Rice University scientists are using machine-learning techniques to streamline the process of synthesizing graphene from waste through flash Joule heating.  

The process discovered two years ago by the Rice lab of chemist James Tour has expanded beyond making graphene from various carbon sources to extracting other materials like metals from urban waste, with the promise of more environmentally friendly recycling to come. 

The technique is the same for all of the above: blasting a jolt of high energy through the source material to eliminate all but the desired product. But the details for flashing each feedstock are different. 

The researchers describe in Advanced Materials how machine-learning models that adapt to variables and show them how to optimize procedures are helping them push forward.

“Machine-learning algorithms will be critical to making the flash process rapid and scalable without negatively affecting the graphene product’s properties,” Tour said.  

“In the coming years, the flash parameters can vary depending on the feedstock, whether it’s petroleum-based, coal, plastic, household waste, or anything else,” he said. “Depending on the type of graphene we want -- small flake, large flake, high turbostratic, level of purity -- the machine can discern by itself what parameters to change.”

Because flashing makes graphene in hundreds of milliseconds, it’s difficult to tease out the details of the chemical process. So Tour and company took a clue from materials scientists who have worked machine learning into their everyday process of discovery.

“It turned out that machine learning and flash Joule heating had really good synergy,” said Rice graduate student and lead author Jacob Beckham. “Flash Joule heating is a really powerful technique, but it’s difficult to control some of the variables involved, like the rate of current discharge during a reaction. And that’s where machine learning can shine. It’s a great tool for finding relationships between multiple variables, even when it’s impossible to do a complete search of the parameter space. 

“That synergy made it possible to synthesize graphene from scrap material based entirely on the models’ understanding of the Joule heating process,” he said. “All we had to do was carry out the reaction -- which can eventually be automated.”

The lab used its custom optimization model to improve graphene crystallization from four starting materials -- carbon black, plastic pyrolysis ash, pyrolyzed rubber tires, and coke -- over 173 trials, using Raman spectroscopy to characterize the starting materials and graphene products. 

The researchers then fed more than 20,000 spectroscopy results to the model and asked it to predict which starting materials would provide the best yield of graphene. The model also took the effects of charge density, sample mass, and material type into account in their calculations. 

Co-authors are Rice graduate students Kevin Wyss, Emily McHugh, Paul Advincula, and Weiyin Chen; Rice alumnus John Li; and postdoctoral researchers Yunchao Xie and Jian Lin, an associate professor of mechanical and aerospace engineering, of the University of Missouri. Tour is the T.T. and W.F. Chao Chair in Chemistry as well as a professor of computer science and materials science and nanoengineering.

An exceptionally large grant will allow a team of Empa researchers to work on an ambitious project over the next ten years: The Werner Siemens Foundation (WSS) is supporting Empa's CarboQuant project with 15 million Swiss francs. The project aims to lay the foundations for novel quantum technologies that may even operate at room temperature – in contrast to current technologies, most of which require cooling to near absolute zero. "With this project, we are taking a big step into the unknown," says Oliver Gröning who coordinates the project. "Thanks to the partnership with the Werner Siemens Foundation, we can now move much further away from the safe shore of existing knowledge than would be possible in our 'normal' day-to-day research. We feel a little like Christopher Columbus and are now looking beyond the horizon for something completely new." 

The expedition into the unknown now being undertaken by Empa researchers Pascal Ruffieux, Oliver Gröning, and Gabriela Borin-Barin under the lead of Roman Fasel was preceded by twelve years of intensive research activity. The researchers from Empa's nanotech@surfaces laboratory, headed by Fasel, regularly published their work in renowned journals such as Science.

In 2010, the team succeeded in synthesizing graphene strips, so-called nanoribbons, from smaller precursor molecules for the first time. With their novel synthesis approach, the Empa team can now produce carbon nanomaterials with atomic precision, thereby precisely defining their quantum properties. Graphene is considered a possible building material for computers of the future; it is made of carbon and resembles the familiar graphite. The material is, however, just one atomic layer thin and promises faster, more powerful computer architectures than the semiconductor materials known today. Back in 2017, the research team, in collaboration with colleagues from the University of California, Berkeley, built the first transistor from graphene nanoribbons.

A first milestone: magnetic carbon

But then the researchers realized an effect that had previously only been predicted theoretically and seemed even more interesting: Their tiny, tailor-made carbon nanomaterials exhibited properties of magnetism. In 2020, they first reported on the effect they had discovered – and followed up with a more refined paper in October 2021: Now, using their carbon nanomaterials, they had demonstrated for the first time a physical effect that the future Nobel Prize winner in physics F.D.M. Haldane had predicted nearly 40 years ago: spin fractionalization. This fractionalization only forms when many spins (i.e., fundamental quantum magnets) can be brought into a common, coherent quantum superposition. Empa researchers have achieved just that in their precisely synthesized molecular chains.

CarboQuant is intended to build on these special spin effects in graphene nanoribbons. Gröning says, "So far, we see spin states at very specific locations in the nanoribbons, which we can generate and detect. The next step will be to manipulate these spin states deliberately, for example, to reverse the spin at one end of the nanoribbon and thus elicit a corresponding reaction at the other end." This would give Empa researchers something very unique to work with: a quantum effect that is stable and can be manipulated even at room temperature or requiring just moderate cooling. That could be a silver bullet for building entirely new kinds of quantum supercomputers. 

0 and 1 at the same time

But why is it that quantum supercomputers can calculate faster than conventional computers? Classical computing machines calculate in bits. Each component can have one of two possible states: 0 or 1. In the quantum world, however, these states can be superimposed: 0 or 1 or both states at the same time are possible. That's why circuits in a quantum computer, known as qubits, can perform not just one computational operation after another, but multiple ones simultaneously. Gröning is already looking forward to the experiment: "If we manage to control the spin states in our nanoribbons, we can use them for quantum electronic devices."

While one part of the team continues to study spin effects in a high vacuum, other team members will focus on the everyday suitability of the graphene nanoribbons. "We have to get the components out of the protected environment of the high vacuum and prepare them in such a way that even in ambient air and at room temperature, they do not disintegrate. Only then can we equip the nanoribbons with contacts – which is the prerequisite for practical applications without the need of an elaborate infrastructure," Gröning says.

High-frequency radiation and intense laser pulses

The journey into this unknown, new world will in any case be very demanding. Already in the initial phase – the entry ticket, if you wish –, the control and time-resolved measurement of spin states require a completely new set of equipment that the researchers will have to develop and build. "We need to combine the scanning tunneling microscope (STM), in which we synthesize the nanoribbons and look at their structure, with ultra-fast measurements of their electronic and magnetic properties," Gröning explains. That can be done by high-frequency electrical signals at high magnetic fields and by irradiation with very short, extremely intense laser pulses.

To achieve this, two new measurement systems are being set up at Empa, which will also play key roles in the team's other research projects and which are co-funded by the Swiss National Science Foundation (SNSF) and the European Research Council (ERC). "This shows that synergies always emerge from different projects," says Gröning, "but also that ambitious goals can only be achieved with the support of different players at multiple levels." The researchers estimate that it will take two to three years just to set up these new analytical instruments and to carry out the first test runs. 

A very distinct project

CarboQuant is a very special project thanks to its long-term and generous funding, says Oliver Gröning. The researchers at Empa's nanotech@surfaces lab now have extraordinarily great and long-term creative freedom on the way to their ambitious goal: a possible building material for next-generation quantum supercomputers. "We don't yet see the island that might be out there. But we can guess it, and if there is something out there, we are confident that we will find it, thanks to the support of the Werner Siemens Foundation and our national and international research partners," says Gröning.

Brendan Dolan-Gavitt is laying the groundwork for more efficient, less costly vulnerability testing. 

The National Science Foundation (NSF) has selected an NYU Tandon School of Engineering researcher who is developing better ways to assess vulnerability discovery tools – thus allowing cybersecurity professionals to better understand what techniques are most effective and ultimately leading to safer software – to receive its most prestigious award for promising young academics. 

Brendan Dolan-Gavitt, an assistant professor in the Department of Computer Science and Engineering and a faculty member of NYU’s Center for Cybersecurity, received a 2022 NSF Faculty Early Career Development Award, more widely known as a CAREER Award, which supports early-career faculty who have the potential to serve as academic role models in research and education.

A five-year, $500,000 grant will support a project that aims to create techniques for automatically generating benchmark corpora of software vulnerabilities that can be used to rigorously assess newly developed and existing tools used to root out dangerous programming bugs.

Software vulnerabilities pose a major threat to the safety and security of computer systems, and while there is a large body of research on how to find vulnerabilities in programs, the large, empirically tested corpora of vulnerabilities required to rigorously test that research are difficult and expensive to assemble. 

Although researchers have discovered ways to automatically generate vulnerabilities and inject them into software, the vulnerabilities created in that way are unrealistic (containing artifacts that made them easier to discover than real vulnerabilities inadvertently created by human programmers) and not varied enough.

Dolan-Gavitt intends to address those shortcomings by employing large language models trained on code to synthesize vulnerabilities that are both realistic and diverse, placing vulnerabilities in hard-to-discover paths, allowing new vulnerability classes to be added quickly with a customized domain-specific language, and automatically generating exploits for each vulnerability. The end result will be a limitless supply of highly realistic vulnerability corpora that can be generated cheaply, at scale, and on-demand, giving researchers valuable benchmarks in measuring the efficacy of their cybersecurity tools.  

In addition to his work’s benefit to cybersecurity researchers and industry professionals, it is also expected to be a boon to educators. Since joining NYU Tandon in 2015, Dolan-Gavitt has been involved in CSAW, the most comprehensive student-run cybersecurity event in the world, and among the most popular offerings at the annual event is a “capture the flag” competition that challenges students to find vulnerabilities in a software program. “These types of competitions are an extremely popular and effective means of teaching a variety of cybersecurity skills, but they require large amounts of time, money, and expertise to create and manage,” he explains. “If the creation of the challenges can be partially or wholly automated, it could bring new educational opportunities within reach of a broader and more diverse population of students by dramatically lowering costs and reducing the time and effort needed.” 

“Brendan Dolan-Gavitt is helping place the field of vulnerability finding on solid scientific footing, allowing for repeatable and reproducible experiments and facilitating comparative evaluations of the cyber tools meant to protect us,” said NYU Tandon Dean Jelena Kovačević. “His work has the potential to make a major impact on cybersecurity education, broadening access and helping to build the next generation of security researchers. We’re proud that his techniques will be employed right here in our own cybersecurity courses and at CSAW and pleased that the NSF has chosen him to receive this much-deserved CAREER Award.”

Dolan-Gavitt joins the over 50% of NYU Tandon’s engineering junior faculty members who hold CAREER Awards or similar young-investigator honors, including 10 since 2019 alone.

His award reflects the NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Researchers from the KTH Royal Institute of Technology in Stockholm, Sweden, and Stanford University have fabricated material for computer components that enable the commercial viability of computers that mimic the human brain. 

Electrochemical random access (ECRAM) memory components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology and contributing toward the commercialization of powerful computers that are modeled after the brain’s neural network. Such neuromorphic computers can be thousands of times more energy-efficient than today’s computers.

These advances in supercomputing are possible because of some fundamental differences from the classic computing architecture in use today, and the ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, says KTH Associate Professor Max Hamedi.

“Instead of transistors that are either on or off, and the need for information to be carried back and forth between the processor and memory—these new computers rely on components that can have multiple states, and perform in-memory computation,” Hamedi says.

The scientists at KTH and Stanford have focused on testing better materials for building an ECRAM, a component in which switching occurs by inserting ions into an oxidation channel, in a sense similar to our brain which also works with ions. What has been needed to make these chips commercially viable are materials that overcome the slow kinetics of metal oxides and the poor temperature stability of plastics.                   

The key material in the ECRAM units that the researchers fabricated is referred to as MXene—a two-dimensional (2D) compound, barely a few atoms thick, consisting of titanium carbide (Ti3C2Tx). The MXene combines the high speed of organic chemistry with the integration compatibility of inorganic materials in a single device operating at the nexus of electrochemistry and electronics, Hamedi says.

Co-author Professor Alberto Salleo at Stanford University, says that MXene ECRAMs combines the speed, linearity, write noise, switching energy, and endurance metrics essential for parallel acceleration of artificial neural networks.

“MXenes are an exciting materials family for this particular application as they combine the temperature stability needed for integration with conventional electronics with the availability of a vast composition space to optimize performance, Salleo says”

While there are many other barriers to overcome before consumers can buy their neuromorphic computers, Hamedi says the 2D ECRAMs represent a breakthrough at least in the area of neuromorphic materials, potentially leading to artificial intelligence that can adapt to confusing input and nuance, the way the brain does with thousands time smaller energy consumption. This can also enable portable devices capable of much heavier computing tasks without having to rely on the cloud.