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.

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.

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.

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.

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.

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.

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.

Revealing Temperature-Dependent Oxidation Dynamics of Ni Nanoparticles via Ambient Pressure Transmission Electron Microscopy.

Understanding the oxidation mechanism of metal nanoparticles under ambient pressure is extremely important to make the best use of them in a variety of applications. Through ambient pressure transmission electron microscopy, we in situ investigated the dynamic oxidation processes of Ni nanoparticles at different temperatures under atmospheric pressure, and a temperature-dependent oxidation behavior was revealed. At a relatively low temperature (e.g., 600 °C), the oxidation of Ni nanoparticles underwent a classic Kirkendall process, accompanied by the formation of oxide shells. In contrast, at a higher temperature (e.g., 800 °C), the oxidation began with a single crystal nucleus at the metal surface and then proceeded along the metal/oxide interface without voids formed during the whole process. Through our experiments and density functional theory calculations, a temperature-dependent oxidation mechanism based on Ni nanoparticles was proposed, which was derived from the discrepancy of gas adsorption and diffusion rates under different temperatures.

Efficient Screening of Metal Promoters of Pt Catalysts for C-H Bond Activation in Propane Dehydrogenation from a Combined First-Principles Calculations and Machine-Learning Study.

Platinum-based materials are the most widely used catalysts in propane direct dehydrogenation, which could achieve a balanced activity between both propane conversion and propene formation. One of the core issues of Pt catalysts is how to efficiently activate the strong C-H bond. It has been suggested that adding second metal promoters could greatly solve this problem. In the current work, first-principles calculations combined with machine learning are performed in order to obtain the most promising metal promoters and identify key descriptors for control performance. The combination of three different modes of adding metal promoters and two ratios between promoters and platinum sufficiently describes the system under investigation. The activity of propane activation and the formation of propene are reflected by the increase or decrease of the adsorption energy and C-H bond activation of propane and propene after the addition of promoters. The data of adsorption energy and kinetic barriers from first-principles calculations are streamed into five machine-learning methods including gradient boosting regressor (GBR), K neighbors regressor (KNR), random forest regressor (RFR), and AdaBoost regressor (ABR) together with the sure independence screening and sparsifying operator (SISSO). The metrics (RMSE and R2) from different methods indicated that GBR and SISSO have the most optimal performance. Furthermore, it is found that some descriptors derived from the intrinsic properties of metal promoters can determine their properties. In the end, Pt3Mo is identified as the most active catalyst. The present work not only provides a solid foundation for optimizing Pt catalysts but also provides a clear roadmap to screen metal alloy catalysts.

Palladium and Ruthenium Dual-Single-Atom Sites on Porous Ionic Polymers for Acetylene Dialkoxycarbonylation: Synergetic Effects Stabilize the Active Site and Increase CO Adsorption.

Heterogeneous single-metal-site catalysts usually suffer from poor stability, thereby limiting industrial applications. Dual Pd1 -Ru1 single-atom-sites supported on porous ionic polymers (Pd1 -Ru1 /PIPs) were constructed using a wetness impregnation method. The two isolated metal species in the form of a binuclear complex were immobilized on the cationic framework of PIPs through ionic bonds. Compared to the single Pd- or Ru-site catalyst, the dual single-atom system exhibits higher activity with 98 % acetylene conversion and near 100 % selectivity to dialkoxycarbonylation products, as well as better cycling stability for ten cycles without obvious decay. Based on DFT calculations, it was found that the single-Ru site exhibited a strong CO adsorption energy of -1.6 eV, leading to an increase in the local CO concentration of the catalyst. Notably, the Pd1 -Ru1 /PIPs catalyst had a much lower energy barrier of 2.49 eV compared to 3.87 eV of Pd1 /PIPs for the rate-determining step. The synergetic effect between neighboring single sites Pd1 and Ru1 not only enhanced the overall activity, but also stabilized PdII active sites. The discovery of synergetic effects between single sites can deepen our understanding of single-site catalysts at the molecular level.

Statistical analysis of the performance of a variety of first-principles schemes for accurate prediction of binary semiconductor band gaps.

Standard density functional theory (DFT) approximations tend to strongly underestimate band gaps, while the more accurate GW and hybrid functionals are much more computationally demanding and unsuitable for high-throughput screening. In this work, we have performed an extensive benchmark of several approximations with different computational complexity [G0W0@PBEsol, HSE06, PBEsol, modified Becke-Johnson potential (mBJ), DFT-1/2, and ACBN0] to evaluate and compare their performance in predicting the bandgap of semiconductors. The benchmark is based on 114 binary semiconductors of different compositions and crystal structures, for about half of which experimental band gaps are known. Surprisingly, we find that, compared with G0W0@PBEsol, which exhibits a noticeable underestimation of the band gaps by about 14%, the much computationally cheaper pseudohybrid ACBN0 functional shows a competitive performance in reproducing the experimental data. The mBJ functional also performs well relative to the experiment, even slightly better than G0W0@PBEsol in terms of mean absolute (percentage) error. The HSE06 and DFT-1/2 schemes perform overall worse than ACBN0 and mBJ schemes but much better than PBEsol. Comparing the calculated band gaps on the whole dataset (including the samples with no experimental bandgap), we find that HSE06 and mBJ have excellent agreement with respect to the reference G0W0@PBEsol band gaps. The linear and monotonic correlations between the selected theoretical schemes and experiment are analyzed in terms of the Pearson and Kendall rank coefficients. Our findings strongly suggest the ACBN0 and mBJ methods as very efficient replacements for the costly G0W0 scheme in high-throughput screening of the semiconductor band gaps.

CuCeOx/CuO Catalyst Derived from the Layered Double Hydroxide Precursor: Catalytic Performance in NO Reduction with CO in the Presence of Water and Oxygen.

Valencies of metal species and lattice defects, such as oxygen vacancies, play a pivotal role in metal oxide-catalyzed reactions. Herein, we report a promising synthetic strategy for preparing CuO-supported CuCeOx catalysts (CuCeOx/CuO) by calcination of a hydrotalcite precursor [Cu6Ce2(OH)16]CO3·nH2O. The structural and chemical properties of catalysts were characterized by XRD, ICP-AES, TEM, TPR, NH3-TPD, XPS, Raman spectroscopy, and N2 adsorption, which revealed that the thermal pretreatment in an oxidative atmosphere caused segregation and reconstitution processes of the precursor, resulting in a mesoporous catalyst consisting of well-dispersed CuO-supported CuCeOx clusters of 1.8-3.2 nm in size with a high population of oxygen vacancies. The as-prepared catalyst shows excellent catalytic performance in the reduction of NO by CO in the absence as well as in the presence of water and oxygen. This behavior is attributed to its high oxygen defect concentration facilitating the interplay of the redox equilibria between Cu2+ and reduced copper species (Cu+/Cu0) and (Ce4+/Ce3+). The high surface population of oxygen vacancies and in situ-generated metallic copper species have been evidenced by Raman spectroscopy and X-ray photoelectron spectroscopy. The layered double hydroxide-derived CuCeOx/CuO also showed good water tolerance and long-term stability. In situ infrared spectroscopy investigations indicated that adsorbed hyponitrite species are the main reaction intermediates of the NO conversion as also corroborated by theoretical simulations.

Synthesis and Self-Assembly of Ultrathin Holey Graphdiyne Nanosheets for Oxygen Reduction Reaction.

Graphdiyne (GDY) is a fascinating graphene-like 2D carbon allotrope comprising sp and sp2 hybridized carbon atoms. However, GDY materials synthesized by solution-phase methods normally come as thick and porous films or amorphous powders with severely disordered stacking modes that obstruct macroscopic applications. Here, a facile and scalable synthesis of ultrathin holey graphdiyne (HGDY) nanosheets is reported via palladium/copper co-catalyzed homocoupling of 1,3,5-triethynylbenzene. The resulting freestanding 2D HGDY self-assembles into 3D foam-like networks which can in situ anchor clusters of palladium atoms on their surfaces. The Pd/HGDY hybrids exhibit high electrocatalytic activity and stability for the oxygen reduction reaction which outperforms that of Pt/C benchmark. Based on the ultrathin graphene-like sheets and their unique 3D interconnected macrostructures, Pd/HGDY holds great promise for practical electrochemical catalysis and energy-related applications.

Revealing the Formation Mechanism and Optimizing the Synthesis Conditions of Layered Double Hydroxides for the Oxygen Evolution Reaction.

Layered double hydroxides (LDHs), whose formation is strongly related to OH- concentration, have attracted significant interest in various fields. However, the effect of the real-time change of OH- concentration on LDHs' formation has not been fully explored due to the unsuitability of the existing synthesis methods for in situ characterization. Here, the deliberately designed combination of NH3 gas diffusion and in situ pH measurement provides a solution to the above problem. The obtained results revealed the formation mechanism and also guided us to synthesize a library of LDHs with the desired attributes in water at room temperature without using any additives. After evaluating their oxygen evolution reaction performance, we found that FeNi-LDH with a Fe/Ni ratio of 25/75 exhibits one of the best performances so far reported.

Facile synthesis of red-emissive carbon dots with theoretical understanding for cellular imaging.

Recently, red emissive carbon dots (R-CDs) have drawn widespread attention on account of their desirable fluorescence properties and good biocompatibility. Despite great efforts, facile synthesis of R-CDs for cellular imaging remains challenging and the fluorescence mechanism of R-CDs is still elusive. Herein, p-phenylenediamine-derived R-CDs with excitation-independency were successfully obtained through a facile solvothermal approach together with proportional precipitation. The fluorescent solvatochromism of R-CDs is realized, while high polarity leads to higher degree of dipole interaction between R-CDs and different solvents, favoring for emissive red-shift. Furthermore, density functional theory is adopted to explore the optical and electronic characteristics of some polycyclic aromatic molecules. Among different configurations, pyridine nitrogen and carbonyl bonds could relatively increase the charge density and significantly narrow the band gap, which can provide a crucial theoretical basis for the precise preparation of R-CDs. Moreover, R-CDs possess favorable cellular imaging ability, which indicates their potential for a promising candidate as fluorescence probes in bioimaging.

Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection.

Symbolic regression offers a promising avenue for describing the structure-property relationships of materials with explicit mathematical expressions, yet it meets challenges when the key variables are unclear because of the high complexity of the problems. In this work, we propose to solve the difficulty by automatically searching for important variables from a large pool of input features. A new algorithm that integrates symbolic regression with iterative variable selection (VS) was designed for optimization of the model with a large amount of input features. Using the recent method SISSO for symbolic regression and random search for variable selection, we show that the VS-assisted SISSO (VS-SISSO) can effectively manage even hundreds of input features that the SISSO alone was computationally hindered, and it fastly converges to (near) optimal solutions when the model complexity is not high. The efficiency of this approach for improving the accuracy of symbolic regression in materials science was demonstrated in the two showcase applications of learning approximate equations for the band gap of inorganic halide perovskites and the stability of single-atom alloy catalysts.

Revealing Surface Restraint-Induced Hexagonal Pd Nanocrystals via In Situ Transmission Electron Microscopy.

Achieving metal nanocrystals with metastable phase draws much attention due to their anticipated fascinating properties, wheras it is still challenging because their polymorphism nature and phase transition mechanism remain elusive. Here, phase stability of face-centered cubic (fcc) Pd nanocrystals was studied via in situ spherical aberration (Cs)-corrected transmission electron microscopy (TEM). By constructing a well-defined Pd/C composite structure, Pd nanocrystals encapsulated by graphite, the dispersion process of fcc Pd was observed through a nucleation and growth process. Interestingly, Cs-corrected scanning TEM analysis demonstrated that the newly formed Pd nanocrystals could adopt a metastable hexagonal phase, which was considered challenging to obtain. Accordingly, formation mechanism of the hexagonal Pd nanocrystals was proposed, which involved the combined effect of two factors: (1) templating of graphite and (2) size effect. This work is expected to offer new insight into the polymorphism of Pd nanocrystals and pave the way for the future design of metastable metal nanomaterials.

Adatom Bonding Sites in a Nickel-Fe3 O4 (001) Single-Atom Model Catalyst and O2 Reactivity Unveiled by Surface Action Spectroscopy with Infrared Free-Electron Laser Light.

Single-atom (SA) catalysis presently receives much attention with its promise to decrease the cost of the active material while increasing the catalyst's performance. However, key details such as the exact location of SA species and their stability are often unclear due to a lack of atomic level information. Here, we show how vibrational spectra measured with surface action spectroscopy (SAS) and density functional theory (DFT) simulations can differentiate between different adatom binding sites and determine the location of Ni and Au single atoms on Fe3 O4 (001). We reveal that Ni and Au adatoms selectively bind to surface oxygen ions which are octahedrally coordinated to Fe ions. In addition, we find that the Ni adatoms can activate O2 to superoxide in contrast to the bare surface and Ni in subsurface positions. Overall, we unveil the advantages of combining SAS and DFT for improving the understanding of single-atom catalysts.

Effect of the Configuration of Copper Oxide-Ceria Catalysts in NO Reduction with CO: Superior Performance of a Copper-Ceria Solid Solution.

Various copper-ceria-based composites have attracted attention as efficient catalysts for the reduction of NO with CO. In this comparative study, we have examined the catalytic potential of different configurations of copper oxide-ceria catalysts, including catalysts based on a copper-ceria solid solution, copper oxide particles supported on ceria, and ball-milled copper oxide-ceria. The structurally different interfaces between the constituents of these catalysts afforded very different catalytic performances. The solid solution catalyst outperformed the corresponding ceria-supported and ball-milled CuO-CeO2 catalysts. The copper cations incorporated into the ceria lattice strongly improved the activity, N2 selectivity, and water vapor tolerance compared to the other catalyst configurations. The experimental observations are supported by first-principles density functional theory (DFT) studies of the reaction pathway, which indicate that the incorporation of Cu cations into the ceria matrix lowers the energy required for activating the lattice oxygen, thereby enhancing the formation and healing of oxygen vacancies, and thus promoting NO reduction with CO.