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.

Ionic Porous Aromatic Frameworks Embedding Polyoxometalates for Heterogeneous Catalysis.

Porous aromatic frameworks (PAFs) are promising functional porous solids known for their feasible amenability and extraordinary stability. When the framework modified by ionic functional groups, the given ionic PAFs (iPAFs) exhibited charged channels for adsorption, separation and catalysis. However, the surface areas of ionic porous frameworks are usually lower than the neutral ones, and their synthesis limited by specific strategies and complex modifying processes. To overcome these problems, we proposed an intuitive route to construct ionic porous framework with high specific surface area, through a multivariable synthesis strategy. Herein, multivariate ionic porous aromatic framework (MTV-iPAFs) material named PAF-270 was synthesized from readily available building units with ionic functional groups. PAF-270 exhibited hierarchical structure with the highest specific surface area among reported imidazolium functionalized PAFs. Leveraging its physical and chemical properties, we explored its availability for polyoxometalates loading and heterogeneous catalysis. PAF-270 exhibited high adsorption capacity up to 50% for both H3O40PW12 (HPW) and (NH4)5H6PV8Mo4O40 (V8). HPW@PAF-270 and V8@PAF-270 exhibited excellent catalytic abilities for oleic acid esterification and extractive oxidative desulfurization, respectively. Due to the stability of PAFs, these materials also showed remarkable resistance to temperature and pH changes. These results highlight the potential application of MTV-iPAFs as functional porous materials.

Porous Aromatic Frameworks Enabling Polyiodide Confinement toward High Capacity and Long Lifespan Zinc-Iodine Batteries.

Aqueous zinc-iodine batteries (AZIBs) are attracting increasing attention because of their high safety and abundance of resources. However, the performance of AZIBs is compromised by inadequate confinement of soluble polyiodides, the undesired shuttle effect, and slow reaction kinetics. In this study, a porous aromatic framework (PAF) with abundant benzene motifs and a well-organized pore structure is adopted as the iodine host, which exhibits high iodine adsorption capacity and robust polyiodide confinement. Both experimental characterizations and theoretical simulations indicate that the interactions between iodine species and the PAF-1 facilitate the redox reaction by coupling the electronic structures of the active species in the framework. A comparison of PAF-1, PAF-5, and PAF-11 also emphasizes the structural advantages of the high surface area and interconnected three-dimensional channels of PAF-1. Consequently, the I2@PAF-1 cathode can deliver a high capacity of 328 mAh g-1 at 0.5 C, outstanding rate performance, and a stable cycling life of 20 000 cycles (86 % retention at 10 C). The robust polyiodide confinement and superb electrochemical performance of Zn-I2@PAF-1 provide insights into the practical application of PAFs as excellent electrode materials for AZIBs.

L-shaped association between the GA/HbA1c ratio and all-cause mortality in U.S. adults with NAFLD: a cross-sectional study from the NHANES 1999-2004.

OBJECTIVE: It is currently unclear whether there is a relationship between the ratio of glycated albumin to hemoglobin A1c (GA/HbA1c) and mortality in individuals diagnosed with nonalcoholic fatty liver disease (NAFLD). The primary objective of the study was to investigate the relationship between the GA/HbA1c ratio and all-cause mortality in adults with NAFLD in the U.S. METHODS: The investigation included a total of 5,295 individuals aged ≥ 18 years who were diagnosed with NAFLD, these individuals were selected from the National Health and Nutrition Examination Survey conducted between 1999 and 2004. To evaluate the outcomes of death, the researchers relied on National Death Index (NDI) records up to December 31, 2019. To better understand the nonlinear relationship between the GA/HbA1c ratio and mortality among individuals with NAFLD, this study employed both subgroup and sensitivity analyses. Furthermore, Cox proportional hazards models and two-part Cox proportional hazards model were utilized. RESULTS: The study included a total of 5,295 adult patients with NAFLD in the U.S. During a median follow-up period of 16.9 years, there were 1,471 recorded deaths, including 419 cardiovascular deaths. After accounting for various factors, a higher GA/HbA1c ratio exhibited a positive and nonlinear association with an increased risk of all-cause mortality in patients with NAFLD. Furthermore, the study revealed an L-shaped relationship between the GA/HbA1c ratio and all-cause mortality, with the inflection point occurring at a GA/HbA1c ratio of 2.21. When the GA/HbA1c ratio exceeded 2.21, each 1-unit increase in the ratio was associated with a 33% increase in the adjusted hazard ratio (HR 1.33; 95% CI 1.14, 1.60) for all-cause mortality. CONCLUSIONS: A nonlinear correlation between the ratio of GA to HbA1c and all-cause mortality was observed in U.S. adults with NAFLD. In addition, an elevated GA/HbA1c ratio was linked to an increased risk of all-cause mortality in these patients.

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.

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.

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.

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.

Multivariate Porous Aromatic Frameworks with High Porosity and Hierarchical Structures for Enzyme Immobilization.

As materials with permanently porous structures and readily modifying availability, porous aromatic frameworks (PAFs) are considered as promising porous materials with versatile functionality. Currently the designable synthesis of PAFs with the desired surface area and pore size is still a challenge, and instead kinetically irreversible coupling reactions for PAFs synthesis has resulted in the unpredictable connection of building units. Herein, a series of PAFs with highly porous and hierarchical structures were successfully synthesized through a multivariate inspired strategy, where multiple building units with various topologies and sizes were selected for PAFs synthesis. All the PAFs synthesized through this strategy possessed hierarchical structures and high specific surface areas at the same time. Encouraged by their high surface area and hierarchical structures, we loaded lipase onto one of the multivariate PAFs. The enzyme loading content of the obtained lipase@PAF-147 was as high as 1456 mg g-1, which surpassed any other currently reported enzyme loading materials. The lipase@PAF-147 also exhibited favorable catalytic activity and stability to a model reaction of p-nitrophenyl caprylate (p-NPC) hydrolysis. This multivariate strategy inspired synthetic method broadens the selection of building units for PAFs design and opens a new avenue for the design of functional porous materials.

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.

Ultrathin ferrite nanosheets for room-temperature two-dimensional magnetic semiconductors.

The discovery of magnetism in ultrathin crystals opens up opportunities to explore new physics and to develop next-generation spintronic devices. Nevertheless, two-dimensional magnetic semiconductors with Curie temperatures higher than room temperature have rarely been reported. Ferrites with strongly correlated d-orbital electrons may be alternative candidates offering two-dimensional high-temperature magnetic ordering. This prospect is, however, hindered by their inherent three-dimensional bonded nature. Here, we develop a confined-van der Waals epitaxial approach to synthesizing air-stable semiconducting cobalt ferrite nanosheets with thickness down to one unit cell using a facile chemical vapor deposition process. The hard magnetic behavior and magnetic domain evolution are demonstrated by means of vibrating sample magnetometry, magnetic force microscopy and magneto-optical Kerr effect measurements, which shows high Curie temperature above 390 K and strong dimensionality effect. The addition of room-temperature magnetic semiconductors to two-dimensional material family provides possibilities for numerous novel applications in computing, sensing and information storage.

Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction.

High-entropy alloys (HEAs) have recently been applied in the field of heterogeneous catalysis benefiting from vast chemical space. However, huge chemical space also brings extreme challenges for the comprehensive study of HEAs by traditional trial-and-error experiments. Therefore, the machine learning (ML) method is presented to investigate the oxygen reduction reaction (ORR) catalytic activity of millions of reactive sites on HEA surfaces. The well-performed ML model is constructed based on the gradient boosting regression (GBR) algorithm with high accuracy, generalizability, and simplicity. In-depth analysis of the results demonstrates that adsorption energy is a mixture of the individual contributions of coordinated metal atoms near the reactive site. An efficient strategy is proposed to further boost the ORR catalytic activity of promising HEA catalysts by optimizing the HEA surface structure, which recommends a highly efficient HEA catalyst of Ir48Pt74Ru30Rh30Ag74. Our work offers a guide to the rational design and nanostructure synthesis of HEA catalysts.

Strain-promoted conductive metal-benzenhexathiolate frameworks for overall water splitting.

Designing efficient catalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is a desirable strategy for overall water splitting and the generation of clean and renewable energies. Herein, the electrocatalytic HER and OER activity of the conductive metal-benzenhexathiolate (M-BHT) frameworks has been evaluated utilizing first-principles calculations. The in-plane π-d conjugation of M-BHT guarantees fast electron transfer during electrocatalytic reactions. Notably, Rh-BHT holds the promise of bifunctional HER/OER activity with the overpotentials of 0.07/0.36 V. Furthermore, the application of strain engineering tailors the adsorption of intermediates and promotes the overall water splitting performance. Rh-BHT with the +1% tensile strain shows the HER/OER overpotential of 0.02/0.37 V. This work not only demonstrates the prospects of conductive metal-organic frameworks in electrocatalysis but also offers new insights into designing efficient catalysts by strain engineering.

Two-dimensional ferroelectric MoS2/Ga2O3 heterogeneous bilayers with highly tunable photocatalytic and electrical properties.

Two-dimensional van der Waals heterostructures with strong intrinsic ferroelectrics are highly promising for novel devices with designed electronic properties. The polarization reversal transition of the 2D ferroelectric Ga2O3 monolayer offers a new approach to tune the photocatalytic and electrical properties of MoS2/Ga2O3 heterogeneous bilayers. In this work, we study MoS2/Ga2O3 heterogeneous bilayers with different intrinsic polarization using hybrid-functional calculations. We closely investigate the structural, electronic and optical properties of two stable stacking configurations with opposite polarization. The results reveal a distinct switch from type-I to type-II heterostructures owing to polarization reversal transition of the 2D ferroelectric Ga2O3 monolayer. Biaxial strain engineering leads to type-I-to-II and type-II-to-III transitions in the two polarized models, respectively. Intriguingly, one of the MoS2/Ga2O3 heterolayers has a larger spatial separation of the valence and conduction band edges and excellent optical absorption ranging from infrared to ultraviolet region under biaxial strain, thus ensuring promising novel applications such as flexible electrical and optical devices. Based on the highly tunable physical properties of the bilayer heterostructures, we further explore their potential applications, such as photocatalytic water splitting and field-controlled switch channel in MOSFET devices.

Reduced Fermi Level Pinning at Physisorptive Sites of Moire-MoS2/Metal Schottky Barriers.

Weaker Fermi level pinning (FLP) at the Schottky barriers of 2D semiconductors is electrically desirable as this would allow a minimizing of contact resistances, which presently limit device performances. Existing contacts on MoS2 have a strong FLP with a small pinning factor of only ∼0.1. Here, we show that Moire interfaces can stabilize physisorptive sites at the Schottky barriers with a much weaker interaction without significantly lengthening the bonds. This increases the pinning factor up to ∼0.37 and greatly reduces the n-type Schottky barrier height to ∼0.2 eV for certain metals such as In and Ag, which can have physisorptive sites. This then accounts for the low contact resistance of these metals as seen experimentally. Such physisorptive interfaces can be extended to similar systems to better control SBHs in highly scaled 2D devices.

Transition Metal and N Doping on AlP Monolayers for Bifunctional Oxygen Electrocatalysts: Density Functional Theory Study Assisted by Machine Learning Description.

It is vital to search for highly efficient bifunctional oxygen evolution/reduction reaction (OER/ORR) electrocatalysts for sustainable and renewable clean energy. Herein, we propose a single transition-metal (TM)-based defective AlP system to validate bifunctional oxygen electrocatalysis by using the density functional theory (DFT) method. We found that the catalytic activity is enhanced by substituting two P atoms with two N atoms in the Al vacancy of the TM-anchored AlP monolayer. Specifically, the overpotential of OER(ORR) in Co- and Ni-based defective AlP systems is found to be 0.38 (0.25 V) and 0.23 V (0.39 V), respectively, showing excellent bifunctional catalytic performance. The results are further presented by establishing the volcano plots and contour maps according to the scaling relation of the Gibbs free-energy change of *OH, *O, and *OOH intermediates. The d-band center and the product of the number of d-orbital electrons and electronegativity of the TM atom are the ideal descriptors for this system. To investigate the activity origin of the OER/ORR process, we performed the machine learning (ML) algorithm. The result indicates that the number of TM-d electrons (Ne), the radius of TM atoms (rd), and the charge transfer of TM atoms (Qe) are the three primary descriptors characterizing the adsorption behavior. Our results can provide a theoretical guidance for designing highly efficient bifunctional electrocatalysts and pave a way for the DFT-ML hybrid method in catalysis research.

A durable and pH-universal self-standing MoC-Mo2C heterojunction electrode for efficient hydrogen evolution reaction.

Efficient water electrolyzers are constrained by the lack of low-cost and earth-abundant hydrogen evolution reaction (HER) catalysts that can operate at industry-level conditions and be prepared with a facile process. Here we report a self-standing MoC-Mo2C catalytic electrode prepared via a one-step electro-carbiding approach using CO2 as the feedstock. The outstanding HER performances of the MoC-Mo2C electrode with low overpotentials at 500 mA cm-2 in both acidic (256 mV) and alkaline electrolytes (292 mV), long-lasting lifetime of over 2400 h (100 d), and high-temperature performance (70 oC) are due to the self-standing hydrophilic porous surface, intrinsic mechanical strength and self-grown MoC (001)-Mo2C (101) heterojunctions that have a ΔGH* value of -0.13 eV in acidic condition, and the energy barrier of 1.15 eV for water dissociation in alkaline solution. The preparation of a large electrode (3 cm × 11.5 cm) demonstrates the possibility of scaling up this process to prepare various carbide electrodes with rationally designed structures, tunable compositions, and favorable properties.