“China’s lab-grown diamonds are emerging as a surprising beneficiary of the artificial intelligence boom, with demand climbing while they gain traction as a key component in advanced chipmaking.

Traditionally associated with jewelry, these synthetic gems are now being adopted as chip‑cooling materials, enabling denser and more powerful AI semiconductors. Momentum has accelerated after several Chinese producers reported that clients validated their diamonds as effective heat spreaders and began commercial shipments…

Gains in this niche segment underscore investors’ search for new AI winners, as crowded hardware bets, from printed circuit boards to optical modules, have grown more expensive after a sharp rally. The surge also highlights a shift toward next‑generation cooling materials, with analysts noting that diamond is increasingly viewed as a superior alternative to traditional solutions like copper or aluminum.”

From Bloomberg.

“As of June 4, 2026, Antares Nuclear’s Mark-0 reactor became the first reactor in the Department of Energy’s Reactor Pilot Program to reach criticality at the Idaho National Laboratory. Antares is one of 11 companies taking part in the Pilot Program.

These reactors are not gigawatt-scale commercial plants, but they are essential first-of-a-kind demonstrations. Done well, the program can generate the data, operating experience, and regulatory lessons that help clear the path for commercial advanced reactor deployment. It fills the gap in the prototyping stage of the innovation cycle—a stage that is crucial for the U.S. to succeed. That Antares has been able to reach criticality a full month before the July 4, 2026 deadline established for the program is a welcome indicator of the program’s potential success.”

From The Ecomodernist.

“Extracting lithium from hard rock today is an energy- and waste-intensive process that is often far more expensive than getting lithium from brine water, which also has major environmental drawbacks. Currently, lithium hard rock extraction involves baking the rock at over 1,000 Celsius and chemically leaching it to extract lithium. The rest of the rock is discarded.

Now, a team of researchers from MIT and elsewhere has developed a low-temperature process for extracting battery-grade lithium from the most common type of lithium-bearing mineral. The process uses a liquid reagent to dissolve the rock into the useful forms of its constituent parts: not just battery-ready lithium salts, but also smelter-grade alumina and cement-ready silica. After the minerals are extracted, the solvent and reagent can be recovered and used again so waste levels approach zero.

The researchers estimate the closed-loop process is half the cost of traditional lithium hard rock extraction and could make it cost-competitive with extracting lithium from brine water.

A paper describing the process was published today in Science. The researchers have already begun commercializing the technology through an MIT spinout, Rock Zero.”

From MIT News.

“A research team at the Department of Energy’s Pacific Northwest National Laboratory has deployed AI agents with the potential to accelerate the recovery of critical minerals from real-world industrial waste in days instead of the months or years required for manual experimentation…

To demonstrate the value of the system, the research team tested three different industrial wastes: two different kinds of spent magnets and wastewater from oil and gas extraction.

The scientists fed a description of what was in the waste to specially designed AI agents. The agents then evaluated the value, concentration, and potential product purity after a separation procedure, before making a technical and economic recovery recommendation. In the trial runs, the AI agents recommended recovery of the element magnesium from wastewater produced during oil and gas extraction, of neodymium and praseodymium from magnet waste, and of samarium, a rare-earth element critical to high-performance aerospace magnets and nuclear reactors. 

Such feedstock evaluations traditionally take months of analysis and preliminary lab protocol preparation. 

Instead, within a day, the AI agents used published scientific literature to develop a plan for 96 simultaneous experiments, including recipes for all chemicals used for separation, their order of addition, and timing steps. A liquid-handling robot then executed the orders. 

For these initial experiments, human operators prepared the completed experimental samples for final chemical analysis. But the resulting data were automatically evaluated by AI for any necessary refinements, and if needed, a second round of 96 experiments to optimize purity and yield.”

From Pacific Northwest National Laboratory.