In the six-year time frame established in the settlement, anyone who “unsuccessfully attempted” to cancel their Prime subscription online is eligible to get paid up to $51 from Amazon. People who signed up for Prime during that same period can also get up to $51 if they signed up through a “challenged enrollment flow”—a page with a confusing interface that may lead to people inadvertently making a purchase. Previous court filings established that in some cases, some users may have selected “two-day shipping” on an item and not realized that, in doing so, they were also signing up for Amazon Prime.

An FTC spokesperson tells WIRED that automatic payments will go out to some customers within 90 days.

“The rest of eligible consumers will receive a notification from Amazon, and will have the opportunity to submit a simple claim form,” the FTC says. “Amazon is required to post information about this to Amazon.com and the app. The settlement also requires Amazon to have an independent third party who will monitor their compliance with these claims.”

The court filing says that Amazon is also “permanently” barred from structuring Prime sign-ups with a confusing “negative option feature” where a customer is assumed to be making a purchase unless they actively refuse it.

For example, the filing says, a button that reads “No thanks, I don’t want free shipping” does not clearly indicate that a customer will be signed up for Prime unless they click it. Amazon also has to make it obvious when a person is choosing to sign up for Prime, and include language like “Join Prime” in its user interface. Similarly, Amazon has to clearly communicate when a Prime subscription is subject to auto-renewals by using words like “renew.”

The initial complaint, which was filed by the FTC in June 2023, alleged that while Amazon had improved its process for canceling Prime memberships, the company had spent years knowingly complicating the cancellation process.

An attachment on a May 7 court filing includes an email chain with Amazon employees from December 2020, which was described as “privileged and confidential” in the subject line. In the email, a manager of Prime content and marketing paraphrased key points that came up in a recent “US prime performance meeting.”

“Subscription is driving a bit of a shady world,” reads one paraphrased quote, attributed to an unnamed person at the meeting.

“We should lean away from experimenting with sign-up clarity, and focus more on driving overall members and increasing confirmation that you are prime,” reads a different paraphrased quote from another person at the meeting, included in the same attachment.

A different attachment shows that Amazon was aware that customers were frustrated. A company slide presentation dated September 17, 2017, focused specifically on customer service complaints about “unintentional” Prime sign-ups. (A different attachment, which includes an email chain dated September 25, 2017, appears to refer to the presentation. Two dozen people were asked to “delete the PowerPoint document” and send “confirmation” once they had.)

One customer complaint in the presentation claims that they were “tricked” into signing up for a free trial for Amazon Prime when they selected two-day shipping on a purchase, not knowing that this would also sign them up for a trial for Prime.

“I DO NOT LIKE YOUR SERVICE,” reads another complaint. “THIS IS CRAP THAT I ORDERED A PRODUCT IN AMAZON ADS [sic] ME TO A PROGRAM WITH AUTO BILLING THAT I DID NOT SIGN UP FOR. I WILL NOT USE AMAZON AND TELL EVERYONE ABOUT THIS TYPE OF CRAP YOU ARE PULLING.”

“IT IS SNEAKY AND BLOODY DISHONEST FORCING SOMETHING THE [sic] I NEVER WANTED,” reads another complaint.

The same Amazon slide presentation noted that confusing Prime sign-ups were leading to an increased burden on Amazon’s customer service workers, as well as a “loss of customer trust.”

Inside the microchips powering the device you’re reading this on, the atoms have a hidden order all their own. A team led by Lawrence Berkeley National Laboratory (Berkeley Lab) and George Washington University has confirmed that atoms in semiconductors will arrange themselves in distinctive localized patterns that change the material’s electronic behavior.

The research, published in Science, may provide a foundation for designing specialized semiconductors for quantum-computing and optoelectronic devices for defense technologies.

On the atomic scale, semiconductors are crystals made of different elements arranged in repeating lattice structures. Many semiconductors are made primarily of one element with a few others added to the mix in small quantities. There aren’t enough of these trace additives to cause a repeating pattern throughout the material, but how these atoms are arranged next to their immediate neighbors has long been a mystery.

Do the rare ingredients just settle randomly among the predominant atoms during material synthesis, or do the atoms have preferred arrangements, a phenomenon seen in other materials called short-range order (SRO)? Until now, no microscopy or characterization technique could zoom in close enough, and with enough clarity, to examine tiny regions of the crystal structure and directly interpret the SRO.

“It’s an interesting scientific question because SRO dramatically changes the properties of a material. Our colleagues have predicted SRO theoretically in semiconductors, but this is the first time the individual structure of these SRO domains has been shown experimentally,” said co-lead author Andrew Minor, director of the National Center for Electron Microscopy at Berkeley Lab’s Molecular Foundry and a professor of Materials Science and Engineering at UC Berkeley.

Minor’s lab is part of the Center for Manipulation of Atomic Ordering for Manufacturing Semiconductors (µ-Atoms), a Department of Energy (DOE) Energy Frontier Research Center focused on understanding atomic ordering in semiconductors. “Our results are exciting because the property that’s being changed by this local ordering is the most important property for microelectronics, the band gap, which is what controls the electronic properties,” he said.

The breakthrough moment came when first author Lilian Vogl, who was then a postdoctoral researcher in Minor’s lab, was studying a sample of germanium containing a small amount of tin and silicon using a powerful type of electron microscopy recently pioneered by the group called 4D-STEM. The initial results were too muddled to parse the faint signals from the electrons diffracting off the tin and silicon from the strong signals off the tidily arranged germanium, so she implemented an energy-filtering device on the system to improve contrast.

When the next dataset started appearing on her monitor, she quickly realized there was a new kind of result. The faint signals were clearer, and repeating patterns emerged, indicating that the atoms have preferred order after all.

To validate her findings and learn what these patterns meant, Vogl collected more data with the energy-filtering 4D-STEM and used a pre-trained neural network to sort the diffraction images. The tool identified six recurring motifs representing particular atomic arrangements in the sample material, but the Berkeley Lab team still couldn’t determine the exact atomic structures that were generating the motifs. To interpret their experimental results, they turned to µ-Atoms collaborators at George Washington University led by co-lead author Tianshu Li, a professor of Civil and Environmental Engineering.

Li’s team generated a highly accurate and efficient machine-learning potential capable of modeling millions of atoms in the material’s structure, allowing Vogl to perform simulated 4D-STEM on different possible structural arrangements until she found matches for the motifs in the experimental data.

“It’s remarkable that modeling and experiment can work seamlessly to unravel SRO structural motifs for the first time,” said Li, whose team had previously predicted SRO and its impact and helped motivate the current study.

“Proving SRO experimentally is not an easy task, let alone identifying its structural motifs. Signals from SRO can easily be obscured by defects or inherent movement of atoms at room temperature, and until now there was no clear way to separate them. This work represents the first step toward our broader goal.”

Shunda Chen, a research scientist in Li’s group who developed the model, said, “With these models, which combine machine learning with first-principles calculations, we can replicate experimental procedures with high fidelity and pinpoint the structural motifs that would otherwise remain hidden.”

Follow-up work initiated by other µ-Atoms members at the University of Arkansas and at Sandia National Laboratories is already yielding insights into how these short range-order motifs affect the semiconductor’s electronic properties, and the scientists hope that manipulating the order to enable new types of devices and processing routes will be possible soon.

“We’re going to be able to really push the boundaries beyond current capabilities by designing semiconductors at the atomic scale,” said Vogl, who is now group leader of the Environmental & Analytical Electron Microscopy Group at the Max Planck Institute for Sustainable Materials.

“We are opening the door to a new era of information technology at the atomic scale, unlocking the deterministic placement of SRO motifs for tailoring of band structures that could impact a wide variety of technologies, from topological quantum materials to neuromorphic computing to optical detectors.”

A team led by Prof. Yin Huajie from the Hefei Institute of Physical Science of the Chinese Academy of Sciences has developed a solar-powered system that produces green hydrogen directly from atmospheric moisture without relying on external water or energy sources.

The results are published in Advanced Materials.

Proton Exchange Membrane Water Electrolysis (PEMWE) technology is one of the primary routes for producing green hydrogen, drawing significant attention due to its high efficiency and high-purity hydrogen output. However, the PEMWE process heavily relies on high-purity water as the reaction raw material, limiting its application in water-scarce regions. Atmospheric water harvesting (AWH), as an emerging approach to obtaining pure water, holds promise as a viable solution to the water shortage issue in the production of green hydrogen.

In this study, the researchers developed a self-sustaining system that couples photothermal atmospheric water harvesting with proton exchange membrane electrolysis.

The system uses hierarchically porous carbon as an adsorbent to capture moisture from the air, which is evaporated by solar heat and fed into a custom-built electrolyzer for hydrogen production. The porous material is fabricated through template synthesis and calcination, followed by surface oxidation to improve water affinity.

It demonstrates remarkable performance. Even under low humidity conditions (as low as 20%), it maintains stable water collection and evaporation performance. Under 40% humidity, the system reached a hydrogen production rate of nearly 300 mL per hour with excellent cycle stability and long-term reliability.

Field tests further confirmed that it can continuously produce green hydrogen using only solar energy, with zero carbon emissions and no external energy input.

This work provides a new pathway for sustainable hydrogen production in water-scarce regions, according to the team.

Provided by
Hefei Institutes of Physical Science, Chinese Academy of Sciences