Researchers at Caltech have made a breakthrough in developing a simple algorithm for underwater robots to harness the power of ocean currents. Led by John Dabiri, the Centennial Professor of Aeronautics and Mechanical Engineering, the team has successfully created a system that allows small autonomous underwater vehicles (AUVs) to ride on turbulent water currents rather than fighting against them.

The researchers began by studying how jellyfish navigate through the ocean using their unique ability to traverse and plumb the depths. They outfitted these creatures with electronics and prosthetic “hats” to carry small payloads and report findings back to the surface. However, they soon realized that jellyfish do not have a brain and therefore cannot make decisions about how to navigate.

To address this limitation, Dabiri’s team developed what would be considered the equivalent of a brain for an AUV using artificial intelligence (AI). This allowed the robots to make decisions underwater and potentially take advantage of environmental flows. However, they soon discovered that AI was not the most efficient solution for their problem.

Enter Peter Gunnarson, a former graduate student who returned to Dabiri’s lab with a simpler approach. He attached an accelerometer to CARL-Bot, an AUV developed years ago as part of his work on incorporating artificial intelligence into its navigation technique. By measuring how CARL-Bot was being pushed around by vortex rings (underwater equivalents of smoke rings), Gunnarson noticed that the robot would occasionally get caught up in a vortex ring and be propelled clear across the tank.

The team then developed simple commands to help CARL-Bot detect the relative location of a vortex ring and position itself to catch a ride. Alternatively, the bot can decide to get out of the way if it does not want to be pushed by a particular vortex ring. This process involves elements of biomimicry, mimicking nature’s ability to use environmental flows for energy conservation.

Dabiri hopes to marry this work with his hybrid jellyfish project, which aims to demonstrate a similar capability to take advantage of environmental flows and move more efficiently through the water. With this breakthrough, underwater robots can now ride the tides, reducing energy expenditure and increasing their efficiency in navigating the ocean depths.

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Harnessing Sunlight: A Breakthrough in Carbon Capture Technology

Scientists at Cornell University have developed a groundbreaking method to capture and release carbon dioxide using an energy source that’s abundant, clean, and free: sunlight. This innovative approach mimics the way plants store carbon, making it a game-changer in the fight against global warming.

The research team, led by Phillip Milner, associate professor of chemistry and chemical biology, has created a light-powered system that can separate carbon dioxide from industrial sources. They’ve used sunlight to make a stable enol molecule reactive enough to “grab” the carbon, and then driven an additional reaction to release the carbon dioxide for storage or reuse.

This is the first light-powered separation system for both carbon capture and release, and it has significant implications for reducing costs and net emissions in current methods of carbon capture. The team tested their system using flue samples from Cornell’s Combined Heat and Power Building, and it was successful in isolating carbon dioxide, even with trace contaminants present.

Milner is excited about the potential to remove carbon dioxide from air, which he believes is the most practical application. “Imagine going into the desert, you put up these panels that are sucking carbon dioxide out of the air and turning it into pure high-pressure carbon dioxide,” he said. This could then be put in a pipeline or converted into something on-site.

The research team is also exploring how this light-powered system could be applied to other gases, as separation drives 15% of global energy use. “There’s a lot of opportunity to reduce energy consumption by using light to drive these separations instead of electricity,” Milner said.

The study was supported by the National Science Foundation, the U.S. Department of Energy, the Carbontech Development Initiative, and Cornell Atkinson. This breakthrough has the potential to revolutionize carbon capture technology and make it more efficient, effective, and sustainable.

A team of researchers from Rice University and Baylor College of Medicine has developed an innovative strategy for identifying toxic compounds in soil, including those that have never been isolated or studied before. The new approach uses machine learning algorithms, theoretical predictions, and light-based imaging techniques to detect polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs), which are linked to cancer and other serious health problems.

The researchers used surface-enhanced Raman spectroscopy, a light-based imaging technique that analyzes how light interacts with molecules, tracking the unique patterns or spectra they emit. These spectra serve as “chemical fingerprints” for each compound. To refine this method, the team designed signature nanoshells to enhance relevant traits in the spectra.

Using density functional theory, a computational modeling technique, the researchers calculated the spectra of a range of PAHs and PACs based on their molecular structure, generating a virtual library of “fingerprints.” Two complementary machine learning algorithms – characteristic peak extraction and characteristic peak similarity – were then used to parse relevant spectral traits in real-world soil samples and match them to compounds mapped out in the virtual library.

This method addresses a critical gap in environmental monitoring, opening the door to identifying a broader range of hazardous compounds, including those that have changed over time. The researchers tested this approach on soil from a restored watershed and natural area using artificially contaminated samples and a control sample, with results showing the new method reliably picked out even minute traces of PAHs.

The future holds promise for on-site field testing by integrating machine learning algorithms and theoretical spectral libraries with portable Raman devices into mobile systems. This would enable farmers, communities, and environmental agencies to test soil for hazardous compounds without needing to send samples to specialized labs and wait days for results.