Carbon-supported single-atom catalysts with metal-N moieties are highly promising for lithium–sulfur batteries. They can enhance redox kinetics and suppress the dissolution of lithium polysulfides. However, carbon substrate structure optimization and catalyst coordination environment modulation must be done simultaneously to maximize the potential of these catalysts.

Taking on this challenge, a team of researchers led by two associate professors from Chung-Ang University—Seung-Keun Park from the Department of Advanced Materials Engineering and Inho Nam from the Department of Chemical Engineering—has demonstrated dual‑level engineering of metal–organic framework (MOF)‑derived hierarchical porous carbon nanofibers with low‑coordinated cobalt single‑atom catalysts for high‑performance lithium–sulfur batteries. Their novel findings were published in Advanced Fiber Materials on 24 September 2025.

Dr. Park says, “Our motivation lies in addressing the fundamental materials challenges that have limited the development of next-generation energy storage systems. Lithium-ion batteries have been widely adopted but are approaching their intrinsic energy density limits.

“Lithium sulfur batteries offer much higher theoretical capacity and energy density, yet they are severely restricted by the polysulfide shuttle effect, slow redox kinetics, and rapid capacity fading. Our group has long been committed to overcoming these bottlenecks by combining structural engineering of carbon frameworks with atomic-level catalyst design.”

In this study, the researchers focused on embedding single cobalt atoms in a low-coordinated N₃ environment within a porous carbon nanofiber network. This approach enhances the adsorption of lithium polysulfides and accelerates their redox reactions, thereby mitigating the shuttle effect and improving overall kinetics. Therefore, the present work supports the belief that rational materials design at both the macro and atomic levels can solve long-standing challenges.

From a materials perspective, the proposed dual-level engineering strategy integrates a hierarchical porous carbon nanofiber structure with atomically dispersed cobalt single-atom sites in a low-coordinated N₃ configuration. The carbon nanofiber provides mechanical stability, abundant pore channels, and excellent electrolyte wettability, while the cobalt sites catalyze the adsorption and conversion of polysulfides. This synergistic design allows the battery to achieve high-capacity retention and superior rate performance over hundreds of cycles.

In the long term, the results of this study could contribute to the realization of high-performance lithium sulfur batteries for diverse real-life applications. These include electric vehicles with extended driving ranges, large-scale renewable energy storage systems that can balance intermittent solar and wind power, and lightweight, flexible power sources for portable and wearable electronics.

“Our material is free standing, binder free, and flexible. It can be directly applied as an interlayer in pouch cells and has been demonstrated to maintain mechanical integrity even under bending, while powering small devices,” points out Dr. Nam, highlighting the immense practical implications of their work.

For society, such advances mean safer and more efficient batteries that accelerate the transition to clean energy. This can reduce dependence on critical raw materials, lower costs, decrease carbon emissions, and ultimately make sustainable technologies more reliable and accessible in everyday life.

On Thursday, Tesla shareholders approved an unprecedented $1 trillion pay package for CEO Elon Musk. The full compensation plan will go into effect by 2035—assuming Musk and the company successfully hit ambitious financial and production targets. If that happens, Musk will also get control of some 25 percent of the business, up from the 12 percent he controls currently. More than 75 percent of Tesla shareholders approved the move in a preliminary vote.

Musk celebrated the news onstage at Tesla’s Gigafactory in Austin, Texas, appearing alongside two dancing humanoid robots, the company’s Optimus products. “Look at us, this is sick,” he said.

To meet its goals, however, Tesla will have to lead in industries well beyond electric cars—and guarantee that Optimus can do much more than dance. It will also have to beat all competitors in autonomous driving technology and robotics. “Tesla will have to be the market leader not just in the US but also Europe and other regions,” says Seth Goldstein, a senior equity analyst at Morningstar, a financial services firm.

Specifically, Tesla needs to hit an$8.5 trillion valuation over the next 10 years, deliver 20 million vehicles to customers, send out 1 million robots, operate 1 million robotaxis, and sell 10 million subscriptions for its “Full Self-Driving” software over a three-month period—in addition to other financial targets.

Still, the vote marks a win for Musk, whose previous package, a $50 billion payday laid out in 2018, has been caught up in litigation after a shareholder alleged that the CEO had too much influence over the company’s board and that Tesla was therefore failing to uphold its legal obligations to shareholders. The lawsuit, brought in Delaware’s Chancery Court, led to Tesla reincorporating in Texas. A panel of judges heard the case on appeal in October; they’ll likely make a final decision in the coming months.

Before the vote, Tesla’s board argued the sky-high pay package was necessary to retain Musk as CEO—and keep him focused on the car company. In a call with investors last month, Musk suggested that he would have a hard time pushing Tesla ahead in robotics and autonomy if he didn’t have a strong sway over the automaker. “If we build this robot army, do I have at least a strong influence over this robot army?” he asked. “I don’t feel comfortable building that robot army unless I have a strong influence.”

Following Thursday’s vote, Musk told investors gathered in Texas that production of the Cybercab, a self-driving vehicle that lacks a steering wheel or sideview mirrors, would begin in April. The company will need permission from the federal government to put the unconventionally designed car on the road.

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.”