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A giant magnetoelectric coefficient has been discovered in laminated magnetoelectric composites incorporating piezoelectric and magnetostrictive layers, which reveals a high sensitivity in AC magnetic field detection under a DC bias field. However, the DC-biased magnetoelectric composites are not capable of detecting DC magnetic fields due to the interference with the DC signal to be measured. Here, we demonstrate a portable magnetoelectric gaussmeter based on torque effect that can detect both DC and AC magnetic fields. The proposed gaussmeter is equipped with a magnetoelectric sensor, a charge amplification module, a signal processing circuit, a power module, a data processing program, a display module, etc. The proposed gaussmeter indicates such performance indexes as an intensity range of 0~10 Oe, frequency range of DC~500 Hz, AC detection limit of 0.01 Oe, DC detection limit of 0.08 Oe, and frequency resolution of 1 Hz. Being powered by a power adapter (or a battery) of 5V 2A, the whole device system is pocket-size, low-cost, and highly portable, demonstrating its potential for magnetic field detection as a distributed sensor.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under d15 thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the d15 thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under d[sub.15] thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the d[sub.15] thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

Shotgun metagenomics has become a pivotal technology in microbiome research, enabling in‐depth analysis of microbial communities at both the high‐resolution taxonomic and functional levels. This approach provides valuable insights of microbial diversity, interactions, and their roles in health and disease. However, the complexity of data processing and the need for reproducibility pose significant challenges to researchers. To address these challenges, we developed EasyMetagenome, a user‐friendly pipeline that supports multiple analysis methods, including quality control and host removal, read‐based, assembly‐based, and binning, along with advanced genome analysis. The pipeline also features customizable settings, comprehensive data visualizations, and detailed parameter explanations, ensuring its adaptability across a wide range of data scenarios. Looking forward, we aim to refine the pipeline by addressing host contamination issues, optimizing workflows for third‐generation sequencing data, and integrating emerging technologies like deep learning and network analysis, to further enhance microbiome insights and data accuracy. EasyMetageonome is freely available at https://github.com/YongxinLiu/EasyMetagenome. EasyMetagenome is a user‐friendly shotgun metagenomics pipeline designed for comprehensive microbiome analysis, supporting quality control, host removal, read‐based, assembly‐based, binning, genome and pan‐genome analysis. It offers customizable settings, data visualizations, and parameter explanations. The pipeline is freely available at https://github.com/YongxinLiu/EasyMetagenome. Highlights EasyMetagenome offers multiple installation options and detailed question and answer explanations. EasyMetagenome includes detailed parameter explanations to accommodate various data scenarios, ensuring flexibility and adaptability. EasyMetagenome supports comprehensive downstream analysis from raw data, including read‐based, assembly‐based, binning, and genome analysis, with the ability to generate publication‐ready visualizations. EasyMetagenome is developed as an open‐source project, encouraging collaboration from the community. It's available for access and contribution at https://github.com/YongxinLiu/EasyMetagenome.