A research team led by Prof. Li Chao from East China Normal University has uncovered the origin of voltage decay in P2-type layered oxide cathodes. Using electron paramagnetic resonance (EPR) spectroscopy at the Steady-State Strong Magnetic Field Facility (SHMFF), the Hefei Institutes of Physical Science of the Chinese Academy of Science, the team tracked the dynamic evolution of oxygen species and clarified their direct role in structural degradation.

The findings, published in Advanced Energy Materials, provide new guidance for designing more stable sodium-ion cathodes.

P2-type sodium layered oxides (Na_xA_yTM_1-yO₂) are long considered stable for anion redox reactions compared to Li-rich O₃-type counterparts, with suppressed voltage decay. However, the team observed significant voltage decay in the high Na-content P2-type Na_0.8Li_0.26Mn_0.74O₂ during cycling—an anomaly unexplainable by existing theories.

The researchers identified a clear sequence of oxygen transformations upon charging, eventually leading to the formation of molecular O₂. While early cycles showed that this oxygen could still be reduced during discharge, with continued cycling a growing fraction of O₂ remained trapped in the discharged state. This irreversible accumulation was pinpointed as the primary driver of voltage decay and capacity loss.

In this study, EPR proved critical as it enabled noninvasive monitoring of oxygen redox behavior and revealed how reactive oxygen intermediates gradually evolve and accumulate during cycling.

EPR further exposed local structural changes: signals associated with spin interactions between manganese and oxidized oxygen became more pronounced with cycling, consistent with the development of Mn-rich and Li-rich domains. These segregation effects, exacerbated by unreduced O₂, aggravated the performance degradation.

Importantly, the team also explained why high sodium-content cathodes behave differently from their low sodium-content counterparts. In high-Na materials, insufficient interlayer spacing allows migration and vacancy growth, making them vulnerable to oxygen trapping.

By contrast, low-Na cathodes with larger spacing remain stable and show no evidence of trapped oxygen.

This study highlights the unique value of EPR in battery research and suggests that bulk modification strategies are key to mitigating voltage decay and developing high-performance cathodes for next-generation batteries, according to the team.

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

Diagnostic testing is big business. The global market for testing semiconductors for defects is estimated at $39 billion in 2025. For medical lab tests, the market is even bigger: $125 billion.

Both kinds of tests have something in common, says Rohan Ghuge, assistant professor of decision science in the information, risk, and operations management department at Texas McCombs. They involve complex systems with vast numbers of components, whether they’re evaluating computer chips or human bodies.

New research from Texas McCombs suggests a new approach to testing complex systems that might save time by eliminating some unnecessary and expensive steps. “Nonadaptive Stochastic Score Classification and Explainable Half-Space Evaluation” is published in Operations Research.

Currently, a common shortcut is to conduct sequences of tests. Instead of testing every component—which isn’t practical for complex systems—a clinician might test certain components first. Each round rules out some possible problems and sets up a new round of tests.

That approach has time-consuming drawbacks, Ghuge says. “First, you might check the vital signs. Then, you come back the next day and do an ECG [electrocardiogram], then we do blood work, step by step. That’s going to take a lot of time, which we don’t really want to waste for a patient.”

What if, he wondered, a single round of tests could provide the most critical information in a fraction of the time? What if the same protocol could prove useful for chips or in clinics?

“We want something that’s highly scalable, deployable, and uniform,” he says. “You need to have it in a way that can be deployed on thousands of kinds of chips, or a first step that you give to clinicians for every patient of that kind.”

Merging success and failure

The key, Ghuge theorized, was to choose a small number of tests that could quickly classify a system’s risk level: low, medium, or high. With Anupam Gupta of New York University and Viswanath Nagarajan of the University of Michigan, he set out to design such a protocol.

Their solution was to combine two sets of tests with opposite goals. One set diagnoses whether a system is working, while the other diagnoses whether it’s failing. Together, they can provide a snapshot of risk.

“You create two lists, say, a success list and a failure list,” Ghuge says. “You combine a fraction of the first list and a fraction of the second list. You want to come up with a single batch of tests that tell you at the same time whether the system is working or failing.”

An existing medical example, he says, is the HEART Score. It rates five factors, such as age and ECG results, to quickly assess the risk that a patient with chest pain will have a major cardiac event within six weeks.

In simulations, Ghuge tested his algorithm against a sequential one on the same sets of data. His got results over 100 times as fast as the sequential algorithm, at a cost that averaged 22% higher.

“The tests are a bit more costly,” he says. “The trade-off is that you can get them done a lot faster.”

But he also notes that a single batch of tests might reduce setup costs, he says, compared with the expenses of setting up one test after another.

A next step, Ghuge hopes, is to try out his algorithm on real-life testing. A broadband internet network, such as Google Fiber or Spectrum, might use it for daily testing, to rapidly diagnose whether a system or subsystem is working.

“I come from a more theoretical background that focuses on the right model,” he says. “There’s a gap between that and applying it in practice. I’m excited to speak with people, to talk to practitioners and see if these can be applied.”

A new study has revealed that artificial intelligence can now generate images of real people that are virtually impossible to tell apart from genuine photographs.

Using AI models ChatGPT and DALL·E, a team of researchers from Swansea University, the University of Lincoln and Ariel University in Israel, created highly realistic images of both fictional and famous faces, including celebrities.

They found that participants were unable to reliably distinguish them from authentic photos—even when they were familiar with the person’s appearance.

Across four separate experiments, the researchers noted that adding comparison photos or the participants’ prior familiarity with the faces provided only limited help.

The research has just been published in the journal Cognitive Research: Principles and Implications and the team say their findings highlight a new level of “deepfake realism,” showing that AI can now produce convincing fake images of real people which could erode trust in visual media.

Professor Jeremy Tree, from the School of Psychology, said, “Studies have shown that face images of fictional people generated using AI are indistinguishable from real photographs. But for this research we went further by generating synthetic images of real people.

“The fact that everyday AI tools can do this not only raises urgent concerns about misinformation and trust in visual media but also the need for reliable detection methods as a matter of urgency.”

One of the experiments, which involved participants from the US, Canada, the UK, Australia and New Zealand, saw subjects shown a series of facial images, both real and artificially generated, and they were asked to identify which was which. The team say the fact the participants mistook the AI-generated novel faces for real photos indicated just how plausible they were.

Another experiment saw participants asked if they could tell genuine pictures of Hollywood stars such as Paul Rudd and Olivia Wilde from computer-generated versions. Again, the study’s results showed just how difficult individuals can find it to spot the authentic version.

The researchers say AI’s ability to produce novel/synthetic images of real people opens up a number of avenues for use and abuse. For instance, creators might generate images of a celebrity endorsing a certain product or political stance, which could influence public opinion of both the identity and the brand/organization they are portrayed as supporting.

Professor Tree added, “This study shows that AI can create synthetic images of both new and known faces that most people can’t tell apart from real photos. Familiarity with a face or having reference images didn’t help much in spotting the fakes. That is why we urgently need to find new ways to detect them.

“While automated systems may eventually outperform humans at this task, for now, it’s up to viewers to judge what’s real.”