The continued scaling of flash memory technologies faces challenges such as limited operation speed, poor data retention, and interface defects inherent to conventional three-dimensional architectures. Two-dimensional (2D) materials, with van der Waals interfaces and atomic-scale thickness, offer a promising pathway to overcome these limitations by enabling efficient charge modulation while minimizing surface defects. In this work, a nonvolatile 2D flash memory device is developed employing monolayer Janus MoSSe as the charge-trapping layer and hexagonal boron nitride (h-BN) as an ultrathin tunneling barrier. The intrinsic structural asymmetry of Janus MoSSe induces a strong vertical dipole moment, resulting in enhanced charge trapping, deeper energy barriers, and directional polarization compared with symmetric 2D materials. Consequently, the devices exhibit outstanding retention times exceeding 10⁴ s, endurance beyond 10⁴ program/erase cycles, and large memory window ratios (ΔV/V_G,max of 50%–70% for 10 and 6 nm h-BN, respectively), with charge-trapping rates up to 8.96 × 10¹⁴ cm⁻² s⁻¹. In addition, Janus MoSSe-based devices show synaptic characteristics under electrical pulses and perform recognition simulations in artificial neural networks. These findings establish a design paradigm for 2D memory devices, enabling ultrathin, flexible, and energy-efficient nonvolatile memories.

After a year and a half of remote work and learning, UC Santa Barbara undergraduate students Sophia Lecuona Manos, Gabrielle Plewe, Carson Gadler and doctoral student Zoe Zilz returned to campus in late 2021 eager for some on-campus, hands-on research, an opportunity that was lost when COVID shut down universities and other public institutions in early 2020.

Iowa State researchers are teaming up with international collaborators to develop a web tool that helps farmers identify and manage the insects, weeds and diseases that threaten their crops. The project's work at Iowa State is supported by the U.S. National Science Foundation.

Artificial intelligence is rapidly transforming how scientists study and manage the environment. A new perspective article suggests that AI is creating a new research paradigm that moves environmental science from traditional observation based approaches toward predictive and intelligent systems capable of addressing complex global challenges.

A new study published in Big Earth Data introduces Geo-Disasters, a database that geocodes 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023. The database enhances disaster risk analysis by providing accurate geocoded locations and offering an open, reproducible framework for integrating spatial climate and socioeconomic data. This framework supports machine learning applications for climate risk, vulnerability, and adaptation studies. The Geo-Disasters database aims to bridge critical data gaps in global climate-hazard risk assessment and inform more equitable adaptation planning. The link to Geo-Disasters Database is https://doi.org/10.5281/zenodo.15487667.

Researchers at Rice University recently convened an international group of scientists to explore how artificial intelligence and machine learning could transform one of the world’s most ambitious physics experiments: the Deep Underground Neutrino Experiment (DUNE). Held March 10-12 at Rice’s BioScience Research Collaborative, the three-day workshop brought together researchers from universities, national laboratories and international partners to discuss how the experiment’s software and computing infrastructure can better support the growing role of AI and machine learning. The event was organized by DUNE’s AI/ML Forum and Core Software and Computing Consortium and was partially supported by the Rice Creative Ventures Fund.

Comparing sleep-like brain activity in adults with ADHD compared to neurotypical adults advances understanding of the brain mechanisms that contribute to attention lapses during tasks.