📌 TOPINDIATOURS Breaking ai: Ion beams simulate decades of reactor damage 1,000x f

Ion beams can now qualify nuclear reactor materials up to 1,000 times faster than conventional test reactors, according to researchers led by the University of Michigan.

The method compresses what would normally take more than a decade of neutron irradiation into just days inside a laboratory accelerator.

The approach, called Qualification under Ion irradiation of Core Components, or QUICC, is advancing through approval stages at ASTM International, the industry standards body.

It will be presented at a special event hosted by the Electric Power Research Institute in March.

Advanced fission and proposed fusion reactors are expected to expose core materials to radiation levels that existing test reactors struggle to replicate within practical timelines.

Some components must withstand up to 200 displacements per atom, or dpa, a measure of how often atoms are knocked out of position inside a material’s crystal lattice.

Reaching such damage levels inside a test reactor can take years. With ion beams, researchers say they can deliver equivalent damage in days and at a fraction of the cost.

Radiation damage in days

The central question for more than 35 years has been whether ion irradiation can truly reproduce the complex damage caused by neutron bombardment inside a reactor core. According to the team behind QUICC, the answer is yes.

“The QUICC methodology, applied to two very different alloys, demonstrates that the critical changes to the materials under ion irradiation mimic those under reactor irradiation. The significance is that ion irradiation can be used to predict material behavior in reactors 1000 times faster than with test reactors and at one one-thousandth the cost,” said Gary Was, professor emeritus of nuclear engineering and radiological sciences at the University of Michigan.

In conventional neutron testing, materials are placed inside operating reactors and exposed for years. Ion irradiation instead uses particle accelerators to bombard samples with controlled beams.

The bulk of atomic displacements are generated using heavy ions, typically matching the dominant metal in the alloy to avoid altering its chemistry.

To replicate helium production inside fission reactors, the team added a helium ion beam.

They also developed a target chamber that submerges samples in high-temperature, pressurized water while irradiation is underway, simulating reactor core conditions.

For fusion environments, the setup becomes more complex. In addition to heavy ions and helium, hydrogen ions are introduced simultaneously.

This triple-beam configuration recreates the combined radiation damage and gas accumulation expected in future fusion reactor components.

Fusion materials under fire

The metric dpa captures the cumulative disruption inside metals as atoms are displaced repeatedly.

At levels approaching 200 dpa, materials can become brittle, form cavities, swell, and develop helium bubbles that weaken structural integrity. Being able to reach such damage thresholds quickly enables faster iteration of alloy design and validation.

The work has been supported by the U.S. Department of Energy, Electric Power Research Institute, Oak Ridge National Laboratory, Framatome, and Rolls-Royce. The core research team includes scientists from the University of Michigan, Pennsylvania State University, Oak Ridge National Laboratory, and the University of Tennessee.

By moving toward ASTM standardization, QUICC could shift material qualification from decade-long reactor campaigns to laboratory-based ion testing.

That change could accelerate deployment timelines for advanced nuclear and fusion systems that depend on durable core components.

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Hot ai: Railway secures $100 million to challenge AWS with AI-nati

Railway, a San Francisco-based cloud platform that has quietly amassed two million developers without spending a dollar on marketing, announced Thursday that it raised $100 million in a Series B funding round, as surging demand for artificial intelligence applications exposes the limitations of legacy cloud infrastructure.

TQ Ventures led the round, with participation from FPV Ventures, Redpoint, and Unusual Ventures. The investment values Railway as one of the most significant infrastructure startups to emerge during the AI boom, capitalizing on developer frustration with the complexity and cost of traditional platforms like Amazon Web Services and Google Cloud.

"As AI models get better at writing code, more and more people are asking the age-old question: where, and how, do I run my applications?" said Jake Cooper, Railway's 28-year-old founder and chief executive, in an exclusive interview with VentureBeat. "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up."

The funding is a dramatic acceleration for a company that has charted an unconventional path through the cloud computing industry. Railway raised just $24 million in total before this round, including a $20 million Series A from Redpoint in 2022. The company now processes more than 10 million deployments monthly and handles over one trillion requests through its edge network — metrics that rival far larger and better-funded competitors.

Why three-minute deploy times have become unacceptable in the age of AI coding assistants

Railway's pitch rests on a simple observation: the tools developers use to deploy and manage software were designed for a slower era. A standard build-and-deploy cycle using Terraform, the industry-standard infrastructure tool, takes two to three minutes. That delay, once tolerable, has become a critical bottleneck as AI coding assistants like Claude, ChatGPT, and Cursor can generate working code in seconds.

"When godly intelligence is on tap and can solve any problem in three seconds, those amalgamations of systems become bottlenecks," Cooper told VentureBeat. "What was really cool for humans to deploy in 10 seconds or less is now table stakes for agents."

The company claims its platform delivers deployments in under one second — fast enough to keep pace with AI-generated code. Customers report a tenfold increase in developer velocity and up to 65 percent cost savings compared to traditional cloud providers.

These numbers come directly from enterprise clients, not internal benchmarks. Daniel Lobaton, chief technology officer at G2X, a platform serving 100,000 federal contractors, measured deployment speed improvements of seven times faster and an 87 percent cost reduction after migrating to Railway. His infrastructure bill dropped from $15,000 per month to approximately $1,000.

"The work that used to take me a week on our previous infrastructure, I can do in Railway in like a day," Lobaton said. "If I want to spin up a new service and test different architectures, it would take so long on our old setup. In Railway I can launch six services in two minutes."

Inside the controversial decision to abandon Google Cloud and build data centers from scratch

What distinguishes Railway from competitors like Render and Fly.io is the depth of its vertical integration. In 2024, the company made the unusual decision to abandon Google Cloud entirely and build its own data centers, a move that echoes the famous Alan Kay maxim: "People who are really serious about software should make their own hardware."

"We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town."

The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout.

This soup-to-nuts control enables pricing that undercuts the hyperscalers by roughly 50 percent and newer cloud startups by three to four times. Railway charges by the second for actual compute usage: $0.00000386 per gigabyte-second of memory, $0.00000772 per vCPU-second, and $0.00000006 per gigabyte-second of storage. There are no charges for idle virtual machines — a stark contrast to the traditional cloud model where customers pay for provisioned capacity whether they use it or not.

"The conventional wisdom is that the big guys have economies of scale to offer better pricing," Cooper noted. "But when they're charging for VMs that usually sit idle in the cloud, and we've purpose-built everything to fit much more density on these machines, you have a big opportunity."

How 30 employees built a platform generating tens of millions in annual revenue

Railway has achieved its scale with a team of just 30 employees generating tens of millions in annual revenue — a ratio of revenue per employee that would be exceptional even for established software companies. The company grew revenue 3.5 times last year and continues to expand at 15 percent month-over-month.

Cooper emphasized that the fundraise was strategic rather than necessary. "We're default alive; there's no reason for us to raise money," he said. "We raised because we see a massive opportunity to accelerate, not because we needed to survive."

The company hired its first salesperson only last year and employs just two solutions engineers. Nearly all of Railway's two million users discovered the platform through word of mouth — developers telling other developers about a tool that actually works.

"We basically did the standard engineering thing: if you build it, they will come," Cooper recalled. "And to some degree, they came."

From side projects to Fortune 500 deployments: Railway's unlikely corporate expansion

Despite its grassroots developer community, Railway has made significant inroads into large organizations. The company claims that 31 percent of Fortune 500 companies now use its platform, though deployments range from company-wide infrastructure to individual team projects.

Notable customers include Bilt, the loyalty program company; Intuit's GoCo subsidiary; TripAdvisor's Cruise Critic; and MGM Resorts. Kernel, a Y Combinator-backed startup providing AI infrastructure to over 1,000 companies, runs its entire customer-facing system on Railway for $444 per month.

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🔗 Sumber: venturebeat.com