In situ Raman spectroscopy reveals the structure and dissociation of interfacial water.

Understanding the structure and dynamic process of water at the solid-liquid interface is an extremely important topic in surface science, energy science and catalysis1-3. As model catalysts, atomically flat single-crystal electrodes exhibit well-defined surface and electric field properties, and therefore may be used to elucidate the relationship between structure and electrocatalytic activity at the atomic level4,5. Hence, studying interfacial water behaviour on single-crystal surfaces provides a framework for understanding electrocatalysis6,7. However, interfacial water is notoriously difficult to probe owing to interference from bulk water and the complexity of interfacial environments8. Here, we use electrochemical, in situ Raman spectroscopic and computational techniques to investigate the interfacial water on atomically flat Pd single-crystal surfaces. Direct spectral evidence reveals that interfacial water consists of hydrogen-bonded and hydrated Na+ ion water. At hydrogen evolution reaction (HER) potentials, dynamic changes in the structure of interfacial water were observed from a random distribution to an ordered structure due to bias potential and Na+ ion cooperation. Structurally ordered interfacial water facilitated high-efficiency electron transfer across the interface, resulting in higher HER rates. The electrolytes and electrode surface effects on interfacial water were also probed and found to affect water structure. Therefore, through local cation tuning strategies, we anticipate that these results may be generalized to enable ordered interfacial water to improve electrocatalytic reaction rates.

Cation-Induced Interfacial Hydrophobic Microenvironment Promotes the C-C Coupling in Electrochemical CO2 Reduction.

The electrochemical carbon dioxide reduction reaction (CO2RR) toward C2 products is a promising way for the clean energy economy. Modulating the structure of the electric double layer (EDL), especially the interfacial water and cation type, is a useful strategy to promote C-C coupling, but atomic understanding lags far behind the experimental observations. Herein, we investigate the combined effect of interfacial water and alkali metal cations on the C-C coupling at the Cu(100) electrode/electrolyte interface using ab initio molecular dynamics (AIMD) simulations with a constrained MD and slow-growth approach. We observe a linear correlation between the water-adsorbate stabilization effect, which manifests as hydrogen bonds, and the corresponding alleviation in the C-C coupling free energy. The role of a larger cation, compared to a smaller cation (e.g., K+ vs Li+), lies in its ability to approach the interface through desolvation and coordinates with the *CO+*CO moiety, partially substituting the hydrogen-bonding stabilizing effect of interfacial water. Although this only results in a marginal reduction of the energy barrier for C-C coupling, it creates a local hydrophobic environment with a scarcity of hydrogen bonds owing to its great ionic radius, impeding the hydrogen of surrounding interfacial water to approach the oxygen of the adsorbed *CO. This skillfully circumvents the further hydrogenation of *CO toward the C1 pathway, serving as the predominant factor through which a larger cation facilitates C-C coupling. This study unveils a comprehensive atomic mechanism of the cation-water-adsorbate interactions that can facilitate the further optimization of the electrolyte and EDL for efficient C-C coupling in CO2RR.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning.

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.

Highly Stable Lithium Metal Batteries Enabled by Tuning the Molecular Polarity of Diluents in Localized High-Concentration Electrolytes.

Electrolyte plays a crucial role in ensuring stable operation of lithium metal batteries (LMBs). Localized high-concentration electrolytes (LHCEs) have the potential to form a robust solid-electrolyte interphase (SEI) and mitigate Li dendrite growth, making them a highly promising electrolyte option. However, the principles governing the selection of diluents, a crucial component in LHCE, have not been clearly determined, hampering the advancement of such a type of electrolyte systems. Herein, the diluents from the perspective of molecular polarity are rationally designed and developed. A moderately fluorinated solvent, 1-(1,1,2,2-tetrafluoroethoxy)propane (TNE), is employed as a diluent to create a novel LHCE. The unique molecular structure of TNE enhances the intrinsic dipole moment, thereby altering solvent interactions and the coordination environment of Li-ions in LHCE. The achieved solvation structure not only enhances the bulk properties of LHCE, but also facilitates the formation of more stable anion-derived SEIs featured with a higher proportion of inorganic species. Consequently, the corresponding full cells of both Li||LiFePO4 and Li||LiNi0.8 Co0.1 Mn0.1 O2 cells utilizing Li thin-film anodes exhibit extended long-term stability with significantly improved average Coulombic efficiency. This work offers new insights into the functions of diluents in LHCEs and provides direction for further optimizing the LHCEs for LMBs.

Algebraic Graph-Based Machine Learning Model for Li-Cluster Prediction.

In cluster research, determining the ground-state structure of medium-sized clusters is hindered by a large number of local minimum on potential energy surfaces. The global optimization heuristic algorithm is time-consuming due to the use of DFT to determine the relative size of the cluster energy. Although machine learning (ML) is proved to be a promising way to reduce the DFT computational costs, a suitable method to represent clusters as input vectors is one of the bottlenecks in the application of ML to cluster research. In this work, we proposed a multiscale weighted spectral subgraph (MWSS) as an effective low-dimension representation of clusters and build an MWSS-based ML model to discover the structure-energy relationships in lithium clusters. We combine this model with the particle swarm optimization algorithm and DFT calculations to search for globally stable structures of clusters. We have successfully predicted the ground-state structure of Li20.

Sequential co-reduction of nitrate and carbon dioxide enables selective urea electrosynthesis.

Despite the recent achievements in urea electrosynthesis from co-reduction of nitrogen wastes (such as NO3-) and CO2, the product selectivity remains fairly mediocre due to the competing nature of the two parallel reduction reactions. Here we report a catalyst design that affords high selectivity to urea by sequentially reducing NO3- and CO2 at a dynamic catalytic centre, which not only alleviates the competition issue but also facilitates C-N coupling. We exemplify this strategy on a nitrogen-doped carbon catalyst, where a spontaneous switch between NO3- and CO2 reduction paths is enabled by reversible hydrogenation on the nitrogen functional groups. A high urea yield rate of 596.1 µg mg-1 h-1 with a promising Faradaic efficiency of 62% is obtained. These findings, rationalized by in situ spectroscopic techniques and theoretical calculations, are rooted in the proton-involved dynamic catalyst evolution that mitigates overwhelming reduction of reactants and thereby minimizes the formation of side products.

Closo-[B12 H12 ]2- Derivatives with Polar Groups As Promising Building Blocks in Metal-Organic Frameworks for Gas Separation.

Engineering design of metal organic frameworks (MOFs) for gas separation applications is nowadays a thriving field of investigation. Based on the recent experimental studies of dodecaborate-hybrid MOFs as potential materials to separate industry-relevant gas mixtures, we herein present a systematic theoretical study on the derivatives of the closo-dodecaborate anion [B12 H12 ]2- , which can serve as building blocks for MOFs. We discover that amino functionalization can impart a greater ability to selectively capture carbon dioxide from its mixtures with other gases such as nitrogen, ethylene and acetylene. The main advantage lies in the polarization effect induced by amino group, which favors the localization of the negative charges on the boron-cluster anion and offers a nucleophilic anchoring site to accommodate the carbon atom in carbon dioxide. This work suggests an appealing strategy of polar functionalization to optimize the molecule discrimination ability via preferential adsorption.

In situ electrochemical Raman spectroscopy and ab initio molecular dynamics study of interfacial water on a single-crystal surface.

The dynamics and chemistry of interfacial water are essential components of electrocatalysis because the decomposition and formation of water molecules could dictate the protonation and deprotonation processes on the catalyst surface. However, it is notoriously difficult to probe interfacial water owing to its location between two condensed phases, as well as the presence of external bias potentials and electrochemically induced reaction intermediates. An atomically flat single-crystal surface could offer an attractive platform to resolve the internal structure of interfacial water if advanced characterization tools are developed. To this end, here we report a protocol based on the combination of in situ Raman spectroscopy and ab initio molecular dynamics (AIMD) simulations to unravel the directional molecular features of interfacial water. We present the procedures to prepare single-crystal electrodes, construct a Raman enhancement mode with shell-isolated nanoparticle, remove impurities, eliminate the perturbation from bulk water and dislodge the hydrogen bubbles during in situ electrochemical Raman experiments. The combination of the spectroscopic measurements with AIMD simulation results provides a roadmap to decipher the potential-dependent molecular orientation of water at the interface. We have prepared a detailed guideline for the application of combined in situ Raman and AIMD techniques; this procedure may take a few minutes to several days to generate results and is applicable to a variety of disciplines ranging from surface science to energy storage to biology.

Tailoring the coercive field in ferroelectric metal-free perovskites by hydrogen bonding.

The miniaturization of ferroelectric devices in non-volatile memories requires the device to maintain stable switching behavior as the thickness scales down to nanometer scale, which requires the coercive field to be sufficiently large. Recently discovered metal-free perovskites exhibit advantages such as structural tunability and solution-processability, but they are disadvantaged by a lower coercive field compared to inorganic perovskites. Herein, we demonstrate that the coercive field (110 kV/cm) in metal-free ferroelectric perovskite MDABCO-NH4-(PF6)3 (MDABCO = N-methyl-N'-diazabicyclo[2.2.2]octonium) is one order larger than MDABCO-NH4-I3 (12 kV/cm) owing to the stronger intermolecular hydrogen bonding in the former. Using isotope experiments, the ferroelectric-to-paraelectric phase transition temperature and coercive field are verified to be strongly influenced by hydrogen bonds. Our work highlights that the coercive field of organic ferroelectrics can be tailored by tuning the strength of hydrogen bonding.

Tuning the exposure of BiVO4-{010} facets to enhance the N2 photofixation performance.

Effective separation of photoexcited carriers and chemisorption of the N2 molecule are two key issues to efficient nitrogen photofixation. The spatial charge separation of BiVO4 with anisotropic exposed facets, namely the transfer of photoexcited electrons and holes to {010} and {110} facets, respectively, helps to enhance the separation ability of photogenerated carriers. Theoretical calculation results predict that a surface oxygen vacancy is easier to form on the (010) facet than on the (110) facet of BiVO4. Accordingly, in this study, enhanced N2 photofixation performance has been achieved for the first time by tuning the exposure of {010} facets of BiVO4.

Wavelength-Dependent Solar N2 Fixation into Ammonia and Nitrate in Pure Water.

Solar-driven N2 fixation using a photocatalyst in water presents a promising alternative to the traditional Haber-Bosch process in terms of both energy efficiency and environmental concern. At present, the product of solar N2 fixation is either NH4 + or NO3 -. Few reports described the simultaneous formation of ammonia (NH4 +) and nitrate (NO3 -) by a photocatalytic reaction and the related mechanism. In this work, we report a strategy to photocatalytically fix nitrogen through simultaneous reduction and oxidation to produce NH4 + and NO3 - by W18O49 nanowires in pure water. The underlying mechanism of wavelength-dependent N2 fixation in the presence of surface defects is proposed, with an emphasis on oxygen vacancies that not only facilitate the activation and dissociation of N2 but also improve light absorption and the separation of the photoexcited carriers. Both NH4 + and NO3 - can be produced in pure water under a simulated solar light and even till the wavelength reaching 730 nm. The maximum quantum efficiency reaches 9% at 365 nm. Theoretical calculation reveals that disproportionation reaction of the N2 molecule is more energetically favorable than either reduction or oxidation alone. It is worth noting that the molar fraction of NH4 + in the total product (NH4 + plus NO3 -) shows an inverted volcano shape from 365 nm to 730 nm. The increased fraction of NO3 - from 365 nm to around 427 nm results from the competition between the oxygen evolution reaction (OER) at W sites without oxygen vacancies and the N2 oxidation reaction (NOR) at oxygen vacancy sites, which is driven by the intrinsically delocalized photoexcited holes. From 427 nm to 730 nm, NOR is energetically restricted due to its higher equilibrium potential than that of OER, accompanied by the localized photoexcited holes on oxygen vacancies. Full disproportionation of N2 is achieved within a range of wavelength from ~427 nm to ~515 nm. This work presents a rational strategy to efficiently utilize the photoexcited carriers and optimize the photocatalyst for practical nitrogen fixation.

Electrochemical Nitrogen Reduction Reaction Performance of Single-Boron Catalysts Tuned by MXene Substrates.

A boron (B) center, which has an electronic structure mimicking the filled and empty d orbitals in transition metals, can effectively activate the triple bond in N2 so as to catalyze the nitrogen reduction reaction (NRR). Here, by means of density functional theory, we have systematically investigated the catalytic performance of a single B atom decorated on two-dimensional transition metal carbides (MXenes). The B-doped Mo2CO2 and W2CO2 MXenes exhibit outstanding catalytic activity and selectivity with limiting potentials of -0.20 and -0.24 V, respectively. Importantly, we have found that, although a high tendency of B-to-adsorbate electron donation can promote the hydrogenation of *N2 to *N2H, it would also severely hamper the *NH2 to *NH3 conversion due to the strong B-N bonding. Such an electron-donation effect can be reasonably tuned by the transition metal in the MXene substrate, which enables us to achieve optimized catalytic performance with a certain moderate degree of electron donation.