A turbine-inspired generator shows how overlooked industrial airflow could quietly become a new source of usable power
Updated
February 12, 2026 4:43 PM
Campus building of Chung-Ang University. PHOTO: CHUNG-ANG UNIVERSITY
Compressed air is used across factories, data centers and industrial plants to move materials, cool systems and power tools. Once it has done that job, the air is usually released — and its remaining energy goes unused.
That everyday waste is what caught the attention of a research team at Chung-Ang University in South Korea. They are investigating how this overlooked airflow can be harnessed to generate electricity instead of disappearing into the background.
Most of the world’s power today comes from systems like turbines, which turn moving fluids into energy or solar cells, which convert sunlight into electricity. The Chung-Ang team has built a device that uses compressed air to generate electricity without relying on traditional blades or sunlight.
At the center of the work is a simple question: what happens when high-pressure air spins through a specially shaped device at very high speed? The answer lies in the air itself. The researchers found that tiny particles naturally present in the air carry an electric charge. When that air moves rapidly across certain surfaces, it can transfer charge without physical contact. This creates electricity through a process known as the “particulate static effect.”
To use that effect, the team designed a generator based on a Tesla turbine. Unlike conventional turbines with blades, a Tesla turbine uses smooth rotating disks and relies on the viscosity of air to create motion. Compressed air enters the device, spins the disks at high speed and triggers charge buildup on specially layered surfaces inside.
What makes this approach different is that the system does not depend on friction between parts rubbing together. Instead, the charge comes from particles in the air interacting with the surfaces as they move past. This reduces wear and allows the generator to operate at very high speeds. And those speeds translate into real output.
In lab tests, the device produced strong electrical power. The researchers also showed that this energy could be used in practical ways. It ran small electronic devices, helped pull moisture from the air and removed dust particles from its surroundings.
The problem this research is addressing is straightforward.
Compressed air is already everywhere in industry, but its leftover energy is usually ignored. This system is designed to capture part of that unused motion and convert it into electricity without adding complex equipment or major safety risks.
Earlier methods of harvesting static electricity from particles showed promise, but they came with dangers. Uncontrolled discharge could cause sparks or even ignition. By using a sealed, turbine-based structure, the Chung-Ang University team offers a safer and more stable way to apply the same physical effect.
As a result, the technology is still in the research stage, but its direction is easy to see. It points toward a future where energy is not only generated in power plants or stored in batteries, but also recovered from everyday industrial processes.
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Sonilo and Shutterstock are betting that licensed training data could define the future of AI music.
Updated
May 13, 2026 3:39 PM
A human operating a digital turntable. PHOTO: UNSPLASH
As copyright disputes continue to grow around AI-generated music, Sonilo, the world’s first professionally licensed video-to-music AI platform, has partnered with Shutterstock to train its models on licensed music catalogs.
The agreement gives Sonilo access to Shutterstock’s music library for AI model training. According to the companies, it is Shutterstock’s first partnership with a video-to-music AI platform and the timing is significant. AI music companies are facing growing pressure over how their systems are trained. Artists and record labels have increasingly challenged the use of copyrighted music in AI datasets, especially when licensing agreements or compensation structures are unclear.
That tension has created a divide across the industry. Some companies have continued building models around scraped or disputed data. Others are trying to position licensing as part of the product itself.
Sonilo falls into the second group. The company says its models are trained only on licensed material where artists and rights holders have agreed to participate and receive compensation. The Shutterstock partnership strengthens that position while giving Sonilo access to a larger pool of commercially cleared music.
The collaboration also points to a broader change happening inside generative AI. As AI tools move into commercial production, companies are being pushed to show not just what their models can generate, but also where their training data comes from.
Sonilo’s platform is built around video rather than text prompts. The system analyses footage directly, studies pacing and emotional tone, then generates an original soundtrack to match the content. The company says this removes the need for manual music searches, syncing or editing workflows. The generated tracks are cleared for commercial use across social media, branded content and broadcast production.
Shawn Song, CEO of Sonilo, said: "Music has always been the last unsolved layer of video creation, and video has always carried its own soundtrack. We built Sonilo to hear it and compose from it, without a single text prompt. But how we build matters as much as what we build. While others have chosen to take artists' work without permission and charge creators for the privilege, we've chosen a different path—one where artists are compensated from day one. Partnering with Shutterstock reflects that standard. Every model we train meets a bar the music industry can stand behind, because the most innovative AI platforms don't have to come at the expense of the artists who make all of these possible."
For Shutterstock, the deal expands the company’s growing role in generative AI infrastructure. The company has increasingly focused on licensing content for AI systems across images, video and music.
Jessica April, Vice President of Data Licensing & AI Services at Shutterstock, said: "AI innovation depends on access to high-quality, rights-cleared content and trusted licensing partnerships. Sonilo's approach reflects the growing demand for responsibly sourced training data and commercially safe AI workflows. We're pleased to support companies building generative AI products with licensed content and scalable data solutions that help accelerate innovation while respecting creators and rights holders."
The partnership also comes as Sonilo expands into creator and developer ecosystems. Earlier this month, the company launched as a native node inside ComfyUI, an open-source AI workflow platform used by millions of creators. Sonilo also offers API access for integration into creator tools, video platforms, game engines and other AI systems.
As AI-generated music becomes more common across advertising, creator platforms and digital media, the industry’s focus is shifting beyond generation alone. Questions around licensing, ownership and compensation are increasingly shaping how AI music companies position themselves and build trust with creators.