Fully Integrated Passive Wireless Sensor with Mechanical-Electrical Double-Gradient for Multifunctional Healthcare Monitoring.

Accurate, effective, and continuous monitoring of pressure, moisture, and temperature is essential for routine health assessments and professional patient care. In this study, we present a fully integrated multiparameter passive wireless sensor (MWS) that employs a mechanical-electrical dual-gradient structure design. The unique gradient porous structure endows the MWS with significant advantages in terms of detection dimensions (pressure, moisture, and temperature), sensitivity, and stability.

Current Research Status and Future Trends of Vibration Energy Harvesters.

The continuous worsening of the natural surroundings requires accelerating the exploration of green energy technology. Utilising ambient vibration to power electronic equipment constitutes an important measure to address the power crisis. Vibration power is widely dispersed in the surroundings, such as mechanical vibration, acoustic vibration, wind vibration, and water wave vibration.

Estimating soil NO emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands.

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct NO emissions from U.S.

Ultra-Broadband Flexible Thin-Film Sensor for Sound Monitoring and Ultrasonic Diagnosis.

Small-scale and flexible acoustic probes are more desirable for exquisite objects like human bodies and complex-shaped components than conventional rigid ones. Herein, a thin-film flexible acoustic sensor (FA-TES) that can detect ultra-broadband acoustic signals in multiple applications is proposed. The device consists of two thin copper-coated polyvinyl chloride films, which are stimulated by acoustic waves and contact each other to generate the triboelectric signal.

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States.

Background: High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale.