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Ultrasensitive frequency comb breathalyser targets real-time disease diagnosis

A team of US-based researchers has developed an innovative frequency comb breathalyser that is one thousand times more sensitive to disease biomarkers than the previous version – paving the way for substantial improvements in the use of non-invasive human breath analysis to detect and monitor disease.

The new breathalyser, created by scientists at JILA, University of Colorado Boulder and NIST – as well as the Center for Astrophysics, Harvard & Smithsonian – uses a mid-IR frequency comb to improve detection sensitivity by two orders of magnitude compared with a near-IR frequency comb made in the same lab in 2008. The team present the findings in the Proceedings of the National Academy of Sciences.

As corresponding author Jutta Toscano, postdoctoral researcher at the University of Basel (previously Lindemann fellow at JILA), explains, the new frequency comb, which is tuneable between 3 and 5 μm, allows the team to probe the molecular fingerprint region where fundamental, and more intense, spectroscopic transitions are found. It also enables users to probe a broadband spectrum instantaneously, without the need to scan the laser frequency – meaning that they can observe many molecules simultaneously without needing to compromise on resolution.

Besides probing the mid-IR region, where transitions are more intense, Toscano reveals that the team also enhanced the sensitivity by coupling the frequency comb into a high-finesse optical cavity.

“By matching the frequency of the comb teeth with the cavity modes – the ‘standing modes’ of the cavity – we can increase the interaction path length between molecules inside the cavity and laser light by a factor of around 4000, equivalent to an effective path length of a few kilometres,” Toscano says. “We then probe the light that leaks out of the cavity by sending it into an FTIR [Fourier-transform infrared] spectrometer to find out which exact comb teeth have been absorbed and by how much. In turn, this tells us which molecules are present in the breath sample and their concentration.”

System set-up: Graduate student Qizhong Liang adjusts JILA’s frequency comb breathalyser. (Courtesy: R Jacobson/NIST)

Campus study

The team is currently using the new device to carry out a campus-wide study at the University of Colorado Boulder, as part of which it tests students’ breath and uses machine learning to look for correlations between the molecules present in breath and potential conditions affecting the participants – such as being covid-positive, for example, or suffering from asthma, diabetes or intestinal issues.

“After identifying these correlations, we would like to be able to reliably predict the presence or absence of these conditions from looking at the breath alone,” explains Toscano. “So far, the apparatus is not exactly compact or transportable, but our collaborators at NIST and CU Engineering – Scott Diddams and Greg Rieker, respectively – are looking into miniaturizing the frequency comb to make the breathalyser suitable for settings other than a laboratory.”

For Toscano, the main advantages of laser-based breathalysers like this include the collection speed, as well as the ability to easily differentiate between different isomers (molecules with the same mass but different structure, which would appear at the same mass-to-charge position in a mass spectrum) and between molecules of similar mass (which can be challenging to resolve in mass spectra).

Using breath to detect lung cancer

“On the other hand, most laser-based breathalysers operate at a single frequency to monitor a single molecule, with the notable exception of devices where a few single-frequency lasers are combined to monitor a few different molecules simultaneously,” she says.

“Adopting frequency combs as a laser source enables us to detect tens of molecules, or potentially even more, simultaneously, circumventing the potential complexity of having to combine tens of different laser sources. This is done by making use of the thousands of comb teeth that compose a frequency comb, which can be thought of as a collection of thousands of very narrow CW [continuous wave] lasers, each with an extremely well-defined frequency,” she adds.

Major US and European labs join forces to tackle climate change

Top physics facilities in Europe and the US have come together to tackle the climate crisis. The labs – including CERN, the European Space Agency, Fermilab and the Los Alamos National Laboratory – have announced that they will step up their scientific collaboration on carbon-neutral energy and climate change as well as share best practices to improve the carbon footprint of big-science facilities.

Large labs demand a huge amount of energy. The CERN particle-physics lab near Geneva, for example, uses 1.3 terawatt hours of electricity annually, which is enough to power 300,000 UK homes for a year. In 2020 the lab released its first public Environment Report that detailed the status of CERN’s environmental footprint. It found that greenhouse-gas emissions emitted by the lab in 2018 was 223,800 tonnes of carbon-dioxide equivalent.

Planned facilities, such as the European Spallation Source that is being constructed in Lund, Sweden, meanwhile, have integrated sustainability into their design such as diverting waste heat into local heating systems instead of it being vented into the atmosphere.

Eager to learn

In a statement, released today by the US National Laboratory Directors’ Council and EIROforum, the 26 labs say that the impacts of climate change are becoming “increasingly visible” through the outbreaks of disease as well as extreme weather events such as heat waves, storms, droughts and flooding.

“Science has a key role to play, and in particular at big-science facilities, where we are constantly pushing forwards the frontiers of knowledge and technology to the highest levels of excellence and inventiveness,” the statement notes. “Research and datasets provide a foundation on which to build innovative technologies and solutions that not only mitigate the impact of climate change, but also help us protect the Earth’s ecosystems, including the human populations around the world vulnerable to a wide array of environmental threats.”

Francesco Sette, director general of the European Synchrotron Radiation Facility, who is also chair of EIROforum, told Physics World that over 25% of users’ research at the facility is linked to climate change and the environment. He also says that the lab has decreased its energy consumption by 20% while at the same time boosting the performance of the synchrotron via a recent upgrade.

“ESRF’s commitment to address and mitigate climate changes is also in identifying the solutions to reduce our carbon footprint,” says Sette. “On a long-term perspective, we are looking for further improvements in energy consumption, and environmental impact thanks to renewable energy sources and improved practices and procedures.”

Sette adds that the labs are “eager” to learn from each other and will now further develop best practices and develop new sustainable technologies.

The move comes just days before the 2021 United Nations Framework Convention on Climate Change Conference of Parties (COP26) in Glasgow, UK, which will be attended by world leaders.

How molecular catalysts mediate the electrochemical generation of fuels

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The conversion of energy-poor feedstocks like water and carbon dioxide into energy-rich fuels involves multi- electron, multi-proton transformations. In order to develop catalysts that can mediate fuel production with optimum energy efficiency, this complex proton-electron reactivity must be carefully considered.

Using a combination of electrochemical methods and time-resolved spectroscopy reveals new details of how molecular catalysts mediate the reduction of protons to dihydrogen and the experimental parameters that dictate catalyst kinetics and mechanism. These studies create opportunities to promote, control and modulate the proton-coupled electron transfer reaction pathways of catalysts.

In this webinar, presented by Jillian Dempsey, you will:

Learn how molecular catalysts are being used to mediate fuel generation.
Learn how to elucidate mechanisms of coupled chemical reactions from cyclic voltammetry experiments.
Find out more about proton-coupled electron transfer.

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Jillian L Dempsey is associate professor at the University of North Carolina at Chapel Hill (UNC). She received her BS from the Massachusetts Institute of Technology in 2005 where she worked in the laboratory of Prof. Daniel G Nocera. As an NSF graduate research fellow, she carried out research with Prof. Harry B Gray and Dr Jay R Winkler at the California Institute of Technology, completing her PhD in 2011. From 2011–2012, Prof. Dempsey was an NSF ACC postdoctoral fellow with Daniel R Gamelin at the University of Washington. She joined UNC in 2012. Her research has garnered numerous awards including the 2019 Harry B Gray Award for Creative Work in Inorganic Chemistry by a Young Investigator; the 2017 J Carlyle Sitterson Award for Teaching First-Year Students; 2016 Sloan Research Fellowship; and 2015 Packard Fellowship for Science and Engineering. The Dempsey Research Group explores charge transfer processes associated with solar fuel production, including proton-coupled electron transfer reactions and electron transfer across interfaces. Prof. Dempsey’s research bridges molecular and materials chemistry, and relies heavily on methods of physical inorganic chemistry, including transient absorption spectroscopy and electrochemistry. She is currently deputy director of the Center for Hybrid Approaches in Solar Energy to Liquid Fuels.

I had been burned out before. This time was different

**Role model, reframed** Particle-physics PhD student Katrina Miller is working to normalize a healthier and more sustainable academic environment. (Courtesy: Katrina Miller)

The summer of 2020 wasn’t my first experience with burnout, but it was definitely the most memorable.

Four months into a global pandemic and weeks into a nationwide racial reckoning, the protesters chanting outside my Chicago apartment window were what pushed me over the edge. “No justice!” they shouted. “No peace!” Make no mistake: it wasn’t that I wanted them to stop. It was that I wanted to be out there with them.

Instead, I was trying to adjust to working from home while drowning in research deadlines, presentation preparations, mentoring duties, outreach initiatives, and the neverending stream of Zoom calls, Slack messages and e-mails. The importance of those responsibilities paled in comparison to living with the fear of how the COVID-19 virus was ravaging the world and to grappling with the grief that police brutality had caused my community.

But the work didn’t stop, so all I could think to do was try to push through my indifference. Let me just get this out of the way, and I’ll join in next time, I’d tell myself as I moved away from my window. Every task I checked off my to-do list, though, was quickly replaced with another. My ability to keep up – and to care about keeping up – was slipping. Eventually my adviser reached out in concern. I was too drained to even talk about it by then; I just asked him for a week off. “Absolutely,” he wrote back without hesitation.

Under-represented and under pressure

Burnout results from chronic, unmitigated stress in the workplace, and although it feels different for everybody, it is a common experience for postgraduates. As a student, there just never seems to be enough time to manage the overload of classes, research and teaching, and to balance it with health, family, relationships, finances and a semblance of a social life.

The stress of competing demands is exacerbated for people who are under-represented in their field. As a Black woman studying one of the least diverse sciences, I feel both internal and external pressure to contribute to efforts toward more equitable academic environments, to make it a little easier for the students who come after me. This responsibility often means saying yes to an overwhelming number of diversity and inclusion initiatives, as well as hyper-managing my time to ensure that it doesn’t affect my research output (which means it cuts into my personal time instead).

As a Black woman studying one of the least diverse sciences, I feel both internal and external pressure to contribute to efforts toward more equitable academic environments

I have burned out enough times in graduate school to know that for me, the most prominent symptom is a loss of interest in, or outright cynicism toward, activities I normally enjoy. But last summer felt different. Though I had experienced detachment from my academic tasks in the past – one particularly bad bout of burnout had me contemplating leaving school altogether – I was always surrounded by other physicists who could affirm the importance of the work. Far away from the hustle and bustle of department culture, however, I was left alone to reflect. Why did any of this matter? And what exactly was I sacrificing for it? Overworked and underpaid, I had little time to contribute to the community organizing efforts happening on the street below. I also had little time to spend with family and friends or even to keep up with basic chores: keeping my house clean and my fridge stocked, for example, or doing the laundry.

Pre-pandemic, I’d usually treat burnout with a trip, but travelling wasn’t an option in 2020 (and, in retrospect, this was just a Band-Aid, anyway). Stuck in my 400-square-foot studio apartment, there was nothing to do but address the real problem. After my week off, I cut back on my work hours and found a therapist, who reminded me of the importance of setting boundaries – not only with others, but also with myself. I created a mental checklist to help me weigh the pros and cons of saying yes to new opportunities. I stopped working in my pyjamas on the couch; instead, I bought a desk, carved out a dedicated workstation and invested in a planner to more formally delineate my school and free time. The most intentional shift I made, however, was pouring myself into passions other than physics. I picked up exercising and writing again, and found new hobbies, like painting and cooking.

Reframing the situation

It took months to overcome the burnout from that summer, and although I’m still searching for that excitement I felt when I started my PhD, I have been able to rediscover some level of enthusiasm for my research. Most importantly, I have done a better job than I did during past stretches of burnout in evaluating whether my lifestyle is sustainable for who I want to be outside of my identity as a physicist. I have detached my worth from my academic productivity because I have cultivated a more well-rounded sense of self. That makes it much easier for me to honour my own needs.

Today, my academic commitment is a lot lower than it was in the summer of 2020. For the most part, I don’t do research on evenings or weekends. I minimize time spent on Zoom and do my best not to respond to messages or e-mails outside standard working hours. Any volunteering I commit to must first pass my mental checklist, an evaluation of whether I have adequate time to devote and whether the opportunity aligns with my values. My research output has gone down, but that, along with the consequences that may arise from it, is a sacrifice I am willing to make. If it’s going to cost my health or my happiness, it isn’t worth it anyway.

My university opened again in September, so I’m now back in the office two to three times a week – the place where I used to pull late nights, eat every meal at my desk (or skip them altogether), and ignore other parts of my life just to keep my head above water. Everything was in the exact same place as before the pandemic. I, however, am not.

I still feel immense pressure to be the role model I didn’t see growing up and to use my voice to make a difference for future Black students. But I reframe that now: saying no, setting boundaries on my time, and asking others to respect my limits may be just as valuable as anything else I could ever do. Rather than encouraging marginalized students to assimilate into current academic culture by making the same sacrifices that I have, I can contribute to normalizing an environment that is healthier and more sustainable for its scholars.

For more on #BlackInPhysics week, including additional essays by outstanding Black physicists, please visit the dedicated pages at Physics World and Physics Today.

Clinical experience on the independent dose distribution verification with RadCalc

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Independent 3D dose check systems play an important role in the patient-specific quality assurance. In this webinar, Dr Ruoxi Wang will share experiences on performing 3D dose distribution verification based on the Monte Carlo Module in RadCalc.

He will highlight the differences between the independent verification and measurement-based QA, explain the detailed resource deployment and commissioning process, and showcase the usage in different clinical settings.

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Dr Wang Ruoxi received his doctorate from Université Claude Bernard Lyon 1, France, in 2015. He was engaged in the research and development of new dosimeters in Lyon Institute of Nanotechnology. After graduation he joined Beijing Cancer Hospital in 2017. His main research directions are: Application of Monte Carlo simulation method in the field of medical physics (dose deposition calculation, dosimeter simulation), in-body dose reconstruction, new methods of radiotherapy quality control and assurance, and automatic radiotherapy planning.

Variational principles and topological constants of motion for MHD

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In this webinar, presented by Asher Yahalom, we describe how MHD can be formulated through a variational principle as a field theory. In particular, we will address the appropriate choice of variational variables that will minimize the computational load and allow us to obtain new analytical solution and better numerical schemes.

We will also show how the new action principles will allow us to derive new constants of motion using Noether’s theorem, some of them with a topological interpretation.

Attend this webinar to:

Understand how MHD can be formulated as a variational problem.
Understand how MHD can be simplified mathematically.
Learn how to use the formalism to obtain new conservation laws.

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Prof. Asher Yahalom is a full professor and the former vice dean of the Faculty of Engineering at Ariel University and the academic director of the free electron laser user center, which is located within the University Center campus. Born in Israel, hereceived a BSc, MSc and PhD in mathematics and physics from the Hebrew University in Jerusalem, Israel in 1990, 1991 and 1996 respectively. Asher was a postdoctoral fellow (1998) in the Department of Electrical Engineering of Tel-Aviv University, Israel, and a visiting fellow at the University of Cambridge, UK, during 2005–2006, 2008 and 2012.

Quantum advantage takes a giant leap in optical and superconducting systems

Two different quantum computers, one using light and the other superconducting circuits, have done calculations well beyond the capability of conventional computers – according to physicists in China. The breakthroughs provide further encouragement that quantum computers could soon be solving practical problems that are impossible to implement on conventional, or “classical”, computers.

The quantum computers were built by two groups at the Hefei National Laboratory for Physical Sciences of the University of Science and Technology of China. Both groups are led by Jian-Wei Pan.

Both systems were used to calculate the output probabilities of quantum circuits. These are systems that accept multiple quantum states as inputs; have those states travel through a quantum circuit; and then deliver multiple states as output. An example is system whereby single photons are input in parallel to an optical circuit where they can interfere with each other via components such as beam splitters and then emerge from multiple output ports.

Exceedingly difficult

The computational goal is to work out the probability that a certain input configuration would lead to a certain output configuration. It turns out that this task is exceedingly difficult for a classical computer if the quantum circuits have more than a few tens of inputs and outputs.

A quantum computer, however, can use quantum sampling techniques to calculate random instances of the probability distribution in much less time that a classical computer. As a result, sampling experiments are a way to demonstrate “quantum advantage”, the idea that quantum computers are much better than their classical counterparts at solving certain problems.

Huge speedup

In a paper in Physical Review Letters, Pan and colleagues explain how they used a technique called Gaussian boson sampling to analyse the output of a 144-mode optical interferometer. The team says that their system has 10⁴³possible outcomes and that their implementation can sample the output 10²⁴times faster than a classical supercomputer. This quantum speedup is a huge increase over the team’s previous result of 10¹⁴times, which they reported in December 2020. The result makes it extremely unlikely that a specialized classical algorithm can be devised to match this performance, thereby establishing quantum advantage.

In a second paper in Physical Review Letters, another team led by Pan used a quantum computer that comprised 66 transmon superconducting qubits that are connected via 100 tuneable couplers. Their sampling experiment involved using 56 of the qubits, and the system was put through 20 quantum logic cycles.

Although their computer is just three qubits larger than the 53 qubit system used in 2019 by researchers at Google to demonstrate quantum advantage, Pan and colleagues estimate that their sampling calculation is as much as 1000 times more difficult to do on a classical computer. As a result, they claim that a calculation that takes about an hour on their system would take eight years to complete on a classical computer.

Qubits for the future: YouTube documentary explores how quantum computing could promote sustainability

How could quantum computing help us to fix climate change? This is the question at the heart of Quantum Technology | Our Sustainable Future, a half-hour-long documentary published on YouTube in July.

Made by “The Quantum Daily”, a resource for news and information on all things quantum, the documentary consists of interviews with people working in a host of organizations in the sector, from Oxford Instruments NanoScience to Google Quantum AI. The main idea is that, since quantum computers have the potential to be much more powerful than classical ones, they could speed up the discovery of solutions, such as molecules that would be very effective at carbon capture.

One concept I find especially intriguing is “using nature to simulate nature”. Since quantum effects are involved in certain naturally occurring processes, quantum computers should be able to simulate them better than classical computers. This could help us understand nitrogen fixation that happens in soil, for example, which we might then simulate to manufacture fertilisers at room temperature. (Our current methods require very high temperatures and pressures, and account for around 2% of our global energy usage.)

The final section of the documentary acknowledges that there is a long way to go to achieve useful quantum computers, and addresses the fact that they currently require vast amounts of energy, since they need to be cooled down to temperatures colder than space. With the latest IPCC report stressing that immediate action is paramount, I’m not sure this technology will arrive in time to help us. However, the quantum scientists’ excitement is infectious, and I look forward to seeing how it plays out.

Monochromatic X-ray method promises dramatic cut in radiation dose

A technology called monochromatic X-ray imaging could reduce radiation dose per mammogram by a factor of five to 10 times, according to a research review published in the European Journal of Radiology.

Michael Fishman from Boston University and Madan Rehani from Massachusetts General Hospital proposed that contrast-enhanced digital mammography with monochromatic X-rays provides a simpler and more effective imaging technique at substantially lower radiation dose.

“Lowering radiation dose by a factor of five to 10 while maintaining image quality implies a major reduction in total exposure from breast cancer screening and dramatically less risk of radiation-induced cancers in at-risk women,” Fishman and Rehani write.

X-ray systems of today are largely based on the hot cathode X-ray tube first developed by William Coolidge in 1913, and this technology remains the universally accepted method for breast cancer detection due to its wide availability, low cost and repeatability, the authors write.

However, the radiation delivered by the technique has been a focus of debates regarding possible cancer risk associated with breast screening. Monochromatic X-ray imaging is a recent development that could reduce cancer risk in mammography by dramatically reducing radiation dose.

While conventional radiography systems use multiwavelength X-ray emission extending over a broad energy band, monochromatic X-rays use two X-ray emission processes to generate monochromatic X-ray beams. The first beam is similar to conventional X-ray, as high-energy electrons bombard a metal target to emit broadband X-ray energies.

The second emission process involves the concentration of X-rays onto a small, thin-foil metallic target. This target emits monochromatic X-rays via fluorescence, and its elemental composition can be identified by its energy.

“It is especially important for examining dense breast tissue where image quality frequently is suboptimal and limited in sensitivity,” the authors write.

Fishman and Rehani wanted to examine details from previous studies about monochromatic X-ray technology and its application to breast imaging. They analysed study results using a prototype system developed by Eric Silver from Imagine Scientific for breast imaging that generates such X-rays through fluorescence emission.

Monochromatic versus broadband X-ray imaging — Dose reduction: Sparkling images of microcalcifications embedded at the top of a 4.1-cm thick breast phantom acquired using monochromatic X-rays at a radiation dose of 0.55 mGy (top), compared with images obtained with a broadband mammography system at 3.62 mGy (bottom). (Courtesy: Eric Silver)

“[We] anticipate its [system’s] first use in the clinic within several months,” Silver tells AuntMinnie.com.

Fishman and Rehani also assessed signal-to-noise ratio as a measure of image quality at different doses in breast phantoms of different sizes, and reviewed the comparison of parameters with a standard mammography system.

Along with finding a five- to 10-times reduction in radiation dose, the researchers found promise for a phantom simulating thick breasts (9 cm). For such simulations, the signal-to-noise ratio for monochromatic X-rays was 2.6 times higher and the radiation dose was 4.2 times lower than conventional X-rays.

“For the conventional broadband system to equal the signal-to-noise ratio of the monochromatic system, it would require a dose of 19 mGy, 29 times higher than the dose delivered by the monochromatic system,” they note.

Rehani tells AuntMinnieEurope.com that the technology is ready for human imaging and has the potential to replace millions of X-ray tubes in the world currently in breast imaging, CT, fluoroscopy and radiography machines. He also says that more funding and clearances are needed before it can be widely accepted.

“There is a plan to upgrade technology to reduce exposure time which is again more of a financial issue rather than development of technology,” he says.

Silver tells AuntMinnieEurope.com that the prototype system is being adapted for use at higher energies to address CT applications with and without contrast.

This article was originally published on AuntMinnieEurope.com ©2021 by AuntMinnieEurope.com. Any copying, republication or redistribution of AuntMinnieEurope.com content is expressly prohibited without the prior written consent of AuntMinnieEurope.com.

All-optical processors could compute any linear transformation, machine learning reveals

Researchers in the US have shown how all-optical processors could be used to carry out a range of linear mathematical transformations, including Fourier transforms. Using machine learning techniques, Onur Kulce, Aydogan Ozcan and colleagues at the University of California, Los Angeles, generated the blueprint for set of diffractive surfaces that can be used to produce specific optical outputs from any arbitrary input. When implemented in the lab, the approach could provide an alternative for calculating linear transformations using conventional computers.

To process information, computers often use linear transformations to perform mathematical operations on data. A classic example is the Fourier transform, which converts a time sequence of data – such as sound captured by a microphone – into a representation of the frequencies present in the data.

The speed at which such transformations can be done is limited by the processing power of electronic computers, but recently researchers have been exploring the possibility of using purely optical devices to do the task. Since optical waves travel effortlessly at the speed of light, this approach could one day be used to process information at far higher speeds and using far less energy than conventional computers.

Metamaterials and metasurfaces

Recent advances in photonics have led to the design of new metamaterials and metasurfaces, which are engineered to diffract light in very specific ways. As wavefronts of light interact with these materials, the light is transformed in ways that depend on the geometry of the surface. By carefully adjusting the properties of diffractive surfaces, researchers can control the nature of the linear transformations they cause in light waves.

In their study, Kulce, Ozcan and colleagues showed how a series of diffractive surfaces could be used to achieve any arbitrary transformation between input and output waves. To do this, they used machine learning methods to design the surfaces required for specific transformations.

Filtering operations

Through this technique, they successfully designed a wide array of arbitrary linear transformations including Fourier transforms, image permutations, and filtering operations. In addition, they showed that the efficiency of their transformations could be significantly improved, simply by increasing the number of diffractive surfaces.

The team’s results could pave the way for a new generation of all-optical processors that could offer several advantages over conventional computers. Aside from the energy used to generate the optical waves themselves, these devices could operate completely passively; requiring no power to run.

Through future research, the techniques developed by Kulce and colleagues could soon be used to create diffractive surfaces in the lab: potentially bringing an all-optical transformation processor a step closer to reality.

The research is described in Light: Science & Applications.

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