findings may allow one to tailor the synthesis process in order to achieve better control of the materials' features affecting the magnetic behaviour, and consequently, the performances as heat mediators or components for data storage devices.

Electrochemical conversion of CO2 into formate is a promising strategy for mitigating the energy and environmental crisis, but simultaneously achieving high selectivity and activity of electrocatalysts remains challenging. Here, we report low-dimensional SnO2 quantum dots chemically coupled with ultrathin Ti3C2Tx MXene nanosheets (SnO2/MXene) that boost the CO2 conversion. The coupling structure is well visualized and verified by high-resolution electron tomography together with nanoscale scanning transmission X-ray microscopy and ptychography imaging. The catalyst achieves a large partial current density of -57.8 mA cm-2 and high Faradaic efficiency of 94% for formate formation. Additionally, the SnO2/MXene cathode shows excellent Zn-CO2 battery performance, with a maximum power density of 4.28 mW cm-2, an open-circuit voltage of 0.83 V, and superior rechargeability of 60 h. In situ X-ray absorption spectroscopy analysis and first-principles calculations reveal that this remarkable performance is attributed to the unique and stable structure of the SnO2/MXene, which can significantly reduce the reaction energy of CO2 hydrogenation to formate by increasing the surface coverage of adsorbed hydrogen.

Anion exchange membrane fuel cells are limited by the slow kinetics of alkaline hydrogen oxidation reaction (HOR). Here, we establish HOR catalytic activities of single-atom and diatomic sites as a function of *H and *OH binding energies to screen the optimal active sites for the HOR. As a result, the Ru-Ni diatomic one is identified as the best active center. Guided by the theoretical finding, we subsequently synthesize a catalyst with Ru-Ni diatomic sites supported on N-doped porous carbon, which exhibits excellent catalytic activity, CO tolerance, and stability for alkaline HOR and is also superior to single-site counterparts. In situ scanning electrochemical microscopy study validates the HOR activity resulting from the Ru-Ni diatomic sites. Furthermore, in situ x-ray absorption spectroscopy and computational studies unveil a synergistic interaction between Ru and Ni to promote the molecular H2 dissociation and strengthen OH adsorption at the diatomic sites, and thus enhance the kinetics of HOR.

All-solid-state sodium batteries (ASSSBs) are promising candidates for grid-scale energy storage. However, there are no commercialized ASSSBs yet, in part due to the lack of a low-cost, simple-to-fabricate solid electrolyte (SE) with electrochemical stability towards Na metal. In this work, we report a family of oxysulfide glass SEs (Na3PS4-xOx, where 0

Photonic crystals are extensively explored to replace inorganic pigments and organic dyes as coloring elements in printing, painting, sensing, and anti-counterfeiting due to their brilliant structural colors, chemical stability, and environmental friendliness. However, most existing photonic-crystal-based pigments can only display monochromatic colors once made, and generating multicolors has to start with designing different building blocks. Here, a novel photonic pigment featuring highly tunable structural colors in the entire visible spectrum, made by the magnetic assembly of monodisperse nanorods into body-centered-tetragonal photonic crystals, is reported. Their prominent magnetic and crystal anisotropy makes it efficient to generate multicolors using one photonic pigment by magnetically controlling the crystal orientation. Further, the combination of angle-dependent diffraction and magnetic orientation control enables the design of rotation-asymmetric photonic films that display distinct patterns and encrypted information in response to rotation. The efficient multicolor generation through precise orientational control makes this novel photonic pigment promising in developing high-performance structural-colored materials and optical devices.

Carbon-supported nanocomposites are attracting particular attention as high-performance, low-cost electrocatalysts for electrochemical water splitting. These are mostly prepared by pyrolysis and hydrothermal procedures that are time-consuming (from hours to days) and typically difficult to produce a nonequilibrium phase. Herein, for the first time ever, we exploit magnetic induction heating-quenching for ultrafast production of carbon-FeNi spinel oxide nanocomposites (within seconds), which exhibit an unprecedentedly high performance towards oxygen evolution reaction (OER), with an ultralow overpotential of only +260 mV to reach the high current density of 100 mA cm-2. Experimental and theoretical studies show that the rapid heating and quenching process (ca. 103 K s-1) impedes the Ni and Fe phase segregation and produces a Cl-rich surface, both contributing to the remarkable catalytic activity. Results from this study highlight the unique advantage of ultrafast heating/quenching in the structural engineering of functional nanocomposites to achieve high electrocatalytic performance towards important electrochemical reactions.

The development of ultrafast detectors for electron microscopy (EM) opens a new door to exploring dynamics of nanomaterials; however, it raises grand challenges for big data processing and storage. Here, we combine deep learning and temporal compressive sensing (TCS) to propose a novel EM big data compression strategy. Specifically, TCS is employed to compress sequential EM images into a single compressed measurement; an end-to-end deep learning network is leveraged to reconstruct the original images. Owing to the significantly improved compression efficiency and built-in denoising capability of the deep learning framework over conventional JPEG compression, compressed videos with a compression ratio of up to 30 can be reconstructed with high fidelity. Using this approach, considerable encoding power, memory, and transmission bandwidth can be saved, allowing it to be deployed to existing detectors. We anticipate the proposed technique will have far-reaching applications in edge computing for EM and other imaging techniques.

High-nickel content cathode materials offer high energy density. However, the structural and surface instability may cause poor capacity retention and thermal stability of them. To circumvent this problem, nickel concentration-gradient materials have been developed to enhance high-nickel content cathode materials' thermal and cycling stability. Even though promising, the fundamental mechanism of the nickel concentration gradient's stabilization effect remains elusive because it is inseparable from nickel's valence gradient effect. To isolate nickel's valence gradient effect and understand its fundamental stabilization mechanism, we design and synthesize a LiNi0.8Mn0.1Co0.1O2 material that is compositionally uniform and has a hierarchical valence gradient. The nickel valence gradient material shows superior cycling and thermal stability than the conventional one. The result suggests creating an oxidation state gradient that hides the more capacitive but less stable Ni3+ away from the secondary particle surfaces is a viable principle towards the optimization of high-nickel content cathode materials.

Multifunctional nano-heterostructures (NHSs) with controlled morphology are cardinal in many applications, but the understanding of the nanoscale colloidal chemistry is yet to be fulfilled. The stability of the involved crystalline phases in different solvents at mid- and high-temperatures and reaction kinetics considerably affect the nucleation and growth of the materials and their final architecture. The formation mechanism of manganese ferrite-based core-shell NHSs is herein investigated. The effects of the core size (8, 10, and 11 nm), the shell nature (cobalt ferrite and spinel iron oxide) and the polarity of the solvent (toluene and octanol) on the dissolution phenomena of manganese ferrite are also studied. Noteworthily, the combined use of bulk (powder X-ray diffraction, 57Fe Mössbauer spectroscopy, and DC magnetometry) and nanoscale techniques (HRTEM and STEM-EDX) provides new insights into the manganese ferrite dissolution phenomena, the colloidal stability in an organic environment, and the critical size below which dissolution is complete. Moreover, the dissolved manganese and iron ions react further, leading to an inverted core-shell in the mother liquor solution, paving the way to novel synthetic pathways in nanocrystal design. The MnFe2O4@CoFe2O4 core-shell heterostructures were also employed as heat mediators, exploiting the magnetic coupling between a hard (CoFe2O4) and a soft phase (MnFe2O4).

Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

We explored a series of highly uniform magnetic nanoparticles (MNPs) with a core-shell nanoarchitecture prepared by an efficient solvothermal approach. In our study, we focused on the water dispersion of MNPs based on two different CoFe2O4 core sizes and the chemical nature of the shell (MnFe2O4 and spinel iron oxide). We performed an uncommon systematic investigation of the time and temperature evolution of the adiabatic heat release at different frequencies of the alternating magnetic field (AMF). Our systematic study elucidates the nontrivial variations in the heating efficiency of core-shell MNPs concerning their structural, magnetic, and morphological properties. In addition, we identified anomalies in the temperature and frequency dependencies of the specific power absorption (SPA). We conclude that after the initial heating phase, the heat release is governed by the competition of the Brown and Néel mechanism. In addition, we demonstrated that a rational parameter sufficiently mirroring the heating ability is the mean magnetic moment per MNP. Our study, thus, paves the road to fine control of the AMF-induced heating by MNPs with fine-tuned structural, chemical, and magnetic parameters. Importantly, we claim that the nontrivial variations of the SPA with the temperature must be considered, e.g., in the emerging concept of MF-assisted catalysis, where the temperature profile influences the undergoing chemical reactions.

Nanoparticle-based clusters permit the harvesting of collective and emergent properties, with applications ranging from optics and sensing to information processing and catalysis. However, existing approaches to create such architectures are typically system-specific, which limits designability and fabrication. Our work addresses this challenge by demonstrating that cluster architectures can be rationally formed using components with programmable valence. We realize cluster assemblies by employing a three-dimensional (3D) DNA meshframe with high spatial symmetry as a site-programmable scaffold, which can be prescribed with desired valence modes and affinity types. Thus, this meshframe serves as a versatile platform for coordination of nanoparticles into desired cluster architectures. Using the same underlying frame, we show the realization of a variety of preprogrammed designed valence modes, which allows for assembling 3D clusters with complex architectures. The structures of assembled 3D clusters are verified by electron microcopy imaging, cryo-EM tomography and in-situ X-ray scattering methods. We also find a close agreement between structural and optical properties of designed chiral architectures.

Layered lithium metal oxides have become the cathode of choice for state-of-the-art Li-ion batteries (LIBs), particularly those with high Ni content. However, the Ni-rich cathode materials suffer from extensive oxygen evolution, which contributes to the formation of surface rocksalt phases as well as thermal instability. Using first-principles calculations, we systematically evaluate the effectiveness of doping elements to enhance surface oxygen retention of Li1-xNiO2. The evaluation process includes (i) choosing the most stable surface facet from the perspective of equilibrium surface stability analysis of as-synthesized LiNiO2, (ii) determining the preferable atomic site and segregation behavior for each dopant, and (iii) evaluating the surface oxygen retention ability of doped-Li1-xNiO2 (0.25 ≤ x ≤ 1) compared to the pristine material. We also discuss and rationalize the ability of these elements to enhance surface oxygen retention based on local environment descriptors such as dopant-oxygen bond strength. Overall, W, Sb, Ta and Ti are predicted as the most promising surface dopants due to their strong oxygen bonds and robust surface segregation behavior. Finally, Sb-doped LiNiO2 is synthesized and shown to present a surface enrichment of Sb and a significantly improved electrochemical performance, comparing with pristine LiNiO2. This work provides a generic approach that can lead to the greatly enhanced stabilization of all high-energy cathode materials, particularly the high Ni and low Co oxides. This journal is

Rechargeable lithium-ion batteries with high energy density that can be safely charged and discharged at high rates are desirable for electrified transportation and other applications1-3. However, the sub-optimal intercalation potentials of current anodes result in a trade-off between energy density, power and safety. Here we report that disordered rock salt4,5 Li3+xV2O5 can be used as a fast-charging anode that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li+ reference electrode. The increased potential compared to graphite6,7 reduces the likelihood of lithium metal plating if proper charging controls are used, alleviating a major safety concern (short-circuiting related to Li dendrite growth). In addition, a lithium-ion battery with a disordered rock salt Li3V2O5 anode yields a cell voltage much higher than does a battery using a commercial fast-charging lithium titanate anode or other intercalation anode candidates (Li3VO4 and LiV0.5Ti0.5S2)8,9. Further, disordered rock salt Li3V2O5 can perform over 1,000 charge-discharge cycles with negligible capacity decay and exhibits exceptional rate capability, delivering over 40 per cent of its capacity in 20 seconds. We attribute the low voltage and high rate capability of disordered rock salt Li3V2O5 to a redistributive lithium intercalation mechanism with low energy barriers revealed via ab initio calculations. This low-potential, high-rate intercalation reaction can be used to identify other metal oxide anodes for fast-charging, long-life lithium-ion batteries.

Bi-magnetic core-shell spinel ferrite-based nanoparticles with different CoFe2O4 core size, chemical nature of the shell (MnFe2O4 and spinel iron oxide), and shell thickness were prepared using an efficient solvothermal approach to exploit the magnetic coupling between a hard and a soft ferrimagnetic phase for magnetic heat induction. The magnetic behavior, together with morphology, stoichiometry, cation distribution, and spin canting, were investigated to identify the key parameters affecting the heat release. General trends in the heating abilities, as a function of the core size, the nature and the thickness of the shell, were hypothesized based on this systematic fundamental study and confirmed by experiments conducted on the water-based ferrofluids.

Electrochemical synthesis of H2O2 through a selective two-electron (2e-) oxygen reduction reaction (ORR) is an attractive alternative to the industrial anthraquinone oxidation method, as it allows decentralized H2O2 production. Herein, we report that the synergistic interaction between partially oxidized palladium (Pdδ+) and oxygen-functionalized carbon can promote 2e- ORR in acidic electrolytes. An electrocatalyst synthesized by solution deposition of amorphous Pdδ+ clusters (Pd3δ+ and Pd4δ+) onto mildly oxidized carbon nanotubes (Pdδ+-OCNT) shows nearly 100% selectivity toward H2O2 and a positive shift of ORR onset potential by ~320 mV compared with the OCNT substrate. A high mass activity (1.946 A mg-1 at 0.45 V) of Pdδ+-OCNT is achieved. Extended X-ray absorption fine structure characterization and density functional theory calculations suggest that the interaction between Pd clusters and the nearby oxygen-containing functional groups is key for the high selectivity and activity for 2e- ORR.

Pursuing active and durable water splitting electrocatalysts is of vital significance for solving the sluggish kinetics of the oxygen evolution reaction (OER) process in energy supply. Herein, theoretical calculations identify that the local distortion-strain effect in amorphous RuTe2 system abnormally sensitizes the Te-pπ coupling capability and enhances the electron-transfer of Ru-sites, in which the excellent inter-orbital p-d transfers determine strong electronic activities for boosting OER performance. Thus, a robust electrocatalyst based on amorphous RuTe2 porous nanorods (PNRs) is successfully fabricated. In the acidic water splitting, a-RuTe2 PNRs exhibit a superior performance, which only require a cell voltage of 1.52 V to reach a current density of 10 mA cm-2. Detailed investigations show that the high density of defects combine with oxygen atoms to form RuOxHy species, which are conducive to the OER. This work offers valuable insights for constructing robust electrocatalysts based on theoretical calculations guided by rational design and amorphous materials.

We present a joint model based on deep learning that is designed to inpaint the missing-wedge sinogram of electron tomography and reduce the residual artifacts in the reconstructed tomograms. Traditional methods, such as weighted back projection (WBP) and simultaneous algebraic reconstruction technique (SART), lack the ability to recover the unacquired project information as a result of the limited tilt range; consequently, the tomograms reconstructed using these methods are distorted and contaminated with the elongation, streaking, and ghost tail artifacts. To tackle this problem, we first design a sinogram filling model based on the use of Residual-in-Residual Dense Blocks in a Generative Adversarial Network (GAN). Then, we use a U-net structured Generative Adversarial Network to reduce the residual artifacts. We build a two-step model to perform information recovery and artifacts removal in their respective suitable domain. Compared with the traditional methods, our method offers superior Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) to WBP and SART; even with a missing wedge of 45°, our method offers reconstructed images that closely resemble the ground truth with nearly no artifacts. In addition, our model has the advantage of not needing inputs from human operators or setting hyperparameters such as iteration steps and relaxation coefficient used in TV-based methods, which highly relies on human experience and parameter fine turning.

In this work, we show that the well-known lithium-ion anode material, Li4Ti5O12, exhibits exceptionally high initial capacity of 310 mAh g-1 when it is discharged to 0.01 V. It maintains a reversible capacity of 230 mAh g-1, far exceeding the "theoretical" capacity of 175 mAh g-1 when this anode is lithiated to the composition Li7Ti5O12. Neutron diffraction analyses identify that additional Li reversibly enters into the Li7Ti5O12 to form Li8Ti5O12. density functional theory (DFT) calculations reveal the average potentials of the Li4Ti5O12 to Li7Ti5O12 step and the Li7Ti5O12 to Li8Ti5O12 step are 1.57 and 0.19 V, respectively, which are in excellent agreement with experimental results. Transmission electron microscopy (TEM) studies confirm that the irreversible capacity of Li4Ti5O12 during its first cycle originates from the formation of a solid electrolyte interface (SEI) layer. This work clarifies the fundamental lithiation mechanism of the Li4Ti5O12, when lithiated to 0.01 V vs Li.

The catalytic activity of low-dimensional electrocatalysts is highly dependent on their local atomic structures, particularly those less-coordinated sites found at edges and corners; therefore, a direct probe of the electrocatalytic current at specified local sites with true nanoscopic resolution has become critically important. Despite the growing availability of operando imaging tools, to date it has not been possible to measure the electrocatalytic activities from individual material edges and directly correlate those with the local structural defects. Herein, we show the possibility of using feedback and generation/collection modes of operation of the scanning electrochemical microscope (SECM) to independently image the topography and local electrocatalytic activity with 15-nm spatial resolution. We employed this operando microscopy technique to map out the oxygen evolution activity of a semi-2D nickel oxide nanosheet. The improved resolution and sensitivity enables us to distinguish the higher activities of the materials' edges from that of the fully coordinated surfaces in operando The combination of spatially resolved electrochemical information with state-of-the-art electron tomography, that unravels the 3D complexity of the edges, and ab initio calculations allows us to reveal the intricate coordination dependent activity along individual edges of the semi-2D material that is not achievable by other methods. The comparison of the simulated line scans to the experimental data suggests that the catalytic current density at the nanosheet edge is ∼200 times higher than that at the NiO basal plane.

Despite the importance of studying the instability of delithiated cathode materials, it remains difficult to underpin the degradation mechanism of lithium-rich cathode materials due to the complication of combined chemical and structural evolutions. Herein, we use state-of-the-art electron microscopy tools, in conjunction with synchrotron X-ray techniques and first-principle calculations to study a 4d-element-containing compound, Li2Ru0.5Mn0.5O3. We find surprisingly, after cycling, ruthenium segregates out as metallic nanoclusters on the reconstructed surface. Our calculations show that the unexpected ruthenium metal segregation is due to its thermodynamic insolubility in the oxygen deprived surface. This insolubility can disrupt the reconstructed surface, which explains the formation of a porous structure in this material. This work reveals the importance of studying the thermodynamic stability of the reconstructed film on the cathode materials and offers a theoretical guidance for choosing manganese substituting elements in lithium-rich as well as stoichiometric layer-layer compounds for stabilizing the cathode surface.

While common growth models assume a structure-less liquid composed of atomic flow units, structural ordering has been shown in liquid metals. Here, we conduct in situ transmission electron microscopy crystallization experiments on metallic glass nanorods, and show that structural ordering strongly affects crystal growth and is controlled by nanorod thermal history. Direct visualization reveals structural ordering as densely populated small clusters in a nanorod heated from the glass state, and similar behavior is found in molecular dynamics simulations of model metallic glasses. At the same growth temperature, the asymmetry in growth rate for rods that are heated versus cooled decreases with nanorod diameter and vanishes for very small rods. We hypothesize that structural ordering enhances crystal growth, in contrast to assumptions from common growth models. The asymmetric growth rate is attributed to the difference in the degree of the structural ordering, which is pronounced in the heated glass but sparse in the cooled liquid.

In the version of this Article originally published, the author Baran Eren was mistakenly affiliated with the Harbin Institute of Technology, China; it has now been corrected to Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

Bimetallic and multi-component catalysts typically exhibit composition-dependent activity and selectivity, and when optimized often outperform single-component catalysts. Here we used ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) and in situ and ex situ transmission electron microscopy (TEM) to elucidate the origin of composition dependence observed in the catalytic activities of monodisperse CoPd bimetallic nanocatalysts for CO oxidation. We found that the catalysis process induced a reconstruction of the catalysts, leaving CoOx on the nanoparticle surface. The synergy between Pd and CoOx coexisting on the surface promotes the catalytic activity of the bimetallic catalysts. This synergistic effect can be optimized by tuning the Co/Pd ratios in the nanoparticle synthesis, and it reaches a maximum at compositions near Co0.24Pd0.76, which achieves complete CO conversion at the lowest temperature. Our combined AP-XPS and TEM studies provide direct observation of the surface evolution of the bimetallic nanoparticles under catalytic conditions and show how this evolution correlates with catalytic properties.