2D Ultrathin Titanium Nitride Nanosheets as Separator Coatings for Li-S Batteries.

Transition metal nitrides (TMNs) are affirmed to be an appealing candidate for boosting the performance of lithium-sulfur (Li-S) batteries due to their excellent conductivity, strong interaction with sulfur species, and the effective catalytic ability for conversion of polysulfides. However, the traditional bulk TMNs are difficult to achieve large active surface area and fast transport channels for electrons/ions simultaneously. Here, a 2D ultrathin geometry of titanium nitride (TiN) is realized by a facile topochemical conversion strategy, which can not only serve as an interconnected conductive platform but also expose abundant catalytic active sites. The ultrathin TiN nanosheets are coated on a commercial separator, serving as a multifunctional interlayer in Li-S batteries for hindering the polysulfide shuttle effect by strong capture and fast conversion of polysulfides, achieving a high initial capacity of 1357 mAh g-1 at 0.1 C and demonstrating a low capacity decay of only 0.046% per cycle over 1000 cycles at 1 C.

Boosting Up Electrosynthesis of Urea with Nitrate and Carbon Dioxide via Synergistic Effect of Metallic Iron Cluster and Single-Atom.

Electrocatalytic conversion of nitrates and carbon dioxide to urea under ambient conditions shows promise as a potential substitute for traditional urea synthesis processes characterized by high consumption and pollution. In this study, a straightforward one-pot method is employed to prepare a highly efficient FeNC-Fe1N4 electrocatalyst, consisting of atomically dispersed Fe1N4 sites and metallic Fe clusters (FeNC) with particle size of 4-7 nm. The FeNC-Fe1N4 catalyst exhibits remarkable electrocatalytic activity for urea synthesis from nitrate anion (NO3 -) and carbon dioxide (CO2), achieving a urea production rate of 38.2 mmol gcat -1 h-1 at -0.9 V (vs RHE) and a Faradaic efficiency of 66.5% at -0.6 V (vs RHE). Both experimental and theoretical results conclusively demonstrate that metallic Fe clusters and Fe1N4 species provide active sites for the adsorption and activation of NO3 - and CO2, respectively, and the synergistic effect between Fe1N4 and metallic Fe clusters significantly enhances the electrochemical efficiency of urea synthesis. In all, this work contributes to the rational design and comprehensive synthesis of a dual-active site iron-based electrocatalyst, facilitating efficient and sustainable urea synthesis.

Two-Orders-of-Magnitude Enhancement of Photoinitiation Activity via a Simple Surface Engineering of Metal Nanoclusters.

Development of high-performance photoinitiator is the key to enhance the printing speed, structure resolution and product quality in 3D laser printing. Here, to improve the printing efficiency of 3D laser nanoprinting, we investigate the underlying photochemistry of gold and silver nanocluster initiators under multiphoton laser excitation. Experimental results and DFT calculations reveal the high cleavage probability of the surface S-C bonds in gold and silver nanoclusters which generate multiple radicals. Based on this understanding, we design several alkyl-thiolated gold nanoclusters and achieve a more than two-orders-of-magnitude enhancement of photoinitiation activity, as well as a significant improvement in printing resolution and fabrication window. Overall, this work for the first time unveils the detailed radical formation pathways of gold and silver nanoclusters under multiphoton activation and substantially improves their photoinitiation sensitivity via surface engineering, which pushes the limit of the printing efficiency of 3D laser lithography.

Interpretable Data-Driven Descriptors for Establishing the Structure-Activity Relationship of Metal-Organic Frameworks Toward Oxygen Evolution Reaction.

The development of readily accessible and interpretable descriptors is pivotal yet challenging in the rational design of metal-organic framework (MOF) catalysts. This study presents a straightforward and physically interpretable activity descriptor for the oxygen evolution reaction (OER), derived from a dataset of bimetallic Ni-based MOFs. Through an artificial-intelligence (AI) data-mining subgroup discovery (SGD) approach, a combination of the d-band center and number of missing electrons in eg states of Ni, as well as the first ionization energy and number of electrons in eg states of the substituents, is revealed as a gene of a superior OER catalyst. The found descriptor, obtained from the AI analysis of a dataset of MOFs containing 3-5d transition metals and 13 organic linkers, has been demonstrated to facilitate in-depth understanding of structure-activity relationship at the molecular orbital level. The descriptor is validated experimentally for 11 Ni-based MOFs. Combining SGD with physical insights and experimental verification, our work offers a highly efficient approach for screening MOF-based OER catalysts, simultaneously providing comprehensive understanding of the catalytic mechanism.

Global Optimization of Cation Ordering in Perovskites by Recommendation-Based Basin-Hopping.

Cation ordering in multication perovskites is related to many important material properties and performances, but computational determination of the cation ordering remains a major challenge. Here, we propose a new computational approach by introducing a machine learning recommender system into the basin-hopping framework (RBH) for optimizing cation ordering. Taking the electrocatalyst Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF5582) as a showcase example, we found that the efficiency of RBH in identifying low-energy configurations outperforms the methods of cluster expansion and conventional basin-hopping. The RBH results revealed that the BSCF5582 catalyst tended to have a layered ordering of A-site cations and disordered B-site cations both in bulk and on the surfaces. Further, on the A-site-terminated surface, we found the segregation of large Ba atoms. Similarly, on the A-site- terminated surface of the recently developed Cs0.2Sr0.8Co0.4Fe0.6O3 (CSCF2846) catalyst, layered ordering at the A-site and surface enrichment of large Cs atoms were also found. The layered ordering was robust against thermal effects, as found from molecular dynamics simulations at 800 K. This work provides a new approach for thermodynamic global optimization of chemical ordering in multicomponent materials.

Unraveling the formation of oxygen vacancies on the surface of transition metal-doped ceria utilizing artificial intelligence.

Ceria has been extensively utilized in different fields, with surface oxygen vacancies playing a central role. However, versatile oxygen vacancy regulation is still in its infancy. In this work, we propose an effective strategy to manipulate the oxygen vacancy formation energy via transition metal doping by combining first-principles calculations and analytical learning. We elucidate the underlying mechanism driving the formation of oxygen vacancies using combined symbolic regression and data analytics techniques. The results show that the Fermi level of the system and the electronegativity of the dopants are the paramount parameters (features) influencing the formation of oxygen vacancies. These insights not only enhance our understanding of the oxygen vacancy formation mechanism in ceria-based materials to improve their functionality but also potentially lay the groundwork for future strategies in the rational design of other transition metal oxide-based catalysts.

Calcium Oxide Adsorption of Gas Phase PCDD/Fs and Its Impact on the Adsorption Properties of Activated Carbon.

Calcium oxide (CaO), utilized in semi-dry/dry desulfurization systems at municipal solid waste incineration (MSWI) plants, demonstrates some capability to remove polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). This study assessed the gas-phase PCDD/F removal performance of CaO, activated carbon (AC) and CaO-AC mixtures. Alone, CaO achieved removal efficiencies of only 31.9% for mass and 50.8% for I-TEQ concentration. However, CaO-AC mixtures exhibited significantly higher efficiencies, reaching 96.0% and 92.5% for mass and I-TEQ concentrations, respectively, surpassing those of AC alone, which were 74.7% and 58.5%. BET analysis indicated that CaO's limited surface area and pore structure are major constraints on its adsorption performance. Density functional theory (DFT) calculations revealed that the π-π electron donor-acceptor (EDA) interaction enhances the adsorption between AC and PCDD/F, with adsorption energies ranging from -1.02 to -1.24 eV. Additionally, the induced dipole interactions between CaO and PCDD/F contribute to adsorption energies ranging from -1.13 to -1.43 eV. Moreover, with increasing chlorination levels, PCDD/F molecules are more predisposed to accept electron transfers from the surfaces of AC or CaO, thereby facilitating adsorption. The calculation for mixed AC and CaO showed that CaO modifies AC's properties, enhancing its ability to adsorb gas phase PCDD/Fs, with the higher adsorption energy and more electrons transfer, aligning with gas phase PCDD/Fs adsorption experiments. This study provides a comprehensive understanding of how CaO influences the PCDD/F adsorption performance of AC.

Unveiling the physical mechanisms driving delafossite crystal (ABX2) formation through interpretable machine learning.

A method integrating machine learning with first-principles calculations is employed to forecast the formation energy of delafossite crystals, facilitating the rapid identification of stable crystals. This approach identifies several stable candidates and highlights the importance of atomic ionization energy and electron affinity in the formation of delafossite crystals.

Direct in-situ insights into the asymmetric surface reconstruction of rutile TiO2 (110).

The reconstruction of rutile TiO2 (110) holds significant importance as it profoundly influences the surface chemistry and catalytic properties of this widely used material in various applications, from photocatalysis to solar energy conversion. Here, we directly observe the asymmetric surface reconstruction of rutile TiO2 (110)-(1×2) with atomic-resolution using in situ spherical aberration-corrected scanning transmission electron microscopy. Density functional theory calculations were employed to complement the experimental observations. Our findings highlight the pivotal role played by repulsive electrostatic interaction among the small polarons -formed by excess electrons following the removal of neutral oxygen atoms- and the subsequent surface relaxations induced by these polarons. The emergence and disappearance of these asymmetric structures can be controlled by adjusting the oxygen partial pressure. This research provides a deeper understanding, prediction, and manipulation of the surface reconstructions of rutile TiO2 (110), holding implications for a diverse range of applications and technological advancements involving rutile-based materials.

Unveiling the Structure of Oxygen Vacancies in Bulk Ceria and the Physical Mechanisms behind Their Formation.

Understanding the structures of oxygen vacancies in bulk ceria is crucial as they significantly impact the material's catalytic and electronic properties. The complex interaction between oxygen vacancies and Ce3+ ions presents challenges in characterizing ceria's defect chemistry. We introduced a machine learning-assisted cluster-expansion model to predict the energetics of defective configurations accurately within bulk ceria. This model effectively samples configurational spaces, detailing oxygen vacancy structures across different temperatures and concentrations. At lower temperatures, vacancies tend to cluster, mediated by Ce3+ ions and electrostatic repulsion, while at higher temperatures, they distribute uniformly due to configurational entropy. Our analysis also reveals a correlation between thermodynamic stability and the band gap between occupied O 2p and unoccupied Ce 4f orbitals, with wider band gaps indicating higher stability. This work enhances our understanding of defect chemistry in oxide materials and lays the groundwork for further research into how these structural properties affect ceria's performance.

A CeO2 (100) surface reconstruction unveiled by in situ STEM and particle swarm optimization techniques.

The reconstruction of the polar CeO2 (100) surface has been a subject of long-standing debates due to its complexity and the limited availability of experimental data. Herein, we successfully reveal a CeO2 (100)-(4 × 6) surface reconstruction by combining in situ spherical aberration-corrected scanning transmission electron microscopy, density functional theory calculations, and a particle swarm optimization-based algorithm for structure searching. We have further elucidated the stabilizing mechanism of the reconstructed structure, which involves the splitting of the filled Ce(4f) states and the mixing of the lower-lying ones with the O(2p) orbitals, as evidenced by the projected density of states. We also reveal that the surface chemisorption properties toward water molecules, an important step in numerous heterogeneous catalytic reactions, are enhanced. These insights into the distinct properties of ceria surface pave the way for performance improvements of ceria in a wide range of applications.