Microwave impedance microscopy (MIM) is an emerging scanning probe technique that enables non-contact, nanoscale measurement of local complex permittivity. By integrating an ultrasensitive, phase-resolved microwave sensor with a near-field probe, MIM has made significant contributions to diverse fundamental and applied fields. These include strongly correlated and topological materials, two-dimensional and biological systems, as well as semiconductor, acoustic, and MEMS devices. Concurrently, notable progress has been made in refining the MIM technique itself and broadening its capabilities. However, existing literature has focused exclusively on linear MIM based on homodyne architectures, where reflected or transmitted microwave is demodulated and detected at the incident frequency. As such, linear MIM lacks the ability to probe local electrical nonlinearity, which is widely present, for example, in dielectrics, semiconductors, and superconductors. Elucidating such nonlinearity with nanoscale spatial resolution would provide critical insights into semiconductor processing and diagnostics as well as fundamental phenomena like local symmetry breaking and phase separation.       To address this shortcoming, UC Berkeley researchers have introduced a novel methodology and apparatus for performing multi-harmonic MIM to locally probe electrical nonlinearities at the nanoscale. The technique achieves unprecedented spatial and spectral resolution in characterizing complex materials. It encompasses both hardware configurations enabling multi-harmonic data acquisition and the theoretical and calibration protocols to transform raw signals into accurate measures of intrinsic nonlinear permittivity and conductivity. The advance extends existing linear MIM into the nonlinear domain, providing a powerful, versatile, and minimally invasive tool for semiconductor diagnostics, materials research, and device development.

Professor Erica Heinrich and their team from the University of California, Riverside have developed a novel clinical tool that can be used for the continuous, objective prediction and monitoring of dyspnea in hospitalized and ICU patients. This tool works by using machine learning models to continuous monitor and predict bouts of dyspnea, even when patient monitoring is difficult due to sedation or other medical conditions. This technology has been tested in healthy individuals and is advantageous because it leverages non-invasive biomarkers and it is designed to overcome the subjectivity and low resolution of current methods.

Professor Kevin Kou and his team at the University of California, Riverside, in collaboration with Professor Wendong Huang's lab at City of Hope, have developed a new method for synthesizing modified versions of bersavine. Using this method, several novel bersavine compounds were synthesized. When these new compounds were tested against lymphoma cells, powerful anti-cancer effects were demonstrated. Notably, these newly synthesized analogs are more effective at inhibiting cell growth than the naturally occurring bersavine. 

Researchers at the University of California, Davis have developed a sophisticated soil nitrate sensing system designed to accurately measure soil pore water nitrate concentrations, enhancing sustainable agriculture and environmental monitoring.

An innovative approach to stabilize non-precious metal catalysts for enhanced efficiency and durability in PEM electrolyzers.

This technology introduces a novel method of producing high-efficiency, durable electrodes for polymer electrolyte membrane water electrolysis (PEMWE) using electrospinning.

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