Effects of B and C doping on tunneling magnetoresistance in CoFe/MgO magnetic tunnel junctions

Using density-functional theory calculations, we investigate the dominant defects formed by boron (B) and carbon (C) impurities in a CoFe/MgO/CoFe magnetic tunnel junction (MTJ) and their influence on conductivity and tunneling magnetoresistance (TMR). We find that, in the O-poor conditions relevant to experiment, B forms the substitutional defect BCo and C forms the interstitial site Ci at the CoFe/MgO interface. The C-doped MTJ is predicted to have a significantly higher TMR than the B-doped MTJ. This is due to interface state densities associated with the majority spin Δ1-symmetry bands being more heavily suppressed by the BCo defects than by the Ci defects. Our results indicate that carbon can serve as a viable alternative to boron as a dopant for MTJ fabrication

Oersted Field and Spin Current Effects on Magnetic Domains in [Co/Pd]

An out-of-plane Oersted field produced from a current-carrying Au wire is used to induce local domain formation in wires made from [Co/Pd][subscript 15] multilayers with perpendicular anisotropy. A 100 ns pulsed current of 56-110 mA injected into the Au wire created a reverse domain size of 120-290 nm in a Co/Pd nanowire on one side of the Au wire. A Biot-Savart model was used to estimate the position dependence of the Oersted field around the Au wire. The shape, size, and location of the reversed region of Co/Pd were consistent with the magnitude of the Oersted field and the switching field distribution of the unpatterned film. A current density of 6.2 × 10[superscript 11] Am[superscript -2] in the Co/Pd nanowire did not translate the domain walls due to low spin transfer efficiency, but the Joule heating promoted domain growth in a field below the coercive field.National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Award DMR1419807

Integrated metagenomics and metabolomics analysis reveals changes in the microbiome and metabolites in the rhizosphere soil of Fritillaria unibracteata

Fritillaria unibracteata (FU) is a renowned herb in China that requires strict growth conditions in its cultivation process. During this process, the soil microorganisms and their metabolites may directly affect the growth and development of FU, for example, the pathogen infection and sipeimine production. However, few systematic studies have reported the changes in the microbiome and metabolites during FU cultivation thus far. In this work, we simultaneously used metagenomics and metabolomics technology to monitor the changes in microbial communities and metabolites in the rhizosphere of FU during its cultivation for one, two, and three years. Moreover, the interaction between microorganisms and metabolites was investigated by co-occurrence network analysis. The results showed that the microbial composition between the three cultivation-year groups was significantly different (2020-2022). The dominant genera changed from Pseudomonas and Botrytis in CC1 to Mycolicibacterium and Pseudogymnoascus in CC3. The relative abundances of beneficial microorganisms decreased, while the relative abundances of harmful microorganisms showed an increasing trend. The metabolomics results showed that significant changes of the of metabolite composition were observed in the rhizosphere soil, and the relative abundances of some beneficial metabolites showed a decreasing trend. In this study, we discussed the changes in the microbiome and metabolites during the three-year cultivation of FU and revealed the relationship between microorganisms and metabolites. This work provides a reference for the efficient and sustainable cultivation of FU

Deep Multimodal Fusion for Generalizable Person Re-identification

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, in this paper, we propose DMF, a Deep Multimodal Fusion network for the general scenarios on person re-identification task, where rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. On top of it, a multimodal fusion strategy is introduced to translate the data of different modalities into the same feature space, which can significantly boost generalization capability of Re-ID model. In the fine-tuning stage, a realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world. Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform previous domain generalization or meta-learning methods. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF

Flow properties of an intact MPL from nano-tomography and pore network modelling

Adding a hydrophobic micro-porous layer (MPL) between a gas diffusion layer (GDL) and a catalyst layer (CL) at the cathode of a PEM fuel cell was found capable of improving cell performance. However, how an MPL does this is not well-understood because current techniques are limited in measuring, observing and simulating multiphase pore fluid flow across the full range of pores that vary to a great extent in geometry, topology, surface morphology. In this work, we focused our investigation on estimating flow properties of an MPL volume to assess the limiting effect of strongly hydrophobic sub-micron pores on water transports. We adopted a nano-tomography and pore network flow modelling approach. A pore-structure model, purposely reconstructed from an intact MPL sample using Focused Ion Beam milling and Scanning Electron Microscope (FIB/SEM) previously, was used to extract a realistic pore network. A two-phase pore network flow model, developed recently for simulating the flow of gas, liquid or their mixture in both micrometre and nanometre pores, was applied to the pore network. We firstly tested the validity of the constructed pore network, and then calculated the properties: permeability for both water and selected gases, water entry pressure, and relative permeability. Knudsen diffusion was taken into consideration in calculations when appropriate. Our calculations showed that the water permeability was three orders of magnitude smaller than experimentally measured results reported in the literature, and when the water contact angle increased from 95° to 150°, the water-entry pressure increased from 2.5 MPa to 28 MPa. Thus our results revealed that for a strongly hydrophobic MPL that contains nanometre pores only it would behave like a buffer to water, and therefore the structural preferential paths in an MPL, such as cracks, are likely to be responsible for significant liquid water transport from the CL to the GDL that has been observed experimentally recently. We highlighted the needs for multi-scale modelling of the interplays of liquid water and gas transfer in MPLs that contain variable pores