Shift invariant sparse coding ensemble and its application in rolling bearing fault diagnosis

This paper proposes an automatic diagnostic scheme without manual feature extraction or signal pre-processing. It directly handles the original data from sensors and determines the condition of the rolling bearing. With proper application of the new technique of shift invariant sparse coding (SISC), it is much easier to recognize the fault. Yet, this SISC, though being a powerful machine learning algorithm to train and test the original signals, is quite demanding computationally. Therefore, this paper proposes a highly efficient SISC which has been proved by experiments to be capable of representing signals better and making converges faster. For better performance, the AdaBoost algorithm is also combined with SISC classifier. Validated by the fault diagnosis of bearings and compared with other methods, this algorithm has higher accuracy rate and is more robust to load as well as to certain variation of speed

Tunable magnetic order in transition metal doped, layered, and anisotropic Bi2O2Se: Competition between exchange interaction mechanisms

Bi2O2Se is a novel layer-structured material with high electron mobility, while its efficiency could be greatly improved by doping different elements to introduce a magnetic spin order. We investigated the electronic and magnetic properties of various transition metal (TM) (TM = Mn, Cr, Fe, Co, and Ni) doped Bi2O2Se within a framework of density functional theory (DFT), and discovered that Bi2-nXnO2Se exhibits long-range magnetic ordered structure via competition among double-exchange, p-d exchange, and superexchange interaction. The magnetic order of the bulk phase in which the magnetic atoms form interlayer coupling would vary with the type and concentration of doped atoms, but all the layered phases in which the magnetic atoms are in-plane coupled show ferromagnetic order. By combing DFT calculations with the Monte Carlo scheme, we solve the exchange interaction constants for the Heisenberg model and further evaluate the Curie temperatures of Bi2-nXnO2Se. Ferromagnetic order for most doped systems exhibit to be robust with high Curie temperature, some of which overcomes room temperature (for 12.5% Co-doped layer Bi2O2Se). It is also worth mentioning that the appearance of impurity energy levels narrows the band gap and enhances the spin-orbit coupling of d orbitals and therefore increase large magnetic anisotropy energy. Our study demonstrates a potential pathway to design new dilute magnetic semiconductors through doping of Bi2-nXnO2Se by magnetic transitional elements.Web of Science1005art. no. 05443

Design of non-transition-metal-doped nanoribbon catalysis to achieve efficient nitrogen fixation

The high percentage of N-2 in the air can provide an abundant nitrogen source for the ammonia industry. However, the nitrogen fixation process still faces great challenges due to the stable nitrogen-nitrogen triple bonds. Recently, single-atom catalysts (SACs) have arguably become the most promising frontier in the synthetic ammonia industry due to their high activity, selectivity and stability. In particular, metal-free catalysis has attracted great attention due to its low-cost and environmentally friendly features. Herein, we investigate a series of nitrogen-reduction reaction (NRR) electrocatalysts as graphene nanoribbons (GNRs) embedded with 16 kinds of non-transition metal single-atom catalysts (non-TMSACs) using density functional theory (DFT) computations. The stability of this system is first confirmed by AIMD simulations and formation energies. Among all the candidates, Si anchored on the GNR system achieves a limiting potential as low as -0.45 V and the binding energy for NNH also serves as a good descriptor for the onset potential. The electronic structure reveals that this design satisfies an "acceptance-donation" interaction scenario, which is also confirmed by the crystal orbital Hamilton population (COHP) and the spatial charge distribution. This study not only proposes an effective catalysis approach for the NRR, but also emphasizes the origin of electronic structures, which may provide guidance for future NRR catalyst designs.Web of Scienc

Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation

Single-view cone-beam X-ray luminescence computed tomography (CB-XLCT) has recently gained attention as a highly promising imaging technique that allows for the efficient and rapid three-dimensional visualization of nanophosphor (NP) distributions in small animals. However, the reconstruction performance is hindered by the ill-posed nature of the inverse problem and the effects of depth variation as only a single view is acquired. To tackle this issue, we present a methodology that integrates an automated restarting strategy with depth compensation to achieve reconstruction. The present study employs a fast proximal gradient descent (FPGD) method, incorporating L0 norm regularization, to achieve efficient reconstruction with accelerated convergence. The proposed approach offers the benefit of retrieving neighboring multitarget distributions without the need for CT priors. Additionally, the automated restarting strategy ensures reliable reconstructions without the need for manual intervention. Numerical simulations and physical phantom experiments were conducted using a custom CB-XLCT system to demonstrate the accuracy of the proposed method in resolving adjacent NPs. The results showed that this method had the lowest relative error compared to other few-view techniques. This study signifies a significant progression in the development of practical single-view CB-XLCT for high-resolution 3−D biomedical imaging

Defect-enhanced CO(2) reduction catalytic performance in O-terminated MXenes

Electrochemical carbon dioxide reduction reaction (CO2RR) represents a promising way to generate fuels and chemical feedstock sustainably. Recently, studies have shown that two-dimensional metal carbides and nitrides (MXenes) can be promising CO2RR electrocatalysts due to the alternating -C and -H coordination with intermediates that decouples scaling relations seen on transition metal catalysts. However, further by tuning the electronic and surface structure of MXenes it should still be possible to reach higher turnover number and selectivities. To this end, defect engineering of MXenes for electrochemical CO2RR has not been investigated to date. In this work, first-principles modelling simulations are employed to systematically investigate CO2RR on M2XO2-type MXenes with transition metal and carbon/nitrogen vacancies. We found that the -C-coordinated intermediates take the form of fragments (e. g., *COOH, *CHO) whereas the -H-coordinated intermediates form a complete molecule (e. g., *HCOOH, *H2CO). Interestingly, the fragment-type intermediates become more strongly bound when transition-metal vacancies are present on most MXenes, while the molecule-type intermediates are largely unaffected, allowing the CO2RR overpotential to be tuned. The most promising defective MXene is Hf(2)NO(2)containing Hf vacancies, with a low overpotential of 0.45 V. More importantly, through electronic structure analysis it could be observed that the Fermi level of the MXene changes significantly in the presence of vacancies, indicating that the Fermi level shift can be used as an ideal descriptor to rapidly predict the catalytic performance of defective MXenes. Such an evaluation strategy is applicable to other catalysts beyond MXenes, which could enhance high throughput screening efforts for accelerated catalyst discovery.Web of Scienc

S-doped graphene-regional nucleation mechanism for dendrite-free lithium metal anodes

Lithium metal is the most promising anode material for next-generation batteries, owing to its high theoretical specific capacity and low electrochemical potential. However, the practical application of lithium metal batteries (LMBs) has been plagued by the issues of uncontrollable lithium deposition. The multifunctional nanostructured anode can modulate the initial nucleation process of lithium before the extension of dendrites. By combing the theoretical design and experimental validation, a novel nucleation strategy is developed by introducing sulfur (S) to graphene. Through first-principles simulations, it is found that S atom doping can improve the Li adsorption ability on a large area around the S doping positions. Consequently, S-doped graphene with five lithiophilic sites rather than a single atomic site can serve as the pristine nucleation area, reducing the uneven Li deposition and improving the electrochemical performance. Modifying Li metal anodes by S-doped graphene enables an ultralow overpotential of 5.5 mV, a high average Coulombic efficiency of 99% over more than 180 cycles at a current density of 0.5 mA cm(-2) for 1.0 mAh cm(-2), and a high areal capacity of 3 mAh cm(-2). This work sheds new light on the rational design of nucleation area materials for dendrite-free LMB.Web of Science924art. no. 180400

Combined Transcriptome and Metabolome Analysis Reveals Adaptive Defense Responses to DON Induction in Potato

Phytophthora infestans poses a serious threat to potato production, storage, and processing. Understanding plant immunity triggered by fungal elicitors is important for the effective control of plant diseases. However, the role of the potato stress response to Fusarium toxin deoxynivalenol (DON)-induced stress is still not fully understood. In this study, the metabolites of DON-treated potato tubers were studied for four time intervals using UPLC-MS/MS. We identified 676 metabolites, and differential accumulation metabolite analysis showed that alkaloids, phenolic acids, and flavonoids were the major differential metabolites that directly determined defense response. Transcriptome data showed that differentially expressed genes (DEGs) were significantly enriched in phenylpropane and flavonoid metabolic pathways. Weighted gene co-expression network analysis (WGCNA) identified many hub genes, some of which modulate plant immune responses. This study is important for understanding the metabolic changes, transcriptional regulation, and physiological responses of active and signaling substances during DON induction, and it will help to design defense strategies against Phytophthora infestans in potato

Interface and Doping Effects on Li Ion Storage Behavior of Graphene/Li₂O

Graphene/metal oxide nanocomposites have been widely used as the anode materials for Li ion batteries, which exhibit much higher Li storage capacity beyond their theoretical capacity. In order to make clear the Li storage mechanism in graphene/metal oxide, we systematically investigated the interface and (B, N, O, S) doping effects on Li ion storage behavior in graphene/Li₂O using first-principles total energy calculations. It is revealed that the doping elements increase the van der Waals interface interaction of graphene/Li₂O by changing the electronic structure of graphene through different mechanisms. The Li storage at the graphene/Li₂O interface exhibits the synergistic effect resulting from the enhanced interface interaction by the Li insertion at the interface. The p-type and n-type doping induced by B and N dopants in graphene enhance and reduce the Li storage capability of graphene/Li₂O, respectively. O and S doping result in the localization of the electronic states in graphene which benefits the Li adsorption at the interface. The localization of electronic states combined with the appropriate dopant electronegativity can enhance the Li atoms adsorption and diffusion simultaneously. Thereby, the highest interfacial lithium storage (0.330 mhA/m²) is obtained for the O-doped system, while the S-doped system possesses the good balance between interfacial Li storage (0.220 mhA/m²) and diffusion energy barrier (0.27 eV). The results open a new insight for the design of graphene/metal oxide composites as energy storage materials

Defect‐Enhanced CO 2

Electrochemical carbon dioxide reduction reaction (CO2RR) represents a promising way to generate fuels and chemical feedstock sustainably. Recently, studies have shown that two-dimensional metal carbides and nitrides (MXenes) can be promising CO2RR electrocatalysts due to the alternating -C and -H coordination with intermediates that decouples scaling relations seen on transition metal catalysts. However, further by tuning the electronic and surface structure of MXenes it should still be possible to reach higher turnover number and selectivities. To this end, defect engineering of MXenes for electrochemical CO2RR has not been investigated to date. In this work, first-principles modelling simulations are employed to systematically investigate CO2RR on M2XO2-type MXenes with transition metal and carbon/nitrogen vacancies. We found that the -C-coordinated intermediates take the form of fragments (e. g., *COOH, *CHO) whereas the -H-coordinated intermediates form a complete molecule (e. g., *HCOOH, *H2CO). Interestingly, the fragment-type intermediates become more strongly bound when transition-metal vacancies are present on most MXenes, while the molecule-type intermediates are largely unaffected, allowing the CO2RR overpotential to be tuned. The most promising defective MXene is Hf(2)NO(2)containing Hf vacancies, with a low overpotential of 0.45 V. More importantly, through electronic structure analysis it could be observed that the Fermi level of the MXene changes significantly in the presence of vacancies, indicating that the Fermi level shift can be used as an ideal descriptor to rapidly predict the catalytic performance of defective MXenes. Such an evaluation strategy is applicable to other catalysts beyond MXenes, which could enhance high throughput screening efforts for accelerated catalyst discovery.Web of Scienc