LAB REPORT

Lawrence Livermore National Laboratory, more widely known for its research in fusion energy, made a different sort of announcement in October: scientists at the lab had developed a new treatment for exposure to fentanyl, the synthetic opioid at the heart of the U.S.’s addiction crisis.

Subetadex is a chemical compound that strongly bonds with fentanyl molecules and remains in the body longer than naloxone, the most commonly used treatment for acute fentanyl overdoses.

Naloxone is often marketed under the brand name Narcan as a nasal spray. Emergency responders sometimes need to administer a number of doses of naloxone to stabilize a patient who is overdosing. Subetadex can require fewer doses. Another advantage of subetadex is that it can be taken or administered before or after fentanyl exposure and can work on its own and in tandem with other treatments, including naloxone.

A California-based laboratory is set to lay the groundwork for the next evolution of extreme ultraviolet (EUV) lithography. Led by Lawrence Livermore National Laboratory, the project aims for the next evolution of EUV lithography, centered around the lab-developed driver system named the Big Aperture Thulium (BAT) laser.

The LLNL-led project will test the BAT laser’s ability to increase EUV source efficiency by about 10 times when compared to carbon dioxide (CO2) lasers, the current industry standard, according to the lab. LLNL maintains that this could lead to a next-generation “beyond EUV” (BEUV) lithography system producing chips that are smaller, more powerful, and faster to manufacture while using less electricity.

Starris: Optimax Space Systems and Lawrence Livermore National Laboratory announce a commercialization partnership for LLNL’s patented monolithic telescope technology — which accelerates rapid deployment of modular optical designs for high-resolution or high sensitivity space imagery.

Starris has collaborated over the last decade with LLNL’s Space Program to develop the monolithic telescope technology and will manufacture — at scale and with customization options — the precision-fabricated optical lens that forms the image in the telescope. The collaboration with LLNL is now extended via a government-use license for commercializing the technology through LLNL’s Innovation and Partnerships Office.

Three American scientists have been named by the White House as recipients of the Enrico Fermi Presidential Award. Héctor D. Abruña, Paul Alivisatos, and John H. Nuckolls were recognized for “exemplary contributions to advance efforts to tackle some of the world’s greatest challenges, including improving health outcomes, clean energy, and national security.”

John Hopkin Nuckolls is a physicist who spent his career at the Lawrence Livermore National Laboratory. His citation recognized his seminal leadership in inertial confinement fusion and high energy density physics, outstanding contributions to national security, and visionary leadership of Lawrence Livermore National Laboratory at the end of the Cold War.

The AI boom is sparking a potential energy crisis. Data centers, housing the powerful GPU-enabled servers that fuel AI’s growth, are projected to consume 12% of US electricity by 2028, as Reuters recently noted. Prominent tech firms like xAI, Meta, Microsoft, and OpenAI are pouring billions into GPU-based “mega-clusters” involving 100,000 or more GPUs. In addition, several companies are turning to nuclear power investments to secure the reliable, carbon-free energy these massive data centers demand.

The implications for power infrastructure are profound. While modern data centers employ advanced cooling systems and energy-efficient hardware, the sheer scale of AI computation presents unprecedented challenges. Bronis R. de Supinski, Chief Technology Officer for Livermore Computing at Lawrence Livermore National Laboratory and ACM Fellow, emphasizes that traditional efficiency metrics like GFlops/Watt fail to capture the complete environmental impact of these systems. In this interview, de Supinski outlines key strategies to measure and manage AI’s environmental footprint, an ever-more pressing concern for data centers and HPC facilities worldwide.