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Doping and alloying are fundamental strategies to improve the thermoelectric performance of bare materials. However, identifying outstanding elements and compositions for the development of high-performance thermoelectric materials is challenging. In this study, we present a data-driven approach to improve the thermoelectric performance of SnSe compounds with various doping. Based on the newly generated experimental and computational dataset, we built highly accurate predictive models of thermoelectric properties of doped SnSe compounds. A well-designed feature vector consisting of the chemical properties of a single atom and the electronic structures of a solid plays a key role in achieving accurate predictions for unknown doping elements. Using the machine learning predictive models and calculated map of the solubility limit for each dopant, we rapidly screened high-dimensional material spaces of doped SnSe and evaluated their thermoelectric properties. This data-driven search provided overall strategies to optimize and improve the thermoelectric properties of doped SnSe compounds. In particular, we identified five dopant candidate elements (Ge, Pb, Y, Cd, and As) that provided a high ZT exceeding 2.0 and proposed a design principle for improving the ZT by Sn vacancies depending on the doping elements. Based on the search, we proposed yttrium as a new high-ZT dopant for SnSe with experimental confirmations. Our research is expected to lead to novel high-ZT thermoelectric material candidates and provide cutting-edge research strategies for materials design and extraction of design principles through data-driven research.

In the present study, we used the electrochemical transparency of graphene to show that the direct intercalation of alkali-metal cations is not a prerequisite for the redox reaction of Prussian blue (PB). PB thin films passivated with monolayer graphene still underwent electrochemical redox reactions in the presence of alkali-metal ions (K

Air pollution is an epochal concern, particularly in urban areas, and is linked to combustion processes 1 . The emission of nitrogen oxides (NOx) constitutes a critical environmental problem, and it can affect severely human health2,3,4 . At ambient temperature and pressure NOx decomposition is thermodynamically favoured; however, this process is kinetically inhibited, owing to a high activation energy5,6 . To date, no reported catalysts have had the required properties to lower the activation energy of this process without the help of coreacting agents and high temperatures7,8,9 . Here, we show that NO conversion to molecular nitrogen can be achieved at room temperature in the presence of O2 and H2O vapour, and in the absence of any further reducing agent, using iron-based Metal-Organic Frameworks (MOFs). Further, we demonstrate that MOFs work similarly to enzymes, but are stable in environments unfriendly to living matter. These findings open large perspectives on the solution of stringent problems in chemistry, such as the removal of pollutants or the activation of highly stable molecules.

Nature materials (2021). doi:10.1038/s41563-021-01064-6, Published by Nature Publishing Group, Basingstoke

Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor ( USE ), which does not require any side information and rigid assumptions on data. Furthermore, USE can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of USE is well justified mathematically, and we also empirically demonstrate the effectiveness of USE for several benchmark datasets.

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C2 compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH4 conversion, C2 yield, and selectivities for C2 and coke) with an error of < 5%. Trained ML models are applied to maximize the C2 yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C2 yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.

The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost. Furthermore, to demonstrate the applicability of our prediction model, we provide a data set of GW band gaps for 45835 materials predicted by TGNN posing higher accuracy than standard density functional theory calculations.

Molecular dynamics (MD) simulations are useful in understanding the interaction between solid materials and molecules. However, performing MD simulations is possible only when interatomic potentials are available and constructing such interatomic potentials usually requires additional computational work. Recently, generating interatomic potentials was shown to be much easier when machine learning (ML) algorithms were used. In addition, ML algorithms require new descriptors for improved performance. Here, we present an ML approach with several categories of atomic descriptors to predict the parameters necessary for MD simulations, such as the potential energies and the atomic forces. We propose several atomic descriptors based on structural information and find that better descriptors can be generated from eXtreme gradient boosting (XGBoost). Moreover, we observe fewer descriptors that perform better in predicting the potential energies and the forces during methane activation processes on a catalytic Pt(111) surface. These results were consistently observed in two different ML algorithms: fully-connected neural network (FNN) and XGBoost. Taking into account the advantages of FNN and XGBoost, we propose an efficient ML model for estimating potential energies. Our findings will be helpful in developing new ML potentials for long-time MD simulations.

Graph neural networks (GNNs) have attracted significant attention from the chemical science community because molecules can be represented as a featured graph. In particular, graph convolutional network (GCN) and its variants have been widely used and have shown a state-of-the-art performance in analyzing molecules, such as molecular label classification, drug discovery, and molecular property prediction. However, in molecular analysis, existing GCNs have two fundamental limitations: (1) information of the molecular scale is distorted and (2) global structures in a molecule are ignored. These limitations can seriously degrade the performance in the machine learning-based molecular analysis because the scale and global structure information of a molecule occasionally have a significant effect on the molecular properties. To overcome the limitations of existing GCNs, we comprehensively analyzed the structure of GCNs and developed a costless solution for the limitations of GCNs. To demonstrate the effectiveness of our solution, extensive experiments were conducted on various benchmark datasets.

Designing and preparing highly active and stable nanostructured Pt-based catalysts with ultralow Pt loading are still challenging for electrochemical applications such as water electrolysis and fuel cells. Here we report for the first time an in situ electrochemical process to synthesize Pt-MoO3−x nanoflakes (NFs) overgrown on commercial bulk MoS2 by employing a facile and simple electrochemical method without using any expensive precious metal salts. The overgrowth of Pt-MoO3−x NFs on the bulk MoS2 surface is conducted by applying electrical energy to the bulk MoS2 and using Pt counter electrode dissolution in an acidic solution. In spite of their 10 times lower Pt loadings compared to commercial Pt black (Alfa Aesar), the synthesized Pt-MoO3−x NFs demonstrate excellent catalytic performance with a Pt mass activity of 2.83 A/mgPt at the overpotential of 100 mV for electrochemical hydrogen evolution reaction (HER), an approximately 4 times higher value than the value of 0.76 A/mgPt at the overpotential of 100 mV for commercial Pt black. We hypothesize that the outstanding HER characteristics of Pt-MoO3−x NFs are related to the existence and increase of Pt-MoO3 interfacial sites and oxygen vacancy sites such as Mo5+ in the Pt-MoO3−x NF structures. In addition, our density functional theory (DFT) calculations demonstrate that Pt and O sites at Pt and MoO3 interfaces and O sites at defective MoO3−x in the Pt-MoO3−x NFs contribute to accelerate the HER.

Atomic layer deposition (ALD) is an efficient technique that allows atomic-level surface control of metal catalysts for the design and development of electrocatalytic materials. Herein, we report a strategy for efficient catalyst design using a particle ALD method to enhance the electrocatalytic glycerol-oxidation-reaction (GOR) performance. Atomically controlled thin SnO2 layers were deposited on a carbon-supported Pt nanoparticle (Pt/C) surface using the ALD technique. The resulting SnO2 overcoated Pt/C (ALD(SnO2)–Pt/C) was then heat-treated at 400 °C under a N2 atmosphere. The onset potential as a kinetic parameter decreased with ALD (SnO2) coatings. The turnover frequency (TOF) for the GOR showed similar values for the tested samples (TOF of Pt/C: 74.86 h−1 and TOF of ALD(SnO2)–Pt/C: 91.29 h−1). Interestingly, interactions between the ALD SnO2 overcoating and Pt nanoparticles improved the catalytic stability for the GOR, preventing sintering of Pt nanoparticle catalysts. This demonstrates that an ALD SnO2 coating on defect sites of Pt can diminish Pt sintering for the GOR. From the GOR in an electrochemical batch reactor, the ALD(SnO2)–Pt/C catalyst also generated more glyceraldehyde (GAD) product than uncoated Pt/C at a similar glycerol conversion level. The density functional theory (DFT) calculations suggest that the binding energies of glycerol and reaction intermediates change at the interface of the SnO2-coated Pt surface compared to those at the Pt surface only, thus affecting the reaction chemistry for the electrocatalytic GOR. This work highlights how we can control reaction performance measures such as catalytic stability and product selectivity by using the particle ALD technique for electrocatalytic reactions such as glycerol oxidation.

The fundamental goal of machine learning (ML) in physical science is to predict the physical properties of unobserved states. However, an accurate predictionfor input data outside of training distributions is a challenging problem in ML due to nonlinearities of input and target dynamics. For an accurate extrapolation of ML algorithms, we propose a new data-driven method that encodes nonlinearities of physical systems into input representations. Based on the proposed encoder, a given physical system is described as linear-like functions that are easy to extrapolate. By applying the proposed encoder, the extrapolation errors were significantly reduced by 48.39% and 40.04% in an n-body problem and a prediction of materials properties, respectively.

Contact engineering for monolayered transition metal dichalcogenides (TMDCs) is considered to be of fundamental challenge for realizing high-performance TMDCs-based (opto) electronic devices. Here, an innovative concept is established for a device configuration with metallic copper monosulfide (CuS) electrodes that induces sulfur vacancy healing in the monolayer molybdenum disulfide (MoS

The chlorination of methane (CH4) is an attractive route to convert CH4 into more valuable chemicals. The selective formation of methyl chloride (CH3Cl) is a key process, but it is rather difficult to achieve with high selectivity due to a radical reaction. Catalytic ionic processes can be a solution. Herein, sulfated tin oxide (STO) was employed in the gas-phase catalytic chlorination of CH4. The STO catalyst exhibited high selectivity to CH3Cl (>96%) even at high CH4 conversion. By applying a suite of physicochemical characterizations, it is shown that the strong Lewis acid sites on STO generated by the interaction of Sn and surface sulfate groups are mainly responsible for the highly selective CH4 conversion. DFT calculations further revealed that STO surface can activate more Cl2 molecules in a heterolytic manner, leading to better catalytic performances as compared to SnO2 and sulfated zirconia catalysts.

Piezoelectric two-dimensional (2D) transition-metal dichalcogenides such as molybdenum disulfide (MoS2) have recently attracted significant attention owing to their applicability for fabrication of flexible power generators. In this study, novel piezoelectric nanogenerators (PNGs) consisting of 2D piezoelectric MoS2 shells are fabricated where an Al2O3 thin layer deposited on the surface of polystyrene (PS) beads is used to avoid collapse of the spherical MoS2 shells under the high growth temperature. In addition, the MoS2 shell size is controlled by adjusting the PS bead size and the effects of the MoS2 shell size on power generation characteristics are investigated. Our PNG based on the piezoelectric MoS2 shells produces a peak output voltage of approximately 1.2 V at a pressure of 4.2 kPa. The minimum pressure for power generation by tapping is 0.3 kPa. This novel method is very promising for development of the next-generation PNGs based on 2D semiconductor piezoelectric materials.

Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites.

Finding new phosphors through an efficient method is important in terms of saving time and cost related to the development of phosphor materials. The ability to identify new phosphors through preliminary simulations by calculations prior to the actual synthesis of the materials can maximize the efficiency of novel phosphor development. In this paper, we demonstrate the use of density functional theory (DFT) calculations to guide the development of a new red phosphor. We performed first-principles calculations based on DFT for pristine and Mn-doped RbxK3−xSiF7 (x = 0, 1, 2, 3) and predicted their stability, electronic structure, and luminescence properties. On the basis of the results, we then synthesized the stable Rb2KSiF7:Mn4+ red conversion phosphor and investigated its luminescence, structure, and stability. As a result, we confirmed that Rb2KSiF7:Mn4+ emitted red light with a longer wavelength than that emitted by K3SiF7:Mn4+ and a wavelength similar to that of K2SiF6:Mn4+. These results show that DFT calculations can provide rational insights into the design of a phosphor material before it is synthesized, thereby reducing the time and cost required to develop new red conversion phosphors.

We report on the modulation of the electrical properties of graphene-based transistors that mirror the properties of a few nanometers thick layer made of dipolar molecules sandwiched in between the 2D material and the SiO2 dielectric substrate. The chemical composition of the films of quinonemonoimine zwitterion molecules adsorbed onto SiO2 has been explored by means of X-ray photoemission and mass spectroscopy. Graphene-based devices are then fabricated by transferring the 2D material onto the molecular film, followed by the deposition of top source-drain electrodes. The degree of supramolecular order in disordered films of dipolar molecules was found to be partially improved as a result of the electric field at low temperatures, as revealed by the emergence of hysteresis in the transfer curves of the transistors. The use of molecules from the same family, which are suitably designed to interact with the dielectric surface, results in the disappearance of the hysteresis. DFT calculations confirm that the dressing of the molecules by an external electric field exhibits multiple minimal energy landscapes that explain the thermally stabilized capacitive coupling observed. This study demonstrates that the design and exploitation of ad hoc molecules as an interlayer between a dielectric substrate and graphene represents a powerful tool for tuning the electrical properties of the 2D material. Conversely, graphene can be used as an indicator of the stability of molecular layers, by providing insight into the energetics of ordering of dipolar molecules under the effect of electrical gating.

Machine learning (ML) has increasingly been of interest in the design of new materials. However, it is still challenging to exploit an ML model in this field because its performance highly depends on the representation of materials, its properties, and the amount of data. In this study, for the cases of prediction of properties of crystalline materials, we explore a systematic comparison of two state-of-the-art frameworks: Crystal Graph Convolutional Neural Networks (CGCNNs) and the Sure Independence Screening and Sparsifying Operator (SISSO). The common key advantage of these two models is the fact that painstakingly handcrafted descriptors from simple material properties are not required. The main differences between the two models are (1) the use of structure information in the arbitrary size of compounds (CGCNN) and (2) limited interpretability (CGCNN) but simple and analytic relations between descriptor-property (SISSO). Using these two ML algorithms we evaluate the prediction performance on the target properties, which are band gap, formation energy, and elasticity of crystalline compounds in the database of Materials Project (MP). Moreover, to improve prediction of the properties of the materials without human bias in the selection of initial atomic features for the CGCNNs, we use Atom2Vec that provides atom representation obtained in an unsupervised manner from the materials. We also perform the predictions with the different sizes of training set to investigate the data-size dependency of the predictive models. According to the amount of dataset, the use of structural information, and the ability to identify the best descriptor with its interpretability, these algorithms showed different prediction performances. This result will enable researchers in materials discovery to gain appropriate choices and insights in various attempts to improve the prediction performance of crystalline materials’ properties.

With great growth in the volume of chemical databases, machine learning receives significant attention from various scientific communities for efficient high-throughput screening of molecular properties and drug discovery on the millions of chemical compounds. In particular, graph neural networks (GNNs) have been widely studied in chemistry-related fields because a molecule is natively represented as a mathematical graph. In GNNs for the graph-level analysis, a global operation called readout is applied after node embedding to generate a graph-level embedding that represents characteristics of the whole graph. However, commonly used readouts frequently distort scale information of the graph and consequently degrade the prediction accuracy of GNNs. This problem becomes more serious in molecular machine learning because molecules have many important scale information (e.g., molecular weight and total energy). In this paper, we investigate this scale distortion problem in GNNs caused by the readouts for the first time and propose an efficient solution with a new attention-based readout. In the experiments, the proposed readout outperformed commonly used readouts on various GNN architectures.

To construct an efficient electrocatalyst for HER, the high binding energy of MXene must be mitigated through electronic modulation of active sites. Here, we propose atomic Pt substitution in V2CTx MXene to modulate the electronic structure and promote catalytic activity toward HER. Pt–V2CTx exhibits high HER performance with a low overpotential of 27 mV at –10 mA cm-2 in acidic media, comparable to the commercial Pt/C catalyst. X-ray absorption spectroscopy and DFT calculations indicate that the Pt atoms are efficiently confined to the V vacancy sites of V2CTx, accompanied by a unique electronic structure. The atomic substitution of Pt with higher occupied d states at the Fermi energy of Pt and surface oxygen sites can significantly indicate an optimum hydrogen-binding free energy (∆GH*), promoting HER performance. This work introduces further prospects for developing efficient electrocatalysts by feasible electronic regulation and highly improved catalytic activity through rational atomic engineering.

Air pollution is an epochal concern, particularly in urban areas, and is linked to combustion processes 1 . The emission of nitrogen oxides (NOx) constitutes a critical environmental problem, and it can affect severely human health2,3,4 . At ambient temperature and pressure NOx decomposition is thermodynamically favoured; however, this process is kinetically inhibited, owing to a high activation energy5,6 . To date, no reported catalysts have had the required properties to lower the activation energy of this process without the help of coreacting agents and high temperatures7,8,9 . Here, we show that NO conversion to molecular nitrogen can be achieved at room temperature in the presence of O2 and H2O vapour, and in the absence of any further reducing agent, using iron-based Metal-Organic Frameworks (MOFs). Further, we demonstrate that MOFs work similarly to enzymes, but are stable in environments unfriendly to living matter. These findings open large perspectives on the solution of stringent problems in chemistry, such as the removal of pollutants or the activation of highly stable molecules.

Silver sulfide nanoparticles (Ag2S NPs) are currently being explored as infrared active nanomaterials that can provide environmentally stable alternatives to heavy metals such as lead. In this paper, we describe the novel synthesis of Ag2S NPs by using a sonochemistry method and the fabrication of photodetector devices through the integration of Ag2S NPs atop a graphene sheet. We have also synthesized Li-doped Ag2S NPs that exhibited a significantly enhanced photodetector sensitivity via their enhanced absorption ability in the UV-NIR region. First-principles calculations based on a density functional theory formalism indicated that Li-doping produced a dramatic enhancement of NIR photoluminescence of the Ag2S NPs. Finally, high-performance photodetectors based on CVD graphene and Ag2S NPs were demonstrated and investigated; the hybrid photodetectors based on Ag2S NPs and Li-doped Ag2S NPs exhibited a photoresponse of 2723.2 and 4146.0 A W−1 respectively under a light exposure of 0.89 mW cm−2 at 550 nm. Our novel approach represents a promising and effective method for the synthesis of eco-friendly semiconducting NPs for photoelectric devices.

Decorating graphene with nanoparticles is an effective method for improving gas selectivity and sensitivity of graphene-based chemical sensors. We report herein the enhancement of the gas selectivity and improved response of a graphene-based chemical sensor by decorating the graphene with synthesized Ag2S nanoparticles. To synthesize uniformly sized Ag2S nanoparticles, we used the ultrasonic irradiation method, and then the synthesized Ag2S nanoparticles were decorated onto graphene uniformly, by using a simple spin coating method. Gas responses of the resulting chemical sensor were tested for volatile organic compounds (VOCs) such as acetone, ethanol, and hexane. While no noticeable gas response changes were obtained for ethanol and hexane vapors, a dramatic increase of ~660% resulted from exposure of the sensor to acetone vapor. In order to determine the mechanism behind the excellent acetone response of graphene decorated with Ag2S nanoparticles, density functional theory (DFT) calculations were performed, and showed higher binding energies and electron transfer between Ag2S and acetone than between Ag2S and the other VOCs. This result indicated that decorating graphene with nanoparticles displaying a high binding energy for the target gas is an efficient way to improve the gas selectivity and response levels of graphene-based chemical sensors.

Contact engineering for monolayered transition metal dichalcogenides (TMDCs) is considered to be of fundamental challenge for realizing high-performance TMDCs-based (opto) electronic devices. Here, an innovative concept is established for a device configuration with metallic copper monosulfide (CuS) electrodes that induces sulfur vacancy healing in the monolayer molybdenum disulfide (MoS2 ) channel. Excess sulfur adatoms from the metallic CuS electrodes are donated to heal sulfur vacancy defects in MoS2 that surprisingly improve the overall performance of its devices. The electrode-induced self-healing mechanism is demonstrated and analyzed systematically using various spectroscopic analyses, density functional theory (DFT) calculations, and electrical measurements. Without any passivation layers, the self-healed MoS2 (photo)transistor with the CuS contact electrodes show outstanding room temperature field effect mobility of 97.6 cm2 (Vs)-1 , On/Off ratio > 108 , low subthreshold swing of 120 mV per decade, high photoresponsivity of 1 × 104 A W-1 , and detectivity of 1013 jones, which are the best among back-gated transistors that employ 1L MoS2 . Using ultrathin and flexible 2D CuS and MoS2 , mechanically flexible photosensor is also demonstrated, which shows excellent durability under mechanical strain. These findings demonstrate a promising strategy in TMDCs or other 2D material for the development of high performance and functional devices including self-healable sulfide electrodes.

Thin TiO2 layers were deposited onto a carbon-supported Ni catalyst (Ni/C) through atomic layer deposition (ALD) and the resulting TiO2-coated Ni/C (ALD(TiO2)-Ni/C) was utilized for electrochemical glycerol oxidation in alkaline media. X-ray photoelectron spectroscopy analysis demonstrated that the Ni surface phase of ALD(TiO2)-Ni/C mainly consisted of Ni(OH)2 while that of uncoated Ni/C was a mixed phase of NiO and Ni(OH)2. The ALD(TiO2)-Ni/C exhibited electrocatalytic activity at least 2.4 times higher than that of Ni/C. Density functional theory calculations were used to investigate how the modified Ni surface with the TiO2 coating affects the adsorption/desorption of glycerol. Keywords: Nickel, Titanium dioxide, Atomic layer deposition, Electrocatalytic oxidation, Glycerol, Alkaline electrolyte

As a consequence of their unique electronic band structures, low-dimensional materials such as one-dimensional (1D) single-wall carbon nanotubes (SWCNTs), 2D graphene and various 2D transition-metal dichalcogenides have exhibited intriguing electrical transport properties when incorporated into field-effect transistors. Meanwhile, the van-der-Waals (vdW) interfaces between top-contacted metals and low-dimensional materials have become a challenging issue for future high-performance electronics. Here, we report a new aspect of vdW interface that offers vdW-gap tunneling spectroscopy by adopting indium (In) as a top-contacted metal on SWCNTs without an artificial insulating tunnel barrier. We show that multiple differential conductance peaks for varying bias voltages correspond to the van Hove singularities existing in the electronic density of states of SWCNTs. Our first-principles calculations reveal that In forms a physisorption interface with a considerable vdW gap, which causes little disruption to the density of states of the SWCNTs near the metal interface and which thus allows vdW-gap tunneling spectroscopy.

A series of binary PtRu catalysts with different Pt:Ru atomic ratios in a range from 7:3 to 3:7 are synthesized on carbon support using the colloidal method; they are then used for electrooxidation of glycerol in acidic media. X-ray diffraction, transmission electron microscopy, X-ray photoelectron spectroscopy, and X-ray absorption spectroscopy analyses are used to investigate the particle size, size distribution, and structural and electronic properties of the prepared catalysts. The addition of Ru content to the Pt-based catalysts caused structural and electronic modifications over the PtRu alloy catalyst formation. The electrocatalytic activities of PtRu/C series catalysts are investigated by using the cyclic voltammetry technique. Among the prepared catalysts, the Pt5Ru5/C catalyst shows enhanced catalytic activity at least 40% higher than that of the Pt/C catalyst, with improved stability for glycerol electrooxidation; these improvements can be attributed to the structural and electronic modifications of the Pt catalysts. Using an electrocatalytic batch reactor, product analysis after the oxidation reaction is performed by high-performance liquid chromatography to determine and compare the reaction pathways on the Pt/C and PtRu/C catalysts. To understand different catalytic activities of glycerol oxidation on the PtRu alloy surfaces, density functional theory (DFT) calculations have been performed and discussed with the experimental results.

Selective dinitrogen binding to transition metal ions mainly covers two strategic domains: biological nitrogen fixation catalysed by metalloenzyme nitrogenases, and adsorptive purification of natural gas and air. Many transition metal-dinitrogen complexes have been envisaged for biomimetic nitrogen fixation to produce ammonia. Inspired by this concept, here we report mesoporous metal-organic framework materials containing accessible Cr(III) sites, able to thermodynamically capture N2 over CH4 and O2. This fundamental study integrating advanced experimental and computational tools confirmed that the separation mechanism for both N2/CH4 and N2/O2 gas mixtures is driven by the presence of these unsaturated Cr(III) sites that allows a much stronger binding of N2 over the two other gases. Besides the potential breakthrough in adsorption-based technologies, this proof of concept could open new horizons to address several challenges in chemistry, including the design of heterogeneous biomimetic catalysts through nitrogen fixation.

Semiconductor gas sensors are advantageous in miniaturization and can be used in a wide range of applications, yet consume large power due to high operating temperature. Here we demonstrated the ability of nanoscale scratches produced with mechanical abrasion to enhance the chemical sensitivity of thin-film-type semiconductor sensors. Well-aligned arrays of scratches parallel to the electrical current direction between the source and drain electrodes were made, using typical polishing machines with diamond suspensions, on semiconductor thin films produced with various deposition methods such as atomic layer deposition (ALD), sputtering, and the sol–gel technique. Processing with sharp diamond microparticles left nano-grooves on the surface, together with changes in chemical composition. For all of the tested metal oxide thin films, the introduction of scratches yielded increased quantities of oxygen vacancies and metallic components. Scratched ZnO devices exhibited superior performance even at room temperature, as predicted by a computational simulation that showed increased binding energy of gas molecules on defects. The scratch technique shown in the present study may be used to produce dense arrays of nanometer-scale, chemically functionalized line patterns on substrates larger than a few tens of centimeters with minimum cost, which in turn may be used in a variety of applications including massive arrays of sensors displaying high sensitivity.

Finding new phosphors through an efficient method is important in terms of saving time and cost related to the development of phosphor materials. The ability to identify new phosphors through preliminary simulations by calculations prior to the actual synthesis of the materials can maximize the efficiency of novel phosphor development. In this paper, we demonstrate the use of density functional theory (DFT) calculations to guide the development of a new red phosphor. We performed first-principles calculations based on DFT for pristine and Mn-doped Rb

Atomically controlled thin TiO2 layers were coated onto a carbon-supported Pt nanoparticle (Pt/C) surface using an atomic layer deposition (ALD) technique. Then, the resulting TiO2 coated Pt/C (ALD(TiO2)-Pt/C) was heat-treated at 150 °C under N2 atmosphere. The heat-treated ALD(TiO2)-Pt/C (ALD(TiO2)-Pt/C(150 H T)) catalyst demonstrated improved electrocatalytic glycerol oxidation reaction (GOR) activity with a turnover frequency (TOF) 3-times higher than for uncoated Pt/C catalyst. Interestingly, the ALD(TiO2)-Pt/C(150 H T) catalyst maintained its catalytic GOR performance after a cyclic voltammetry (CV) stability test of 1200 cycles, preventing Pt sintering due to strong Pt-TiO2 interaction. The reaction chemistry for GOR on the ALD(TiO2)-Pt/C(150 H T) catalyst also changed due to the newly created Pt-TiO2 interfaces, producing more oxidized chemicals such as glyceric acid and glycolic acid. The enhanced GOR performance is related to the formation of new Pt-O-Ti bonds and to change in the physicochemical properties of the Pt by interaction between the Pt nanoparticles and ALD-coated TiO2.

Eu-doped CaAlSiN3 (CASN) is widely utilized as an efficient red phosphor; however, the high price of rare-earth metals has driven efforts toward finding non-rare-earth metal dopants. This paper reports first-principles calculations based on density functional theory (DFT) and geared toward identifying new non-rare-earth metal dopants for use in the CASN-based phosphors. We calculated the formation energies, the electronic structures, and the optical absorption spectra of various metal dopants (Eu, Mn, Sn, and Bi) in CASN. The calculated density of states, band structures, and absorption spectra were consistent with previous experimental observations obtained from Eu- and Mn-doped CASN. The DFT calculations suggested that Sn and Bi are promising candidates as non-rare-earth metal dopants in CASN-based phosphors. Our calculations demonstrate that DFT-based first-principles calculations provide a viable tool for finding new phosphor materials.

Si nanocrystal (NC)/SiO2 multilayers containing interfacial nitrogens are formed by radiofrequency magnetron-sputtering and post-annealing. By analyzing the photoluminescence (PL) of the multilayer structures after proton irradiation (PI), we found that the peak at ∼740 nm was shifted toward shorter wavelengths and that its intensity was considerably suppressed. Furthermore, a new peak simultaneously appeared at ∼500 nm. We interpret that PI not only modifies the shapes of the Si NCs and generate defects at the NC/SiO2 interface, but also induces simultaneously hydrogen passivation (HP) of interfacial nitrogens due to the attenuated protons. To investigate the relationship between the HP of N at the Si NC/SiO2 interface and the observed band gap reduction of Si NC structures within the SiO2 matrix, we modeled the atomic configurations at the interface and applied first-principles calculations. The results clearly indicate that the HP of composite structures containing Si–N bonds at the Si NC/SiO2 interface induces the blue-shift in PL. We expect that this investigation helps to pave the way for developing more efficient Si-based light emitting devices or solar cells.

We present an interesting phenomenon, "atomic force masking", which is the deposition of a few-nanometer-thick gold film on ultrathin low-molecular-weight (LMW) polydimethylsiloxane (PDMS) engineered on a polycrystalline gold thin film, and demonstrated the formation of hot spot based on SERS. The essential principle of this atomic force masking phenomenon is that an LMW PDMS layer on a single crystalline grain of gold thin film would repel gold atoms approaching this region during a second cycle of evaporation, whereas new nucleation and growth of gold atoms would occur on LMW PDMS deposited on grain boundary regions. The nanostructure formed by the atomic force masking, denoted here as "hot spots on grain boundaries" (HOGs), which is consistent with finite-difference time-domain (FDTD) simulation, and the mechanism of atomic force masking were investigated by carrying out systematic experiments, and density functional theory (DFT) calculations were made to carefully explain the related fundamental physics. Also, to highlight the manufacturing advantages of the proposed method, we demonstrated the simple synthesis of a flexible HOG SERS, and we used this substrate in a swabbing test to detect a common pesticide placed on the surface of an apple.

Despite many encouraging properties of transition metal dichalcogenides (TMDs), a central challenge in the realm of industrial applications based on TMD materials is to connect the large-scale synthesis and reproducible production of highly crystalline TMD materials. Here, the primary aim is to resolve simultaneously the two inversely related issues through the synthesis of MoS2(1-x) Se2x ternary alloys with customizable bichalcogen atomic (S and Se) ratio via atomic-level substitution combined with a solution-based large-area compatible approach. The relative concentration of bichalcogen atoms in the 2D alloy can be effectively modulated by altering the selenization temperature, resulting in 4 in. scale production of MoS1.62 Se0.38 , MoS1.37 Se0.63 , MoS1.15 Se0.85 , and MoS0.46 Se1.54 alloys, as well as MoS2 and MoSe2 . Comprehensive spectroscopic evaluations for vertical and lateral homogeneity in terms of heteroatom distribution in the large-scale 2D TMD alloys are implemented. Se-stimulated strain effects and a detailed mechanism for the Se substitution in the MoS2 crystal are further explored. Finally, the capability of the 2D alloy for industrial application in nanophotonic devices and hydrogen evolution reaction (HER) catalysts is validated. Substantial enhancements in the optoelectronic and HER performances of the 2D ternary alloy compared with those of its binary counterparts, including pure-phase MoS2 and MoSe2 , are unambiguously achieved.