Plastic waste poses an urgent problem for our planetā€™s ecosystems, especially our waterways. Millions of tons of plastic waste enter Earthā€™s oceans every year, and plastic has been found in every part of the ocean, including at the bottom of the deepest ocean trenches.

Although some biodegradable plastics, or bioplastics, have recently been developed, these plastics were intended to break down in industrial compost facilities. In cold, dark ocean environments, they break down very slowly.

What if there were a way to avoid the problem of plastic pollution while still reaping the benefits of plasticā€™s durability, versatility, and low cost?

To help tackle this problem, , an associate professor in the ā€™s and her colleagues developed a reusable 3D-printed ā€œbio-stickerā€ that uses bacteria to break down bioplastic. The sticker, described in in ACS Applied Polymer Materials, offers a controllable way to speed up plastic disintegration in environments where the plastic would otherwise linger for decades.

ā€œThis is a proof-of-concept that we could use living, engineered materials to help get rid of plastic in marine environments, making bioplastics more practical and environmentally friendly,ā€ Meyer says.

The project is part of a larger collaboration with marine microbiologist Alyson Santoro at the University of California, Santa Barbara; University of Rhode Island oceanographer Melissa Omand; ecologist Ryan Freedman from the Channel Islands National Marine Sanctuary; and industry partner .

Supported by a $5 million National Science Foundation grant as part of the NSFā€™s program, the group is testing the biodegradable bioplastic and developing solutions to accelerate breakdown.

Meyer, Santoro, and Omand additionally founded a start-up company called , which aims to make the ocean-degradable plastics available for various marine applications.

Rethinking ocean instruments

Ocean-degradable plastics will be vital for oceanographers, who are increasingly reliant on expendable, plastic instruments to observe and predict ocean phenomena. These instruments are often deployed in the ocean and never retrieved, adding to the growing amount of plastic in the sea.

ā€œWhile these expendableĀ ocean sensors are revolutionizing ocean research, they inherently pose a threat to the same environments that they are studying,ā€ Meyer says. ā€œWe need new materials that can allow oceanographers to monitor the oceans without creating plastic ocean waste that gets left behind.ā€

The team has partnered with a handful of oceanographic equipment manufacturers who have committed to replace all, or a large portion of, their traditional petro-chemical plastic parts with the teamā€™s ocean-degradable materials.

ā€œThis will introduce new sustainability into the fields of ocean observation, reef restoration, and maritime defense,ā€ Meyer says.

Nature-inspired plastics

To create their ocean-degradable plastic, the team drew upon processes already found in nature. Their materials are based on a biopolymer called polyhydroxybutyrate (PHB)ā€”a polyester naturally made by bacteria. Because bacteria have been making this polymer for billions of years, other marine microbes have naturally evolved to break down PHB.

At UC Santa Barbara, Santoro and her lab partners culture new bacteria that can break down PHB. One focus of their work is to isolate bacteria that thrive in the cold conditions of the ocean.

ā€œWe found that thereā€™s a huge need for biodegradable materials and there is a range of lifespans that users required for their items,ā€ she adds. The team spoke with regulators and nonprofits that deal with marine debris and found that some groups wanted a material that could disappear in a day, others wanted devices that would last a year, and yet others wanted to be able to trigger the degradation.

Bio-stickers that degrade plastic

This is where Meyerā€™s lab comes in. Meyer and the members of her lab have developed first-of-their-kind bacterial 3D printers. This revolutionary 3D-bioprinting approach allows them to embed PHB-degrading bacteria into engineered living materials.

The resulting ā€œbio-stickersā€ are made with salt-tolerant bacteria suspended in a gel-like material. Users can place the stickers directly onto PHB-based bioplastics, where the bacteria remain alive and active for at least three weeks and speed up the materialā€™s breakdown. The rate of degradation can be tuned by adjusting factors such as bacterial concentration or temperature. The stickers are also reusable, allowing them to be moved from one piece of plastic to another, and are stable and adhesive enough to be used in marine environments.

From prototype to ocean deployment

The team developed the bioplastics with input from industry partners and built a prototype with support from Omand at the University of Rhode Island, whose expertise in oceanographic sensor design helped shape the technology.

In collaboration with more than a dozen industry and government partners that committed to using the technology or supported the project in other ways, the researchers also tested how the bioplastics performed under different ocean conditions as well as how the material breaks down in marine environments.

The work could pave the way for engineered living materials that help create more sustainable, environmentally friendly alternatives to traditional plastics.

ā€œAfter introducing our ocean-degradable bioplastic to ocean instruments, we plan to expand to other applications as well,ā€ Meyer says. ā€œOur tough plastics that break down in the ocean could be a great fit for aquaculture and fishing industries, ecosystem restoration efforts, maritime defense, or government agencies, such as the NOAA (National Oceanic and Atmospheric Administration) National Data Buoy Center.ā€

Editorā€™s note: The story above was initially published on October 6, 2022. It has been updated and republished to reflect new research related to the project.

When a person goes into deep sleep, water-like fluid circulates around the brain, washing away metabolic waste linked to diseases such as Alzheimerā€™s. This process, known as the glymphatic system, was first described in 2012 by ā€”a pioneering neuroscientist and codirector of the °µĶų³Ō¹Ļā€™s .

But questions remain about the systemā€™s mechanicsā€”notably, how quickly the fluid circulates. Studying the circulation within a living brain is difficult without causing irreparable harm to a subject.

ā€œYou can put a microscope on a small patch of the brain and watch whatā€™s happening there with a lot of detail, and weā€™ve worked with that type of data in the past, but itā€™s only a tiny view of the overall process,ā€ says Professor from °µĶų³Ō¹Ļā€™s . ā€œIf you want to image whole brains, an MRI is a great approach because it gives you a three-dimensional view. But an MRI has serious limitations, too, the biggest of which is that it does not capture the fluid flow velocity, at least not for flows this slow.ā€

Kelley and his colleagues from °µĶų³Ō¹Ļ, Brown University, and the University of Copenhagen turned to artificial intelligence for help. In a new published in Science Advances, they outline how they used physics-informed AI to determine fluid flow velocities from magnetic resonance imaging (MRI) data. Using videos of dye spreading across brain tissue over time, the neural networks the researchers built were able to deduce how fast the fluid flows and how permeable the brain tissue is.

The results showed that there are two main ways that the glymphatic system washes away particles in the brain such as the amyloid beta proteins linked to Alzheimerā€™s diseaseā€”and one of these ways is much faster than the other. The fast flow of the glymphatic systemā€™s waterlike fluid moves at a few microns per second around the brainā€™s open regions such as the surface between the skull and the brain, while the slower flow of the waterlike fluid trickles through the brainā€™s deep tissue at a rate about 50 times slower.

So far, the researchers have been working to get baseline measurements of fluid flow in the brains of animals such as mice to inform the AI tools. In the future, they hope to be able to compare the fluid flow in healthy and sick brains as well as young and old brains, with aspirations to eventually study circulation in humans.

ā€œWeā€™re working hard toward being able to measure the flow of waterlike fluids in and around human brains because then the clinical applications get a lot more important and exciting,ā€ says Kelley. ā€œWe hope to someday be able to see whether an Alzheimerā€™s patient has poor circulation in their brain or even screen for poor circulation earlier in life to try to stave off Alzheimerā€™s. Or we could check when somebody has been concussed to see whether the fluid circulation in their brain is disrupted. This study gets us a step closer.ā€

Kelleyā€™s collaborators on the study include Brown University PhD student Juan Diego Toscano, °µĶų³Ō¹Ļ computational scientist Yisen Guo, Brown University PhD student Zhibo Wang, °µĶų³Ō¹Ļ PhD student Mohammad Vaezi, University of Copenhagen Associate Professor Yuki Mori, Brown University Professor George Karniadakis, and °µĶų³Ō¹Ļ Assistant Professor Kimberly Boster.

The NIH National Center for Complementary and Integrative Health and the NIH BRAIN Initiative supported this research.

Advances in artificial intelligence promise to help chemical engineers discover complex new materials. These materials could be used for reactions such as turning carbon dioxide into fuel, but technical barriers have limited catalysis adoption so far. Researchers at the °µĶų³Ō¹Ļ are now harnessing the benefits of large language models (LLMs) similar to ChatGPT, Claude, or Gemini to empower more researchers to use AI to discover new materials and accelerate experiment workflows.

In a published in ACS Central Science, a team led by , an associate professor in the , and , visiting associate professor and the cofounder and chief technology officer of , describes an AI-based method they developed that allows users to input natural language prompts about the materials they want to create and suggest optimal procedures for experiments to produce them. As the users run the experiments, they input the results back into the AI model and continue iterating until they reach their goal.

ā€œWeā€™re able to leverage the pre-trained knowledge of large language models and well-established statistical methods for materials discovery to help us as researchers navigate large experimental design spaces more efficiently,ā€ says Porosoff.

Porosoff likens the new AI method to describing a cup of coffee, noting that someone could describe the coffee by its taste, color, and aroma, or by the type of beans, grind size, apparatus, and water temperature used to make the brew. Both representation methods describe the same cup of coffee, but the second approach gives you a recipe to reproduce it that others can easily replicate.

Porosoff and his team are applying the same principle to catalysts for energy applications, using language-based representations to describe materials not just by their properties, but by the steps needed to create them.

To build on their success, the US Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) it will provide nearly $3 million in funding to apply the University of Rochester teamā€™s method toward creating catalysts for the production of fuel from abundant materials, specifically methanol and ethanol from carbon dioxide and hydrogen. Porosoff will lead a multi-institution project team that includes °µĶų³Ō¹Ļ, Virginia Polytechnic Institute and State University, Stanford University, Northwestern University, A*STAR Institute of Sustainability for Chemicals, Energy and Environment (ISCE2) in Singapore, and OxEon Energy, a small business based in Salt Lake City.

Leveraging the power of LLMs

Traditional AI methods for materials discovery typically use a strategy called Bayesian optimization to identify and design the best candidates. But the result is complex numerical data about a materialā€™s structure, which requires deep expertise to use effectively. The new LLM method instead produces a set of procedures that researchers can easily understand, execute, and verify to determine if the experimentā€™s output matches the predicted results.

This can be extremely useful for working with complex materials such as trimetallic catalysts, which are made of three metals.

ā€œOur method reduces the technical barrier associated with using Bayesian optimization, which is a well-established method for efficiently exploring large and complicated parameter spaces,ā€ says Shane Michtavy, a °µĶų³Ō¹Ļ chemical engineering PhD student who helped develop the AI method, synthesize materials, and run the chemical reactions described in the paper. ā€œUsing pre-trained LLMs allows users to explore using less data than traditional models, as they are deployed in a frozen state with built-in knowledge of the physical world and catalysis.ā€

The paper shows how the researchers applied the method to several live experiments, including one to identify catalysts for turning carbon dioxide and hydrogen into carbon monoxide and water using trimetallic catalysts made from low-cost metals. Porosoff says that there are about 360,000 possible experiments that could have been run to find the ideal catalyst, but by using procedures produced by the AI model and providing it with the results from the experiments, they were able to find an ideal candidate in just ten experiments.

The study was supported by funding from the National Science Foundation, the National Institutes of Health, and the US Department of Energy. Additional authors includedĀ Mayk Caldas, technical staff at Edison Scientific.

Next steps

Now that they have shown the model works as a proof of concept in the lab, Porosoff aims to take the method further using the funding announced through ARPA-Eā€™s Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program.

ā€œRight now, it takes a decade or longer to go from conceptualizing a new catalyst to testing it in a lab to putting it in a real reactor,ā€ says Porosoff. ā€œThe CATALCHEM-E program aims to cut that by an order of magnitude to a single year, and we think using AI with text-based representations will be a big factor in shortening the development cycle.ā€

Porosoff and his collaborators will first demonstrate their workflow on carbon dioxide-to-methanol and then extend the process to higher alcohols such as ethanol, which is a key additive for gasoline and used in pharmaceuticals, cosmetics, and many other applications. Ultimately, they hope to commercially deploy the model for industries to create catalysts to synthesize alcohols for fuel.

The project is scheduled to begin in July and run through 2029. See a full list of CATALCHEM-E programs on the .

The worldā€™s oceans may be quietly amplifying climate change in ways scientists are only beginning to understand.

In a published in the journal Proceedings of the National Academy of Sciences, ²õ³¦¾±±š²Ō³Ł¾±²õ³Ł²õā€”i²Ō³¦±ō³Ü»å¾±²Ō²µ , an associate professor in the , as well as graduate student Shengyu Wang and postdoctoral research associate Hairong Xu in Weberā€™s labā€”uncovered a key mechanism behind methane production in the open ocean. Their research indicates that this mechanism could intensify as the planet warms, providing an alarming feedback loop for global warming.

Methane is a powerful greenhouse gas, and for decades scientists have puzzled over a paradox: surface ocean waters consistently release methane into the atmosphere, even though surface water is rich in oxygen. Traditionally, methane production has been associated with oxygen-free environments, such as wetlands or deep sediments.

Weberā€™s team set out to solve this puzzle using a global dataset and computer modeling. Their findings point to a specific microbial process that is responsible for methane production in the ocean environment: certain bacteria generate methane as a byproduct when they break down organic compounds, but they only do this when the nutrient phosphate is scarce.

ā€œThis means that phosphate scarcity is the primary control knob for methane production and emissions in the open ocean,ā€ Weber says.

The findings reframe how scientists understand methane production in the ocean. Rather than being a rare or unusual process, methane production in oxygen-rich environments may be widespread in regions where phosphate is limited.

But the study extends further than explaining marine methane production in the presentā€”it also offers a troubling glimpse into the future.

ā€œClimate change is warming the ocean from the top down, increasing the density difference between surface and deep waters,ā€ Weber says. ā€œThis is expected to slow the vertical mixing that carries nutrients like phosphate up from depth.ā€

According to the teamā€™s model, with less vertical mixing, surface waters could become increasingly nutrient-starved, creating ideal conditions for methane-producing microbes to thrive.

The result, Weber warns, would be more methane released from the ocean into the atmosphere. Because methane is such a potent greenhouse gas, this creates the potential for a harmful feedback loop: warming oceans lead to more methane emissions, which in turn drive further warming.

The findings highlight how even processes occurring at the microscopic level in the ocean can have global consequences.

Crucially, this feedback is not currently included in major climate projection models. As researchers continue to refine climate models, incorporating feedbacks such as this may be essential for accurately predicting the pace and scale of future climate change.

ā€œOur work will help fill a key gap in climate predictions, which often overlook interactions between the changing environment and natural greenhouse gas sources to the atmosphere,ā€ Weber says.

When lasers were invented in the 1960s, they opened new avenues for scientific discovery and everyday applications from scanners at the grocery store to corrective eye surgery. Conventional lasers control photonsā€”individual particles of lightā€”but over the past 20 years, scientists have invented lasers that control other fundamental particles, including phononsā€”individual particles of vibration or sound. Controlling phonons could open even more possibilities with lasers, such as taking advantage of unique quantum properties like entanglement.

A new squeezed phonon laser developed by researchers at the and Rochester Institute of Technology provides precise control over phonons at the nanoscale level. This could give new insights into the nature of gravity, particle acceleration, and quantum physics. In in Nature Communications, the researchers describe how they coax these individual particles of mechanical motion to behave like a laser.

, the Marie C. Wilson and Joseph C. Wilson Professor of Optical Physics with the University of Rochester , and his collaborators first demonstrated a phonon laser by trapping and levitating phonons with an optical tweezer in a vacuum in 2019. But to make this technology useful for extremely accurate measurements, they had to overcome a key obstacle fundamental to both photon and phonon lasers: noise, or unwanted disturbances that make a signal difficult to accurately read.

ā€œWhile a laser looks to the naked eye like a steady beam, thereā€™s actually a lot of fluctuation, which causes noise when youā€™re using lasers for measurement,ā€ says Vamivakas. ā€œBy pushing and pulling on a phonon laser with light in the right way, we can reduce that phonon laser fluctuation significantly.ā€

Specifically, the researchers were able to squeeze or reduce the thermal noise intrinsic to the phonon laser. Vamivakas says that noise reduction provides the ability to measure acceleration more accurately than techniques that use photon lasers or radio frequency waves.

Vamivakas envisions researchers using the phonon laser to obtain pinpoint accurate measurements of gravity and other forces, which could be important in applications such as navigation. Scientists have envisioned quantum compasses as more accurate, ā€œunjammableā€ alternatives to GPS navigation that do not require the use of satellites, and Vamivakas is intrigued by seeing if the phonon laser could be a step toward such systems.

The research was supported by the National Science Foundation. Vamivakasā€™ collaborators on the paper include °µĶų³Ō¹Ļ optics PhD student Kai Zhang, RIT postdoctoral researcher Kewen Xiao, and Mishkat Bhattacharya ā€™05, a professor of physics at RIT.

The °µĶų³Ō¹Ļā€™s (LLE) and have established a $6.9 million partnership, the largest single industrial-sponsored research agreement awarded to LLE, to address fundamental challenges inĀ Ā and accelerate progress toward practical, sustainable fusion power.