Theoretical Insight into the Band Alignment at High-κ Oxide XO2/Diamond (X = Hf and Zr) Interfaces with a SiO2 Interlayer for MOS Devices.

Diamond has become a promising candidate for high-power devices based on its ultrawide bandgap and excellent thermoelectric properties, where an appropriate gate dielectric has been a bottleneck hindering the development of diamond devices. Herein, we have systematically investigated the structural arrangement and electronic properties of diamond/high-κ oxide (HfO2, ZrO2) heterojunctions by first-principles calculations with a SiO2 interlayer. Charge analysis reveals that the C-Si bonding interface attracts a large amount of charge concentrated at the diamond interface, indicating the potential for the formation of a 2D hole gas (2DHG). The diamond/HfO2 and diamond/ZrO2 heterostructures exhibit similar "Type II" band alignments with VBOs of 2.47 and 2.21 eV, respectively, which is consistent with experimental predictions. The introduction of a SiO2 dielectric layer into the diamond/SiO2/high-κ stacks exhibits the typical "Type I″ straddling band offsets (BOs). In addition, the wide bandgap SiO2 interlayer keeps the valence band maximum (VBM) and conduction band minimum (CBM) in the stacks away from those of diamond, effectively confining the electrons and holes in MOS devices. This work exhibits the potential of SiO2/high-κ oxide gate dielectrics for diamond devices and provides theoretical insights into the rational design of high-quality gate dielectrics for diamond-based MOS device applications.

Enhanced Electrochemical CO2 Reduction to Formate over Phosphate-Modified In: Water Activation and Active Site Tuning.

Electrochemical CO2 reduction reaction (CO2RR) offers a sustainable strategy for producing fuels and chemicals. However, it suffers from sluggish CO2 activation and slow water dissociation. In this work, we construct a (P-O)δ- modified In catalyst that exhibits high activity and selectivity in electrochemical CO2 reduction to formate. A combination of in situ characterizations and kinetic analyses indicate that (P-O)δ- has a strong interaction with K+(H2O)n, which effectively accelerates water dissociation to provide protons. In situ attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) measurements together with density functional theory (DFT) calculations disclose that (P-O)δ- modification leads to a higher valence state of In active site, thus promoting CO2 activation and HCOO* formation, while inhibiting competitive hydrogen evolution reaction (HER). As a result, the (P-O)δ- modified oxide-derived In catalyst exhibits excellent formate selectivity across a broad potential window with a formate Faradaic efficiency as high as 92.1 % at a partial current density of ~200 mA cm-2 and a cathodic potential of -1.2 V vs. RHE in an alkaline electrolyte.

Synthesis of Ultrathin High-Entropy Oxides with Phase Controllability.

High-entropy oxides (HEOs) with an ultrathin geometric structure are especially expected to exhibit extraordinary performance in different fields. The phase structure is deemed as a key factor in determining the properties of HEOs, rendering their phase control synthesis tempting. However, the disparity in intrinsic phase structures and physicochemical properties of multiple components makes it challenging to form single-phase HEOs with the target phase. Herein, we proposed a self-lattice framework-guided strategy to realize the synthesis of ultrathin HEOs with desired phase structures, including rock-salt, spinel, perovskite, and fluorite phases. The participation of the Ga assistor was conducive to the formation of the high-entropy mixing state by decreasing the formation energy. The as-prepared ultrathin spinel HEOs were demonstrated to be an excellent catalyst with high activity and stability for the oxygen evolution reaction in water electrolysis. Our work injects new vitality into the synthesis of HEOs for advanced applications and undoubtedly expedites their phase engineering.

Experimental Study of Compatibility between the Eco-friendly Insulation Mixed Gas CF3SO2F/N2 and EPDM and CR Materials.

As a greenhouse gas with strong global warming potential, the use of SF6 needs to be reduced as much as possible. Researching environmentally friendly insulation (EFI) gases to replace SF6 in power electrical equipment is an effective way to reduce its usage. CF3SO2F/N2, as a newly proposed EFI gas, has certain potential to replace SF6. Compatibility of CF3SO2F/N2 gas with rubber sealing materials commonly used in electrical equipment is still unknown. In this article, the compatibility of CF3SO2F/N2 with the ethylene-propylene-diene monomer (EPDM) and chloroprene rubber (CR) was investigated experimentally. It was found that CF3SO2F/N2 would slightly decompose under the influence of EPDM and CR rubber under certain conditions. The surface morphology of EPDM changed slightly under the influence of CF3SO2F/N2, and it was similar to the influence of SF6. While the surface morphology of CR deteriorated significantly with obvious defects. The mechanical properties of EPDM were not significantly affected by CF3SO2F, which is similar to the influence of SF6. But CR was affected greatly by CF3SO2F gas. Permanent deformation compression and surface morphology are two effective indicators for characterizing the compatibility between gas and rubber sealing materials. This research provides a reference for the application of CF3SO2F/N2 as a new EFI gas in power equipment.

Heterometallic Electrocatalysts Derived from High-Nuclearity Metal Clusters for Efficient Overall Water Splitting.

The development of cost-effective electrocatalysts with an optimal surface affinity for intermediates is essential for sustainable hydrogen fuel production, but this remains insufficient. Here we synthesize Ni2P/MoS2-CoMo2S4@C heterometallic electrocatalysts based on the high-nuclearity cluster {Co24(TC4A)6(MoO4)8Cl6}, in which Ni2P nanoparticles were anchored to the surface of the MoS2-CoMo2S4@C nanosheets via strong interfacial interactions. Theoretical calculations revealed that the introduction of Ni2P phases induces significant disturbances in the surface electronic configuration of Ni2P/MoS2-CoMo2S4@C, resulting in more relaxed d-d orbital electron transfers between the metal atoms. Moreover, continuous electron transport was established by the formation of multiple heterojunction interfaces. The optimized Ni2P/MoS2-CoMo2S4@C electrocatalyst exhibited ultralow overpotentials of 198 and 73 mV for oxygen and hydrogen evolution reactions, respectively, in alkaline media, at 10 mA cm-2. The alkali electrolyzer constructed using Ni2P/MoS2-CoMo2S4@C required a cell voltage of only 1.45 V (10 mA cm-2) to drive overall water splitting with excellent long-term stability.

Unveiling Atomic-Scale Product Selectivity at the Cocatalyst-TiO2 Interface Using X-Ray Techniques: Insights into Interface Reactivity.

The microstructure at the interface between the cocatalyst and semiconductor plays a vital role in concentrating photo-induced carriers and reactants. However, observing the atomic arrangement of this interface directly using an electron microscope is challenging due to the coverings of the semiconductor and cocatalyst. To address this, multiple metal-semiconductor interfaces on three TiO2 crystal facets (M/TiO2 ─N, where M represents Ag, Au, and Pt, and N represents the 001, 010, and 101 single crystal facets). The identical surface atomic configuration of the TiO2 facets allowed us to investigate the evolution of the microstructure within these constructs using spectroscopies and DFT calculations. For the first time, they observed the transformation of saturated Ti6c ─O bonds into unsaturated Ti5c ─O and Ti6c ─O─Pt bonds on the TiO2 ─010 facet after loading Pt. This transformation have a direct impact on the selectivity of the resulting products, leading to the generation of CO and CH4 at the Ti6c ─O─Pt and Pt sites, respectively. These findings pinpoint the pivotal roles played by the atomic arrangement at the M/TiO2 ─N interfaces and provide valuable insights for the development of new methodologies using conventional lab-grade equipment.

Cascade NH3 Oxidation and N2O Decomposition via Bifunctional Co and Cu Catalysts.

The selective catalytic oxidation of NH3 (NH3-SCO) to N2 is an important reaction for the treatment of diesel engine exhaust. Co3O4 has the highest activity among non-noble metals but suffers from N2O release. Such N2O emissions have recently been regulated due to having a 300× higher greenhouse gas effect than CO2. Here, we design CuO-supported Co3O4 as a cascade catalyst for the selective oxidation of NH3 to N2. The NH3-SCO reaction on CuO-Co3O4 follows a de-N2O pathway. Co3O4 activates gaseous oxygen to form N2O. The high redox property of the CuO-Co3O4 interface promotes the breaking of the N-O bond in N2O to form N2. The addition of CuO-Co3O4 to the Pt-Al2O3 catalyst reduces the full NH3 conversion temperature by 50 K and improves the N2 selectivity by 20%. These findings provide a promising strategy for reducing N2O emissions and will contribute to the rational design and development of non-noble metal catalysts.

In-Situ-Grown Cu Dendrites Plasmonically Enhance Electrocatalytic Hydrogen Evolution on Facet-Engineered Cu2 O.

Herein, facet-engineered Cu2 O nanostructures are synthesized by wet chemical methods for electrocatalytic HER, and it is found that the octahedral Cu2 O nanostructures with exposed crystal planes of (111) (O-Cu2 O) has the best hydrogen evolution performance. Operando Raman spectroscopy and ex-situ characterization techniques showed that Cu2 O is reduced during HER, in which Cu dendrites are grown on the surface of the Cu2 O nanostructures, resulting in the better HER performance of O-Cu2 O after HER (O-Cu2 O-A) compared with that of the as-prepared O-Cu2 O. Under illumination, the onset potential of O-Cu2 O-A is ca. 52 mV positive than that of O-Cu2 O, which is induced by the plasmon-activated electrochemical system consisting of Cu2 O and the in-situ generated Cu dendrites. Incident photon-to-current efficiency (IPCE) measurements and the simulated UV-Vis spectrum demonstrate the hot electron injection (HEI) from Cu dendrites to Cu2 O. Ab initio nonadiabatic molecular dynamics (NAMD) simulations revealed the transfer of photogenerated electrons (27 fs) from Cu dendrites to Cu2 O nanostructures is faster than electron relaxation (170 fs), enhancing its surface plasmons activity, and the HEI of Cu dendrites increases the charge density of Cu2 O. These make the energy level of the catalyst be closer to that of H+ /H2 , evidenced by the plasmon-enhanced HER electrocatalytic activity.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook.

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

Engineering Atomic-Scale Patterning and Resistive Switching in 2D Crystals and Application in Image Processing.

The ultrathin thickness of 2D layered materials affords the control of their properties through defects, surface modification, and electrostatic fields more efficiently compared with bulk architecture. In particular, patterning design, such as moiré superlattice patterns and spatially periodic dielectric structures, are demonstrated to possess the ability to precisely control the local atomic and electronic environment at large scale, thus providing extra degrees of freedom to realize tailored material properties and device functionality. Here, the scalable atomic-scale patterning in superionic cuprous telluride by using the bonding difference at nonequivalent copper sites is reported. Moreover, benefitting from the natural coupling of ordered and disordered sublattices, controllable piezoelectricity-like multilevel switching and bipolar switching with the designed crystal structure and electrical contact is realized, and their application in image enhancement is demonstrated. This work extends the known classes of patternable crystals and atomic switching devices, and ushers in a frontier for image processing with memristors.

Active Learning the High-Dimensional Transferable Hubbard U and V Parameters in the DFT + U + V Scheme.

Density functional theory (DFT) is a powerful quantum mechanical computational tool to perform electronic structure calculations for materials. Few DFT methods can ensure accuracy and efficiency simultaneously. DFT + U + V is an alternative effective approach to overcome this drawback. However, the accuracy sensitively depends on the self-consistent estimation of the high-dimensional onsite and intersite Hubbard interaction U and V terms. We propose Bayesian optimization using a dropout (BOD) algorithm, one type of active learning method, to optimize U and V terms. The DFT + U + V with U/V obtained by BOD can produce improved electronic properties for diverse bulk materials of comparable quality to the hybrid functionals with lower computational cost compared to the linear response approach. Note that the band gaps calculated by BOD are somewhat different from that of hybrid functionals by simply applying the same U/V parameters as in the case of surface slabs and interfaces, which suggests that the transferability of U/V from the bulk models to slabs and interfaces is not as well as expected. BOD is extended to calculate the U/V parameters for slabs and interfaces and reach similar results as bulk solids. Moreover, we find that the U/V are reasonably transferable between surface slabs and interfaces with different thicknesses under various effects of quantum confinement, which contributes to fast access to the electronic properties of large-scale systems with higher accuracy.

Fluorinated Graphene-Lewis-Base Polymer Composites as a Multifunctional Passivation Layer for High-Performance Perovskite Solar Cells.

Increasing the open-circuit voltage (Voc) stands as a critical strategy for further improving the efficiency of organic-inorganic halide perovskite solar cells (PSCs). Lewis basic polymers, such as polymethyl methacrylate (PMMA), are considered as an effective approach to reduce the nonradiative recombination at the perovskite surface and protect the photoactive layer against moisture. However, the insulating nature of PMMA inherently leads to increased series resistance in PSCs. Here, we propose a multifunctional passivation layer (FG-PMMA) composed of fluorinated graphene (FG) and PMMA, offering high conductivity, a good passivation effect, and excellent hole transportation capabilities. The introduction of FG not only reduces the resistance of the PMMA layer but also improves its hydrophobicity. More importantly, we found that fluoride, which acts as a p-type dopant in graphene, can further reduce the nonradiative recombination centers by forming PbF2 with uncoordinated Pb0 at the perovskite/hole transport layer interface. As a result, the introduction of FG-PMMA significantly enhances the photovoltaic performance, with a record-high open-circuit voltage (Voc) of 1.247 V and an average power conversion efficiency of 22.91%, higher than those of PMMA-based devices (20.75%, 1.210 V), as well as increasing the device's moisture stability, with over 90% of the initial efficiency maintained after 1200 h of aging at room temperature and a relative humidity of 35%.

MTA1, a Novel ATP Synthase Complex Modulator, Enhances Colon Cancer Liver Metastasis by Driving Mitochondrial Metabolism Reprogramming.

Liver metastasis is the most fatal event of colon cancer patients. Warburg effect has been long challenged by the fact of upregulated oxidative phosphorylation (OXPHOS), while its mechanism remains unclear. Here, metastasis-associated antigen 1 (MTA1) is identified as a newly identified adenosine triphosphate (ATP) synthase modulator by interacting with ATP synthase F1 subunit alpha (ATP5A), facilitates colon cancer liver metastasis by driving mitochondrial bioenergetic metabolism reprogramming, enhancing OXPHOS; therefore, modulating ATP synthase activity and downstream mTOR pathways. High-throughput screening of an anticancer drug shows MTA1 knockout increases the sensitivity of colon cancer to mitochondrial bioenergetic metabolism-targeted drugs and mTOR inhibitors. Inhibiting ATP5A enhances the sensitivity of liver-metastasized colon cancer to sirolimus in an MTA1-dependent manner. The therapeutic effects are verified in xenograft models and clinical cases. This research identifies a new modulator of mitochondrial bioenergetic reprogramming in cancer metastasis and reveals a new mechanism on upregulating mitochondrial OXPHOS as the reversal of Warburg effect in cancer metastasis is orchestrated.

Liquid metal for high-entropy alloy nanoparticles synthesis.

High-entropy alloy nanoparticles (HEA-NPs) show great potential as functional materials1-3. However, thus far, the realized high-entropy alloys have been restricted to palettes of similar elements, which greatly hinders the material design, property optimization and mechanistic exploration for different applications4,5. Herein, we discovered that liquid metal endowing negative mixing enthalpy with other elements could provide a stable thermodynamic condition and act as a desirable dynamic mixing reservoir, thus realizing the synthesis of HEA-NPs with a diverse range of metal elements in mild reaction conditions. The involved elements have a wide range of atomic radii (1.24-1.97 Å) and melting points (303-3,683 K). We also realized the precisely fabricated structures of nanoparticles via mixing enthalpy tuning. Moreover, the real-time conversion process (that is, from liquid metal to crystalline HEA-NPs) is captured in situ, which confirmed a dynamic fission-fusion behaviour during the alloying process.

Deep-learning-assisted theoretical insights into the compatibility of environment friendly insulation medium with metal surface of power equipment.

Exploring a new generation of eco-friendly gas insulation medium to replace greenhouse gas sulphur hexafluoride (SF6) in power industry is significant for reducing the greenhouse effect and building a low-carbon environment. The gas-solid compatibility of insulation gas with various electrical equipment is also of significance before practical applications. Herein, take a promising SF6 replacing gas trifluoromethyl sulfonyl fluoride (CF3SO2F) for example, one strategy to theoretically evaluate the gas-solid compatibility between insulation gas and the typical solid surfaces of common equipment was raised. Firstly, the active site where the CF3SO2F molecule is prone to interact with other compounds was identified. Secondly, the interaction strength and charge transfer between CF3SO2F and four typical solid surfaces of equipment were studied by first-principles calculations and further analysis was conducted, with SF6 as the control group. Then, the dynamic compatibility of CF3SO2F with solid surfaces was investigated by large-scale molecular dynamics simulations with the aid of deep learning. The results indicate that CF3SO2F has excellent compatibility similar to SF6, especially in the equipment whose contact surface is Cu, CuO, and Al2O3 due to their similar outermost orbital electronic structures. Besides, the dynamic compatibility with pure Al surfaces is poor. Finally, preliminary experimental verifications indicate the validity of the strategy.

High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning.

Nowadays, trifluoromethyl sulfonyl fluoride (CF3SO2F) has shown great potential to replace SF6 as an eco-friendly insulation medium in the power industry. In this work, an effective and low-cost design strategy toward ideal gas sensors for the decomposed gas products of CF3SO2F was proposed. The strategy achieved high-throughput screening from a large candidate space based on first-principle calculation and machine learning (ML). The candidate space is made up of different transition metal-embedded graphic carbon nitrides (TM/g-C3N4) owing to their high surface area and subtle electronic structure. Four main noteworthy decomposition gases of CF3SO2F, namely, CF4, SO2, SO2F2, and HF, as well as their initial stable structure on TM/g-C3N4 were determined. The best-performing ML model was established and implemented to predict the interaction strength between gas products and TM/g-C3N4, thus determining the promising gas-sensing materials for target gases with the requirements of interaction strength, recovery time, sensitivity, and selectivity. Further analysis guarantees their stability and reveals the origin of excellent properties as a gas sensor. The high-throughput strategy opens a new avenue of rational and low-cost design principles of desirable gas-sensing materials in an interdisciplinary view.

High-Accuracy Neural Network Interatomic Potential for Silicon Nitride.

In the field of machine learning (ML) and data science, it is meaningful to use the advantages of ML to create reliable interatomic potentials. Deep potential molecular dynamics (DEEPMD) are one of the most useful methods to create interatomic potentials. Among ceramic materials, amorphous silicon nitride (SiNx) features good electrical insulation, abrasion resistance, and mechanical strength, which is widely applied in industries. In our work, a neural network potential (NNP) for SiNx was created based on DEEPMD, and the NNP is confirmed to be applicable to the SiNx model. The tensile tests were simulated to compare the mechanical properties of SiNx with different compositions based on the molecular dynamic method coupled with NNP. Among these SiNx, Si3N4 has the largest elastic modulus (E) and yield stress (σs), showing the desired mechanical strength owing to the largest coordination numbers (CN) and radial distribution function (RDF). The RDFs and CNs decrease with the increase of x; meanwhile, E and σs of SiNx decrease when the proportion of Si increases. It can be concluded that the ratio of nitrogen to silicon can reflect the RDFs and CNs in micro level and macro mechanical properties of SiNx to a large extent.

Quantum transport of sub-5 nm InSe and In2SSe monolayers and their heterostructure transistors.

The emerging two-dimensional (2D) semiconductors hold a promising prospect for sustaining Moore's law benefitting from the excellent device electrostatics with narrowed channel length. Here, the performance limits of sub-5 nm InSe and In2SSe metal-oxide-semiconductor field-effect transistors (MOSFETs) are explored by ab initio quantum transport simulations. The van der Waals heterostructures prepared by assembling different two-dimensional materials have emerged as a new design of artificial materials with promising physical properties. In this study, device performance was investigated utilizing InSe/In2SSe van der Waals heterostructure as the channel material. Both the monolayer and heterostructure devices can scale Moore's law down to 5 nm. A heterostructure transistor exhibits a higher on-state current and faster switching speed compared with isolated monolayer transistors. This work proves that the sub-5 nm InSe/In2SSe MOSFET can satisfy both the low power and high-performance requirements for the international technology roadmap for semiconductors in the next decade and can provide a feasible approach for enhancing device performance.

Designing Reactive Bridging O2- at the Atomic Cu-O-Fe Site for Selective NH3 Oxidation.

Surface oxidation chemistry involves the formation and breaking of metal-oxygen (M-O) bonds. Ideally, the M-O bonding strength determines the rate of oxygen absorption and dissociation. Here, we design reactive bridging O2- species within the atomic Cu-O-Fe site to accelerate such oxidation chemistry. Using in situ X-ray absorption spectroscopy at the O K-edge and density functional theory calculations, it is found that such bridging O2- has a lower antibonding orbital energy and thus weaker Cu-O/Fe-O strength. In selective NH3 oxidation, the weak Cu-O/Fe-O bond enables fast Cu redox for NH3 conversion and direct NO adsorption via Cu-O-NO to promote N-N coupling toward N2. As a result, 99% N2 selectivity at 100% conversion is achieved at 573 K, exceeding most of the reported results. This result suggests the importance to design, determine, and utilize the unique features of bridging O2- in catalysis.