Facile and Green Synthesis of Well-Defined Nanocrystal Oxygen Evolution Catalysts by Rational Crystallization Regulation

The development of catalysts for an economical and efficient oxygen evolution reaction (OER) is critical for clean and sustainable energy storage and conversion. Nickel-iron-based (NiFe) nanostructures are widely investigated as active OER catalysts and especially shape-controlled nanocrystals exhibit optimized surface structure and electronic properties. However, the structural control from amorphous to well-defined crystals is usually time-consuming and requires multiple stages. Here, a universal two-step precipitation-hydrothermal approach is reported to prepare a series of NiFe-based nanocrystals (e.g., hydroxides, sulfides, and molybdates) from amorphous precipitates. Their morphology and evolution of atomic and electronic structure during this process are studied using conclusive microscopy and spectroscopy techniques. The short-term, additive-free, and low-cost method allows for the control of the crystallinity of the materials and facilitates the generation of nanosheets, nanorods, or nano-octahedra with excellent water oxidation activity. The NiFe-based crystalline catalysts exhibit slightly compromised initial activity but more robust long-term stability than their amorphous counterparts during electrochemical operation. This facile, reliable, and universal synthesis method is promising in strategies for fabricating NiFe-based nanostructures as efficient and economically valuable OER electrocatalysts.

Metal Complex/ZnS-Modified Ni Foam as Magnetically Stirrable Photocatalysts: Roles of Redox Mediators and Carrier Dynamics Monitored by Operando Synchrotron X-ray Spectroscopy

Magnetically stirrable photocatalysts binding the ZnS-decorated Ni foam with the metal complex cocatalyst as a redox mediator and light-absorbing composition were investigated. Loading metal complex can improve light absorption, surface hydrophilicity, interfacial charge migration, and H₂ production activity. The variation of the metal valences of the composite photocatalysts in an operando environment (with sacrificial agent solution) with and without light irradiation was investigated by X-ray absorption near-edge structure (XANES) spectra and Fourier-transformed extended X-ray absorption fine structure (EXAFS) spectra to monitor the charge carrier dynamics of photocatalysis and explain how the macrocyclic Cu complex (CuC) acted as a redox mediator better than the Ni complex. The smaller valence difference of copper valence in ZS/CuC for dark and light states revealed that the Cu complex facilitates a reversible electron transfer between the ZnS photocatalyst and H⁺. Loading the Cu complex can improve the separation of photogenerated carriers by the redox couple of complexes, leading to a significantly improved photocatalytic H₂ production activity of 8150 μmol h^-1 g^-1. The reactants can flow through these magnetically stirrable Ni foam-based photocatalysts by magnetic-field-driven stirring, which improves the contact between photocatalysts and the sacrificial agents. The operando synchrotron provides new insights for understanding the roles of redox mediators.

Control the greenhouse gas emission via mediating the dissimilatory iron reduction: Fulvic acid inhibit secondary mineralization of ferrihydrite

Reducing methane emission is of great importance to control the global greenhouse effect. Dissimilatory iron reduction (DIR) coupling of organic matter decomposition may suppress methane production via reducing primary electron donors available for methanogenesis. However, during DIR, the amorphous iron oxides (e.g., ferrihydrite) are easy to transform into more stable crystalline iron minerals, which slowdowns the rate of DIR. Humic substance (HS) with redox activity has been extensively reported to facilitate DIR via "electron shuttles" mechanism, yet little known about the effect of HS on mediating the mineralization of iron oxides and the subsequent influences on DIR and methanogenesis. To clarify this, ferrihydrite and fulvic acid (FA) (as the model substance of HS) were supplied to anaerobic methanogenesis systems. Results showed that FA could significantly decrease the formation of crystalline iron oxides, enhance DIR rate by 13.72% and suppress methanogenesis by 25.13% compared to ferrihydrite supplemented only. By X-ray absorption spectra analysis, it was found that FA could complex with ferrihydrite via forming a Fe-C/O structure on the second shell of Fe atom. Quantum chemical calculation further confirmed that FA reduced the adsorption energy between Fe(II) and ferrihydrite. Our study suggested that rational use of HS to mediate mineralization pathway of iron oxides could efficiently improve the availability of iron oxides to drive DIR and control the conversion of organics into CH₄ in natural or engineered systems.

Emulator-based decomposition for structural sensitivity of core-level spectra

We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least-squares fitting and the observed behaviour is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.

XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing methods of experimental characterization of nanocatalysts, which make it possible to assess the possibility of using these materials in specific chemical reactions or applications, generate significant amounts of heterogeneous data. The acceleration of new functional materials, including nanocatalysts, directly depends on the speed and quality of extracting hidden dependencies and knowledge from the obtained experimental data. Usually, such experiments involve different characterization techniques and different types of X-ray absorption spectroscopy (XAS) too. Using the machine learning (ML) methods based on XAS data, we can study and predict the atomic-scale structure and another bunch of parameters for the nanocatalyst efficiently. However, before using any ML model, it is necessary to make sure that the XAS raw experimental data is properly pre-processed, cleared, and prepared for ML application. Usually, the XAS preprocessing stage is vaguely presented in scientific studies, and the main efforts of researchers are devoted to the ML description and implementation stage. However, the quality of the input data influences the quality of ML analysis and the prediction results used in the future. This paper fills the gap between the stage of obtaining XAS data from synchrotron facilities and the stage of using and customizing various ML analysis and prediction models. We aimed this study to develop automated tools for the preprocessing and presentation of data from physical experiments and the creation of deposited datasets on the basis of the example of studying palladium-based nanocatalysts using synchrotron radiation facilities. During the study, methods of preliminary processing of XAS data were considered, which can be conditionally divided into X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). This paper proposes a software toolkit that implements data preprocessing scenarios in the form of a single pipeline. The main preprocessing methods used in this study proposed are principal component analysis (PCA); z-score normalization; the interquartile method for eliminating outliers in the data; as well as the k-means machine learning method, which makes it possible to clarify the phase of the studied material sample by clustering feature vectors of experiments. Among the results of this study, one should also highlight the obtained deposited datasets of physical experiments on palladium-based nanocatalysts using synchrotron radiation. This will allow for further high-quality data mining to extract new knowledge about materials using artificial intelligence methods and machine learning models, and will ensure the smooth dissemination of these datasets to researchers and their reuse.

Atomic Iridium Incorporated in Cobalt Hydroxide for Efficient Oxygen Evolution Catalysis in Neutral Electrolyte

Developing highly efficient catalysts for oxygen evolution reaction (OER) in neutral media is extremely crucial for microbial electrolysis cells and electrochemical CO₂ reduction. Herein, a facile one-step approach is developed to synthesize a new type of well-dispersed iridium (Ir) incorporated cobalt-based hydroxide nanosheets (nominated as CoIr) for OER. The Ir species as clusters and single atoms are incorporated into the defect-rich hydroxide nanosheets through the formation of rich Co-Ir species, as revealed by systematic synchrotron radiation based X-ray spectroscopic characterizations combining with high-angle annular dark-field scanning transmission electron microscopy measurement. The optimized CoIr with 9.7 wt% Ir content displays highly efficient OER catalytic performance with an overpotential of 373 mV to achieve the current density of 10 mA cm^-2 in 1.0 m phosphate buffer solution, significantly outperforming the commercial IrO₂ catalysts. Further characterizations toward the catalyst after undergoing OER process indicate that unique Co oxyhydroxide and high valence Ir species with low-coordination structure are formed due to the high oxidation potentials, which authentically contributes to superior OER performance. This work not only provides a state-of-the-art OER catalyst in neutral media but also unravels the root of the excellent performance based on efficient structural identifications.

Reactivating Catalytic Surface: Insights into the Role of Hot Holes in Plasmonic Catalysis

Surface plasmon resonance of coinage metal nanoparticles is extensively exploited to promote catalytic reactions via harvesting solar energy. Previous efforts on elucidating the mechanisms of enhanced catalysis are devoted to hot electron-induced photothermal conversion and direct charge transfer to the adsorbed reactants. However, little attention is paid to roles of hot holes that are generated concomitantly with hot electrons. In this work, 13 nm spherical Au nanoparticles with small absorption cross-section are employed to catalyze a well-studied glucose oxidation reaction. Density functional theory calculation and X-ray absorption spectrum analysis reveal that hot holes energetically favor transferring catalytic intermediates to product molecules and then desorbing from the surface of plasmonic catalysts, resulting in the recovery of their catalytic activities. The studies shed new light on the use of the synergy of hot holes and hot electrons for plasmon-promoted catalysis.

X-ray natural circular dichroism in langasite crystal

Optical activity in the X-ray range stems from the electric-dipole-electric-quadrupole interference terms mixing multipoles of opposite parity, and can be observed exclusively in systems with broken inversion symmetry. The gyration tensor formalism is used to describe the X-ray optical activity in langasite La₃Ga₅SiO₁₄ crystal with the P321 space group. An experimental study of the X-ray natural circular dichroism (XNCD) near the Ga K-edge in La₃Ga₅SiO₁₄ single crystal was performed at ESRF beamline ID12, both along and perpendicular to the crystal optical axis. The combination of the quantum mechanical calculations and high-quality experimental results has allowed us to separate the contributions into X-ray absorption and XNCD spectra of Ga atoms occupying three distinct Wyckoff positions.

Cheap, High-Performance, and Wearable Mn Oxide Supercapacitors with Urea-LiClO4 Based Gel Electrolytes

Here we report a simple, scalable, and low-cost method to enhance the electrochemical properties of Mn oxide electrodes for highly efficient and flexible symmetrical supercapacitors. The method involving printing on a printer, pencil-drawing, and electrodeposition is established to fabricate Mn oxide/Ni-nanotube/graphite/paper hybrid electrodes operating with a low-cost, novel urea-LiClO₄/PVA as gel electrolyte for flexible solid-state supercapacitor (FSSC) devices. The Mn oxide nanofiber/Ni-nanotube/graphite/paper (MNNGP) electrodes in urea-LiClO₄/PVA gel electrolyte show specific capacitance (C_sp) 960 F/g in voltage region 0.8 V at 5 mV/s and exhibit excellent rates of capacitance retention more than 85% after 5000 cycles. Moreover, the electrochemical behavior of the MNNGP electrodes in urea-LiClO₄/PVA at operating temperatures 27-110 °C was investigated; the results show that the MNNGP electrodes in urea-LiClO₄/PVA exhibit outstanding performance (1100 F/g), even at 90 °C. The assembled FSSC devices based on the MNNGP electrodes in urea-LiClO₄/PVA exhibit great C_sp (380 F/g in potential region of 2.0 V at 5 mV/s, exhibiting superior energy density 211.1 W h/kg) and great cycle stability (less than 15% loss after 5000 cycles at 25 mV/s). The oxidation-state change was examined by in situ X-ray absorption spectroscopy. FSSC devices would open new opportunities in developing novel portable, wearable, and roll-up electric devices owing to the cheap, high-performance, wide range of operating temperature, and simple procedures for large-area fabrication.