The move shows how some panel-makers are adjusting to stricter requirements under President Donald Trump.

The state is demanding cities and counties detail "green new deal" spending, including funds for EVs, sustainable buildings and solar power.

The ruling notches a small win for the state ahead of a full trial.

The water tech startup Hyfi began installing 50 sensors in flood-prone areas across the city.

European countries, particularly in the south, have already reported their share of vector-borne virus outbreaks this summer.

MIT researchers have developed a reconfigurable antenna that dynamically adjusts its frequency range by changing its physical shape, making it more versatile for communications and sensing than static antennas.

A user can stretch, bend, or compress the antenna to make reversible changes to its radiation properties, enabling a device to operate in a wider frequency range without the need for complex, moving parts. With an adjustable frequency range, a reconfigurable antenna could adapt to changing environmental conditions and reduce the need for multiple antennas.

The word “antenna” may draw to mind metal rods like the “bunny ears” on top of old television sets, but the MIT team instead worked with metamaterials — engineered materials whose mechanical properties, such as stiffness and strength, depend on the geometric arrangement of the material’s components.

The result is a simplified design for a reconfigurable antenna that could be used for applications like energy transfer in wearable devices, motion tracking and sensing for augmented reality, or wireless communication across a wide range of network protocols.

In addition, the researchers developed an editing tool so users can generate customized metamaterial antennas, which can be fabricated using a laser cutter.

“Usually, when we think of antennas, we think of static antennas — they are fabricated to have specific properties and that is it. However, by using auxetic metamaterials, which can deform into three different geometric states, we can seamlessly change the properties of the antenna by changing its geometry, without fabricating a new structure. In addition, we can use changes in the antenna’s radio frequency properties, due to changes in the metamaterial geometry, as a new method of sensing for interaction design,” says lead author Marwa AlAlawi, a mechanical engineering graduate student at MIT.

Her co-authors include Regina Zheng and Katherine Yan, both MIT undergraduate students; Ticha Sethapakdi, an MIT graduate student in electrical engineering and computer science; Soo Yeon Ahn of the Gwangju Institute of Science and Technology in Korea; and co-senior authors Junyi Zhu, assistant professor at the University of Michigan; and Stefanie Mueller, the TIBCO Career Development Associate Professor in MIT’s departments of Electrical Engineering and Computer Science and Mechanical Engineering and leader of the Human-Computer Interaction Group at the Computer Science and Artificial Intelligence Lab. The research will be presented at the ACM Symposium on User Interface Software and Technology.

Making sense of antennas

While traditional antennas radiate and receive radio signals, in this work, the researchers looked at how the devices can act as sensors. The team’s goal was to develop a mechanical element that can also be used as an antenna for sensing.

To do this, they leveraged the antenna’s “resonance frequency,” which is the frequency at which the antenna is most efficient.

An antenna’s resonance frequency will shift due to changes in its shape. (Think about extending the left “bunny ear” to reduce TV static.) Researchers can capture these shifts for sensing. For instance, a reconfigurable antenna could be used in this way to detect the expansion of a person’s chest, to monitor their respiration.

To design a versatile reconfigurable antenna, the researchers used metamaterials. These engineered materials, which can be programmed to adopt different shapes, are composed of a periodic arrangement of unit cells that can be rotated, compressed, stretched, or bent.

By deforming the metamaterial structure, one can shift the antenna’s resonance frequency.

“In order to trigger changes in resonance frequency, we either need to change the antenna’s effective length or introduce slits and holes into it. Metamaterials allow us to get those different states from only one structure,” AlAlawi says.

The device, dubbed the meta-antenna, is composed of a dielectric layer of material sandwiched between two conductive layers.

To fabricate a meta-antenna, the researchers cut the dielectric laser out of a rubber sheet with a laser cutter. Then they added a patch on top of the dielectric layer using conductive spray paint, creating a resonating “patch antenna.”

But they found that even the most flexible conductive material couldn’t withstand the amount of deformation the antenna would experience.

“We did a lot of trial and error to determine that, if we coat the structure with flexible acrylic paint, it protects the hinges so they don’t break prematurely,” AlAlawi explains.

A means for makers

With the fabrication problem solved, the researchers built a tool that enables users to design and produce metamaterial antennas for specific applications.

The user can define the size of the antenna patch, choose a thickness for the dielectric layer, and set the length to width ratio of the metamaterial unit cells. Then the system automatically simulates the antenna’s resonance frequency range.

“The beauty of metamaterials is that, because it is an interconnected system of linkages, the geometric structure allows us to reduce the complexity of a mechanical system,” AlAlawi says.

Using the design tool, the researchers incorporated meta-antennas into several smart devices, including a curtain that dynamically adjusts household lighting and headphones that seamlessly transition between noise-cancelling and transparent modes.

For the smart headphone, for instance, when the meta-antenna expands and bends, it shifts the resonance frequency by 2.6 percent, which switches the headphone mode. The team’s experiments also showed that meta-antenna structures are durable enough to withstand more than 10,000 compressions.

Because the antenna patch can be patterned onto any surface, it could be used with more complex structures. For instance, the antenna could be incorporated into smart textiles that perform noninvasive biomedical sensing or temperature monitoring.

In the future, the researchers want to design three-dimensional meta-antennas for a wider range of applications. They also want to add more functions to the design tool, improve the durability and flexibility of the metamaterial structure, experiment with different symmetric metamaterial patterns, and streamline some manual fabrication steps.

This research was funded, in part, by the Bahrain Crown Prince International Scholarship and the Gwangju Institute of Science and Technology.

Nature Climate Change, Published online: 18 August 2025; doi:10.1038/s41558-025-02386-y

Elevated atmospheric CO2 has stimulated plant growth, yet the future land carbon sink may be constrained in part by nutrient availability. Here the authors review plant nutrient acquisition strategies and the need for better representation in models to improve predictions of land carbon uptake.

Here’s the story. The commenters on X (formerly Twitter) are unimpressed.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

In the wake of rolling out its own proposal to significantly limit a key law protecting internet users’ speech in the summer of 2020, the Department of Justice under the first Trump administration actively worked with lawmakers to support further efforts to stifle online speech.

The new documents, disclosed in an EFF Freedom of Information Act (FOIA) lawsuit, show officials were talking with Senate staffers working to pass speech- and privacy-chilling bills like the EARN IT Act and PACT Act (neither became law). DOJ officials also communicated with an organization that sought to condition Section 230’s legal protections on websites using age-verification systems if they hosted sexual content.

Section 230 protects users’ online speech by protecting the online intermediaries we all rely on to communicate on blogs, social media platforms, and educational and cultural platforms like Wikipedia and the Internet Archive. Section 230 embodies the principle that we should all be responsible for our own actions and statements online, but generally not those of others. The law prevents most civil suits against users or services that are based on what others say.

DOJ’s work to weaken Section 230 began before President Donald Trump issued an executive order targeting social media services in 2020, and officials in DOJ appeared to be blindsided by the order. EFF was counsel to plaintiffs who challenged the order, and President Joe Biden later rescinded it. EFF filed two FOIA suits seeking records about the executive order and the DOJ’s work to weaken Section 230.

The DOJ’s latest release provides more detail on a general theme that has been apparent for years: that the DOJ in 2020 flexed its powers to try to undermine or rewrite Section 230. The documents show that in addition to meeting with congressional staffers, DOJ was critical of a proposed amendment to the EARN IT Act, with one official stating that it “completely undermines” the sponsors’ argument for rejecting DOJ’s proposal to exempt so-called “Bad Samaritan” websites from Section 230.

Further, DOJ reviewed and proposed edits to a rulemaking petition to the Federal Communications Commission that tried to reinterpret Section 230. That effort never moved forward given the FCC lacked any legal authority to reinterpret the law.

You can read the latest release of documents here, and all the documents released in this case are here.

Related Cases: EFF v. OMB (Trump 230 Executive Order FOIA)

Porn sites are hiding code in .svg files:

Unpacking the attack took work because much of the JavaScript in the .svg images was heavily obscured using a custom version of “JSFuck,” a technique that uses only a handful of character types to encode JavaScript into a camouflaged wall of text.

Once decoded, the script causes the browser to download a chain of additional obfuscated JavaScript. The final payload, a known malicious script called Trojan.JS.Likejack, induces the browser to like a specified Facebook post as long as a user has their account open...

Colorado’s state insurer of last resort began selling property coverage in April and is running far short of its goal to sell 20,000 policies by 2028.

Nonprofits and state agencies that received $7 billion in Inflation Reduction Act solar grants face having to decide whether to accept EPA’s cancellation or fight it.

Scientists thought a marine climate cycle that influences U.S. weather was driven by natural factors. New research has turned that theory on its head.

The attorney general missed a June 7 deadline set by President Donald Trump to investigate state climate programs, easing fears of a federal crackdown.

The administration blacked out 27 pages of findings in a document the Interior secretary cited when he ordered a work stoppage on the offshore wind project.

A legal nonprofit group says the state transportation agency isn’t doing enough to implement New York’s climate law and reduce emissions.

State officials granted temporary protections to the Quino checkerspot, setting up a review — and a new flashpoint in the housing-versus-conservation fight.

Politicians blowing hot air won’t solve Britain’s record-breaking hot summers.

The study shows waves could reach Ahu Tongariki, the largest ceremonial platform on the island, as early as 2080.

Using artificial intelligence, MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies.

After training a machine-learning model to analyze thousands of existing delivery particles, the researchers used it to predict new materials that would work even better. The model also enabled the researchers to identify particles that would work well in different types of cells, and to discover ways to incorporate new types of materials into the particles.

“What we did was apply machine-learning tools to help accelerate the identification of optimal ingredient mixtures in lipid nanoparticles to help target a different cell type or help incorporate different materials, much faster than previously was possible,” says Giovanni Traverso, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and the senior author of the study.

This approach could dramatically speed the process of developing new RNA vaccines, as well as therapies that could be used to treat obesity, diabetes, and other metabolic disorders, the researchers say.

Alvin Chan, a former MIT postdoc who is now an assistant professor at Nanyang Technological University, and Ameya Kirtane, a former MIT postdoc who is now an assistant professor at the University of Minnesota, are the lead authors of the new open-access study, which appears today in Nature Nanotechnology.

Particle predictions

RNA vaccines, such as the vaccines for SARS-CoV-2, are usually packaged in lipid nanoparticles (LNPs) for delivery. These particles protect mRNA from being broken down in the body and help it to enter cells once injected.

Creating particles that handle these jobs more efficiently could help researchers to develop even more effective vaccines. Better delivery vehicles could also make it easier to develop mRNA therapies that encode genes for proteins that could help to treat a variety of diseases.

In 2024, Traverso’s lab launched a multiyear research program, funded by the U.S. Advanced Research Projects Agency for Health (ARPA-H), to develop new ingestible devices that could achieve oral delivery of RNA treatments and vaccines.

“Part of what we’re trying to do is develop ways of producing more protein, for example, for therapeutic applications. Maximizing the efficiency is important to be able to boost how much we can have the cells produce,” Traverso says.

A typical LNP consists of four components — a cholesterol, a helper lipid, an ionizable lipid, and a lipid that is attached to polyethylene glycol (PEG). Different variants of each of these components can be swapped in to create a huge number of possible combinations. Changing up these formulations and testing each one individually is very time-consuming, so Traverso, Chan, and their colleagues decided to turn to artificial intelligence to help speed up the process.

“Most AI models in drug discovery focus on optimizing a single compound at a time, but that approach doesn’t work for lipid nanoparticles, which are made of multiple interacting components,” Chan says. “To tackle this, we developed a new model called COMET, inspired by the same transformer architecture that powers large language models like ChatGPT. Just as those models understand how words combine to form meaning, COMET learns how different chemical components come together in a nanoparticle to influence its properties — like how well it can deliver RNA into cells.”

To generate training data for their machine-learning model, the researchers created a library of about 3,000 different LNP formulations. The team tested each of these 3,000 particles in the lab to see how efficiently they could deliver their payload to cells, then fed all of this data into a machine-learning model.

After the model was trained, the researchers asked it to predict new formulations that would work better than existing LNPs. They tested those predictions by using the new formulations to deliver mRNA encoding a fluorescent protein to mouse skin cells grown in a lab dish. They found that the LNPs predicted by the model did indeed work better than the particles in the training data, and in some cases better than LNP formulations that are used commercially.

Accelerated development

Once the researchers showed that the model could accurately predict particles that would efficiently deliver mRNA, they began asking additional questions. First, they wondered if they could train the model on nanoparticles that incorporate a fifth component: a type of polymer known as branched poly beta amino esters (PBAEs).

Research by Traverso and his colleagues has shown that these polymers can effectively deliver nucleic acids on their own, so they wanted to explore whether adding them to LNPs could improve LNP performance. The MIT team created a set of about 300 LNPs that also include these polymers, which they used to train the model. The resulting model could then predict additional formulations with PBAEs that would work better.

Next, the researchers set out to train the model to make predictions about LNPs that would work best in different types of cells, including a type of cell called Caco-2, which is derived from colorectal cancer cells. Again, the model was able to predict LNPs that would efficiently deliver mRNA to these cells.

Lastly, the researchers used the model to predict which LNPs could best withstand lyophilization — a freeze-drying process often used to extend the shelf-life of medicines.

“This is a tool that allows us to adapt it to a whole different set of questions and help accelerate development. We did a large training set that went into the model, but then you can do much more focused experiments and get outputs that are helpful on very different kinds of questions,” Traverso says.

He and his colleagues are now working on incorporating some of these particles into potential treatments for diabetes and obesity, which are two of the primary targets of the ARPA-H funded project. Therapeutics that could be delivered using this approach include GLP-1 mimics with similar effects to Ozempic.

This research was funded by the GO Nano Marble Center at the Koch Institute, the Karl van Tassel Career Development Professorship, the MIT Department of Mechanical Engineering, Brigham and Women’s Hospital, and ARPA-H.