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Turning captured carbon into natural gas could provide cost-competitive energy storage

Solar and wind energy are highly variable, dependent on the day, weather and location of the facilities. At times, they can generate more electricity than is needed, but they can also fall short when demand is at its peak. Unfortunately, any extra energy created by these sources is often wasted, as there are few methods that adequately store it long-term. To improve energy security in the United States, the nation requires both sources of energy and novel ways to store and distribute it.

In a new study, published in Cell Reports Sustainability, researchers from Lawrence Livermore National Laboratory (LLNL) have explored how a reactive capture and conversion (RCC) process could be used to produce synthetic renewable natural gas—a chemical form of long-duration energy storage.

“Rather than sourcing carbon from below-ground, RCC enables the use of above-ground carbon as a resource,” said LLNL scientist and lead author Alvina Aui. “Synthetic renewable natural gas, when used as an energy-storage option, can reduce grid instability caused by the intermittency of energy sources like wind and solar.”

“Delete-To-Recruit” — Scientists Discover Simpler Approach to Gene Therapy

Repositioning genes awakens fetal hemoglobin to treat disease. CRISPR editing may change future gene therapy.

Researchers have discovered a promising new approach to gene therapy by reactivating genes that are normally inactive. They achieved this by moving the genes closer to regulatory elements on the DNA known as enhancers. To do so, they used CRISPR-Cas9 technology to cut out the piece of DNA separating the gene from its enhancer. This method could open up new ways to treat genetic diseases. The team demonstrated its potential in treating sickle cell disease and beta-thalassemia, two inherited blood disorders.

In these cases, a malfunctioning gene might be bypassed by reactivating an alternative gene that is usually turned off. This technique, called “delete-to-recruit,” works by altering the distance between genetic elements without introducing new genes or foreign material. The study was conducted by researchers from the Hubrecht Institute (De Laat group), Erasmus MC, and Sanquin, and published in the journal Blood.

Connected Minds: Preparing For The Cognitive Gig Economy

There’s also the risk of neuro-exploitation. In a world where disadvantaged individuals might rent out their mental processing to make ends meet, new forms of inequality could emerge. The cognitive gig economy might empower people to earn money with their minds, but it could also commoditize human cognition, treating thoughts as labor units. If the “main products of the 21st-century economy” indeed become “bodies, brains and minds,” as Yuval Noah Harari suggests, society must grapple with how to value and protect those minds in the marketplace.

Final Thoughts

What steam power and electricity were to past centuries, neural interfaces might be to this one—a general-purpose technology that could transform economies and lives. For forward-looking investors and executives, I recommend keeping a close eye on your head because it may also be your next capital asset. If the next era becomes one of connected minds, those who can balance bold innovation with human-centered ethics might shape a future where brainpower for hire could truly benefit humanity.

Metal-organic frameworks with metallic conductivity pave new paths for electronics and energy storage

Metal-organic frameworks (MOFs) are characterized by high porosity and structural versatility. They have enormous potential, for example, for applications in electronics. However, their low electrical conductivity has so far greatly restricted their adoption.

Using AI and robot-assisted synthesis in a self-driving laboratory, researchers from Karlsruhe Institute of Technology (KIT), together with colleagues in Germany and Brazil, have now succeeded in producing an MOF thin film that conducts electricity like metals. This opens up new possibilities in electronics and —from sensors and quantum materials to functional materials.

The team reports on this work in the Materials Horizons journal.

AI tool identifies five distinct cancer cell groups within individual tumors

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterize the diversity of individual cells within tumors, opening doors for more targeted therapies for patients.

Findings on the development and use of the AI tool, called AAnet, have been published in Cancer Discovery.

Tumors aren’t made up of just one cell type—they’re a mix of different cells that grow and respond to treatment in different ways. This diversity, or heterogeneity, makes cancer harder to treat and can in turn lead to worse outcomes, especially in .

Can we fix AI’s evaluation crisis?

This is something that I often wonder about, because a model’s hardcore reasoning ability doesn’t necessarily translate into a fun, informative, and creative experience. Most queries from average users are probably not going to be rocket science. There isn’t much research yet on how to effectively evaluate a model’s creativity, but I’d love to know which model would be the best for creative writing or art projects.

Human preference testing has also emerged as an alternative to benchmarks. One increasingly popular platform is LMarena, which lets users submit questions and compare responses from different models side by side—and then pick which one they like best. Still, this method has its flaws. Users sometimes reward the answer that sounds more flattering or agreeable, even if it’s wrong. That can incentivize “sweet-talking” models and skew results in favor of pandering.

AI researchers are beginning to realize—and admit—that the status quo of AI testing cannot continue. At the recent CVPR conference, NYU professor Saining Xie drew on historian James Carse’s Finite and Infinite Games to critique the hypercompetitive culture of AI research. An infinite game, he noted, is open-ended—the goal is to keep playing. But in AI, a dominant player often drops a big result, triggering a wave of follow-up papers chasing the same narrow topic. This race-to-publish culture puts enormous pressure on researchers and rewards speed over depth, short-term wins over long-term insight. “If academia chooses to play a finite game,” he warned, “it will lose everything.”