Fluid mechanics researchers from Brown University and the University of Toulouse found that surfactants give the celebratory drink its stable and signature straight rise of bubbles. 

Here are some scientific findings worthy of a toast: Researchers from Brown University and the University of Toulouse in France have explained why bubbles in Champagne fizz up in a straight line while bubbles in other carbonated drinks, like beer or soda, don’t.

The findings are based on a series of numerical and physical experiments, including, of course, pouring out glasses of Champagne, beer, sparkling water, and sparkling wine. The results not only explain what gives Champagne its line of bubbles but may hold important implications for understanding bubbly flows in the field of fluid mechanics. 

“This is the type of research that I've been working out for years,” said Brown Engineering professor Roberto Zenit, who was one of the paper’s authors. “Most people have never seen an ocean seep or an aeration tank but most of them have had a soda, a beer, or a glass of Champagne. By talking about Champagne and beer, our master plan is to make people understand that fluid mechanics is important in their daily lives.”

The team’s goal was to investigate the stability of bubble chains in carbonated drinks. Part of the signature experience of enjoying these beverages is the tiny or large bubbles that form when the drink is poured, creating a visible chain of bubbles and fizz. Depending on the drink and its ingredients, the fluid mechanics involved are all different.

When it comes to Champagne and sparkling wine, for instance, the gas bubbles that continuously appear rise rapidly to the top in a single-file line and keep doing so for some time. This is known as a stable bubble chain. With other carbonated drinks, like beer, many bubbles veer off to the side, making it look like multiple bubbles are coming up at once. This means the bubble chain isn’t stable.

The chain of bubbles from Champagne and sparkling wine rise in a straight line. The bubble chain in many beers veers off to the side when they rise, making it look like multiple bubbles rise at once. 

The researchers set out to explore the mechanics of what makes bubble chains stable and if they could recreate them, making unstable chains as stable as they are in Champagne or prosecco.

The results of their experiments indicate that the stable bubble chains in Champagne and other sparkling wines occur due to ingredients that act as soap-like compounds called surfactants. These surfactant-like molecules help reduce the tensions between the liquid and the gas bubbles, making for a smooth rise to the top.

“The theory is that in Champagne these contaminants that act as surfactants are the good stuff,” said Zenit, senior author of the paper. “These protein molecules that give flavor and uniqueness to the liquid are what makes the bubbles chains they produce stable.”

The experiments also showed the stability of bubbles is impacted by the size of the bubbles themselves. They found that the chains with large bubbles have a wake similar to that of bubbles with contaminants, leading to a smooth rise and stable chains.

In beverages, however, bubbles are always small. It makes surfactants the key ingredient to producing straight and stable chains. Beer, for example, also contains surfactant-like molecules but, depending on the type of beer, the bubbles can rise in straight chains or not. In contrast, bubbles in carbonated water are always unstable since no contaminants are helping the bubbles move smoothly through the wake flows left behind by the other bubbles in the chain.

“This wake, this velocity disturbance, causes the bubbles to be knocked out,” Zenit said. “Instead of having one line, the bubbles end up going up in more of a cone.”

The results of the new study go well beyond understanding the science that goes into celebratory toasts, the researchers said. The findings provide a general framework in fluid mechanics for understanding the formation of clusters in bubbly flows, which have economic and societal value.

Technologies that use bubble-induced mixing, like aeration tanks at water treatment facilities, for instance, would benefit greatly from researchers having a clearer understanding of how bubbles cluster, their origins, and how to predict their appearance. In nature, understanding these flows may help better explain ocean seeps in which methane and carbon dioxide emerge from the bottom of the ocean.

The experiments the research team ran were relatively straightforward — and some could even be run in any local pub. To observe the bubble chains, the researchers poured glasses of carbonated beverages including Pellegrino sparkling water, Tecate beer, Charles de Cazanove Champagne, and a Spanish-style brut.

To study the bubble chains and what goes into making them stable, they filled a small rectangular plexiglass container with liquid and inserted a needle at the bottom so they could pump in gas to create different kinds of bubble chains.

In this figure from the paper, the researchers show that when the frequency of gas bubbles is increased in a clean liquid to the rate of bubble chains in Champagne, the chain quickly loses stabilization. Courtesy of Roberto Zenit.

The researchers then gradually added surfactants or increased bubble size. They found that when they made the bubbles larger, they could make unstable bubble chains become stable, even without surfactants. When they kept a fixed bubble size and only added surfactants, they found they could also go from unstable chains to stable ones.

The two experiments indicate that there are two distinct possibilities to stabilize a bubble chain: adding surfactants and making bubbles bigger, the researchers explain in the paper.

The researchers performed numerical simulations on a computer to explain some of the questions they couldn’t explain through the physical experiments, such as calculating how much of the surfactants go into the gas bubbles, the weight of the bubbles, and their precise velocity.

They plan to keep looking into the mechanics of stable bubble chains to apply them to different aspects of fluid mechanics, especially in bubbly flows.

“We’re interested in how these bubbles move and their relationship to industrial applications and in nature,” Zenit said.

Sophisticated systems for the detection of biomarkers — molecules such as DNA or proteins that indicate the presence of a disease — are crucial for real-time diagnostic and disease-monitoring devices.

Holger Schmidt, a distinguished professor of electrical and computer engineering at UC Santa Cruz, and his group have long been focused on developing unique, highly sensitive devices called optofluidic chips to detect biomarkers.  

Schmidt’s graduate student Vahid Ganjalizadeh led an effort to use machine learning to enhance their systems by improving its ability to classify biomarkers accurately. The deep neural network he developed classifies particle signals with 99.8 percent accuracy in real-time, on a system that is relatively cheap and portable for point-of-care applications.

When taking biomarker detectors into the field or a point-of-care setting such as a health clinic, the signals received by the sensors may not be as high quality as those in a lab or a controlled environment. This may be due to various factors, such as the need to use cheaper chips to bring down costs, or environmental characteristics such as temperature and humidity. 

To address the challenges of a weak signal, Schmidt and his team developed a deep neural network that can identify the source of that weak signal with high confidence. The researchers trained the neural network with known training signals, teaching it to recognize potential variations it could see so that it can recognize patterns and identify new signals with very high accuracy. 

First, a parallel cluster wavelet analysis (PCWA) approach designed in Schmidt’s lab detects that a signal is present. Then, the neural network processes the potentially weak or noisy signal, identifying its source. This system works in real-time, so users are able to receive results in a fraction of a second. 

“It’s all about making the most of possibly low-quality signals, and doing that really fast and efficiently,” Schmidt said. 

A smaller version of the neural network model can run on portable devices. In the paper, the researchers run the system over a Google Coral Dev board, a relatively cheap edge device for the accelerated execution of artificial intelligence algorithms. This means the system also requires less power to execute the processing compared to other techniques. 

“Unlike some research that requires running on supercomputers to do high-accuracy detection, we proved that even a compact, portable, relatively cheap device can do the job for us,” Ganjalizadeh said. “It makes it available, feasible, and portable for point-of-care applications.”

The entire system is designed to be used completely locally, meaning the data processing can happen without internet access, unlike other systems that rely on cloud supercomputing. This also provides a data security advantage, because results can be produced without the need to share data with a cloud server provider. 

It is also designed to be able to give results on a mobile device, eliminating the need to bring a laptop into the field. 

“You can build a more robust system that you could take out to under-resourced or less-developed regions, and it still works,” Schmidt said.  

This improved system will work for any other biomarkers Schmidt’s lab’s systems have been used to detect in the past, such as COVID-19, Ebola, flu, and cancer biomarkers. Although they are currently focused on medical applications, the system could potentially be adapted for the detection of any type of signal. 

To push the technology further, Schmidt and his lab members plan to add dynamic signal processing capabilities to their devices. This will simplify the system and combine the processing techniques needed to detect signals at both low and high concentrations of molecules. The team is also working to bring discrete parts of the setup into the integrated design of the optofluidic chip.

The most common stars in the universe are red dwarf stars, which means that rocky exoplanets are most likely to be found orbiting such a star. Red dwarf stars are cool, so a planet has to hug it in a tight orbit to stay warm enough to potentially host liquid water (meaning it lies in the habitable zone). Such stars are also active, particularly when they are young, releasing ultraviolet and X-ray radiation that could destroy planetary atmospheres. As a result, one important open question in astronomy is whether a rocky planet could maintain, or re-establish, an atmosphere in such a harsh environment. 

To help answer that question, astronomers used NASA’s James Webb Space Telescope to study a rocky exoplanet known as GJ 486 b. It is too close to its star to be within the habitable zone, with a surface temperature of about 800 degrees Fahrenheit (430 degrees Celsius). And yet, their observations using Webb’s Near-Infrared Spectrograph (NIRSpec) show hints of water vapor. If the water vapor is associated with the planet, that would indicate that it has an atmosphere despite its scorching temperature and proximity to its star. Water vapor has been seen on gaseous exoplanets before, but to date, no atmosphere has been definitely detected around a rocky exoplanet. However, the team cautions that the water vapor could be on the star itself – specifically, in cool starspots – and not from the planet at all.

“We see a signal, and it’s almost certainly due to water. But we can't tell yet if that water is part of the planet's atmosphere, meaning the planet has an atmosphere, or if we’re just seeing a water signature coming from the star,” said Sarah Moran of the University of Arizona in Tucson, lead author of the study.

“Water vapor in an atmosphere on a hot rocky planet would represent a breakthrough for exoplanet science. But we must be careful and make sure that the star is not the culprit,” added Kevin Stevenson of the Johns Hopkins University Applied Physics Laboratory in Laurel, Maryland, principal investigator on the program.

GJ 486 b is about 30% larger than Earth and three times as massive, which means it is a rocky world with stronger gravity than Earth. It orbits a red dwarf star in just under 1.5 Earth days. It is expected to be tidally locked, with a permanent day side and a permanent night side.

GJ 486 b transits its star, crossing in front of the star from our point of view. If it has an atmosphere, then when it transits starlight would filter through those gasses, imprinting fingerprints in the light that allow astronomers to decode its composition through a technique called transmission spectroscopy.

The team observed two transits, each lasting about an hour. They then used three different methods to analyze the resulting data. The results from all three are consistent in that they show a mostly flat spectrum with an intriguing rise at the shortest infrared wavelengths. The team ran supercomputer models considering several different molecules and concluded that the most likely source of the signal was water vapor.

While the water vapor could potentially indicate the presence of an atmosphere on GJ 486 b, an equally plausible explanation is water vapor from the star. Surprisingly, even in our own Sun, water vapor can sometimes exist in sunspots because these spots are very cool compared to the surrounding surface of the star. GJ 486 b’s host star is much cooler than the Sun, so even more water vapor would concentrate within its starspots. As a result, it could create a signal that mimics a planetary atmosphere.

“We didn't observe evidence of the planet crossing any starspots during the transits. But that doesn't mean that there aren't spots elsewhere on the star. And that's exactly the physical scenario that would imprint this water signal into the data and could wind up looking like a planetary atmosphere,” explained Ryan MacDonald of the University of Michigan in Ann Arbor, one of the study’s co-authors.

A water vapor atmosphere would be expected to gradually erode due to stellar heating and irradiation. As a result, if an atmosphere is present, it would likely have to be constantly replenished by volcanoes ejecting steam from the planet’s interior. If the water is indeed in the planet’s atmosphere, additional observations are needed to narrow down how much water is present.

Future Webb observations may shed more light on this system. An upcoming Webb program will use the Mid-Infrared Instrument (MIRI) to observe the planet’s day side. If the planet has no atmosphere or only a thin atmosphere, then the hottest part of the day side is expected to be directly under the star. However, if the hottest point is shifted, that would indicate an atmosphere that can circulate heat.

Ultimately, observations at shorter infrared wavelengths by another Webb instrument, the Near-Infrared Imager and Slitless Spectrograph (NIRISS), will be needed to differentiate between the planetary atmosphere and starspot scenarios.

“It’s joining multiple instruments together that will pin down whether or not this planet has an atmosphere,” said Stevenson.

A multi-institutional study on dengue led by researchers at the Indian Institute of Science (IISc) shows how the virus causing the disease has evolved dramatically over the last few decades in the Indian subcontinent.  

Cases of dengue – a mosquito-borne viral disease – have steadily increased in the last 50 years, predominantly in South-East Asian counties. And yet, there are no approved vaccines against dengue in India, although some vaccines have been developed in other countries. 

“We were trying to understand how different the Indian variants are, and we found that they are very different from the original strains used to develop the vaccines,” says Rahul Roy, Associate Professor at the Department of Chemical Engineering (CE), IISc, and corresponding author of the study published in PLoS Pathogens. He and collaborators examined all available (408) genetic sequences of Indian dengue strains from infected patients collected between the years 1956 and 2018 by others as well as the team themselves.

There are four broad categories – serotypes – of the dengue virus (Dengue 1, 2, 3, and 4). Using computational analysis, the team examined how much each of these serotypes deviated from their ancestral sequence, from each other, and other global sequences. “We found that the sequences are changing in a very complex fashion,” says Roy.  

Until 2012, the dominant strains in India were Dengue 1 and 3. But in recent years, Dengue 2 has become more dominant across the country, while Dengue 4 – once considered the least infectious – is now making a niche for itself in South India, the researchers found. The team sought to investigate what factors decide which strain is the dominant one at any given time. One possible factor could be Antibody-Dependent Enhancement (ADE), says Suraj Jagtap, a Ph.D. student at CE and first author of the study.  

Jagtap explains that sometimes, people might be infected first with one serotype and then develop a secondary infection with a different serotype, leading to more severe symptoms. Scientists believe that if the second serotype is similar to the first, the antibodies in the host’s blood generated after the first infection bind to the new serotype and bind to immune cells called macrophages. This proximity allows the newcomer to infect macrophages, making the infection more severe. “We knew that ADE enhances severity, [but] we wanted to know if that can also change the evolution of dengue virus,” Jagtap adds.  

At any given time, several strains of each serotype exist in the viral population. The antibodies generated in the human body after a primary infection provide complete protection from all serotypes for about 2-3 years. Over time, the antibody levels begin to drop, and cross-serotype protection is lost. The researchers propose that if the body is infected around this time by a similar – not identical – viral strain, then ADE kicks in, giving a huge advantage to this new strain, causing it to become the dominant strain in the population. Such an advantage lasts for a few more years, after which the antibody levels become too low to make a difference. “This is what is new about this paper,” says Roy. “Nobody has shown such interdependence between the dengue virus and the immunity of the human population before.” This is probably why the recent Dengue 4 strains, which supplanted the Dengue 1 and 3 strains, were more similar to the latter than their own ancestral Dengue 4 strains, the researchers believe.

Such insights are possible only from studying the disease in countries like India with genomic surveillance, explains Roy, because the infection rates here have been historically high, and a huge population carries antibodies from a previous infection.