Tag Archives: topology

Canadian and Italian researchers go beyond graphene with 2D polymers

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According to a May 20,2020 McGill University news release (also on EurkekAltert), a team of Canadian and Italian researchers has broken new ground in materials science (Note: There’s a press release I found a bit more accessible and therefore informative coming up after this one),

A study by a team of researchers from Canada and Italy recently published in Nature Materials could usher in a revolutionary development in materials science, leading to big changes in the way companies create modern electronics.

The goal was to develop two-dimensional materials, which are a single atomic layer thick, with added functionality to extend the revolutionary developments in materials science that started with the discovery of graphene in 2004.

In total, 19 authors worked on this paper from INRS [Institut National de la Recherche Scientifique], McGill {University], Lakehead [University], and Consiglio Nazionale delle Ricerche, the national research council in Italy.

This work opens exciting new directions, both theoretical and experimental. The integration of this system into a device (e.g. transistors) may lead to outstanding performances. In addition, these results will foster more studies on a wide range of two-dimensional conjugated polymers with different lattice symmetries, thereby gaining further insights into the structure vs. properties of these systems.

The Italian/Canadian team demonstrated the synthesis of large-scale two-dimensional conjugated polymers, also thoroughly characterizing their electronic properties. They achieved success by combining the complementary expertise of organic chemists and surface scientists.

“This work represents an exciting development in the realization of functional two-dimensional materials beyond graphene,” said Mark Gallagher, a Physics professor at Lakehead University.

“I found it particularly rewarding to participate in this collaboration, which allowed us to combine our expertise in organic chemistry, condensed matter physics, and materials science to achieve our goals.”

Dmytro Perepichka, a professor and chair of Chemistry at McGill University, said they have been working on this research for a long time.

“Structurally reconfigurable two-dimensional conjugated polymers can give a new breadth to applications of two-dimensional materials in electronics,” Perepichka said.

“We started dreaming of them more than 15 years ago. It’s only through this four-way collaboration, across the country and between the continents, that this dream has become the reality.”

Federico Rosei, a professor at the Énergie Matériaux Télécommunications Research Centre of the Institut National de la Recherche Scientifique (INRS) in Varennes who holds the Canada Research Chair in Nanostructured Materials since 2016, said they are excited about the results of this collaboration.

“These results provide new insights into mechanisms of surface reactions at a fundamental level and simultaneously yield a novel material with outstanding properties, whose existence had only been predicted theoretically until now,” he said.

About this study

“Synthesis of mesoscale ordered two-dimensional π-conjugated polymers with semiconducting properties” by G. Galeotti et al. was published in Nature Materials.

This research was partially supported by a project Grande Rilevanza Italy-Quebec of the Italian Ministero degli Affari Esteri e della Cooperazione Internazionale, Direzione Generale per la Promozione del Sistema Paese, the Natural Sciences and Engineering Research Council of Canada, the Fonds Québécois de la recherche sur la nature et les technologies and a US Army Research Office. Federico Rosei is also grateful to the Canada Research Chairs program for funding and partial salary support.

About McGill University

Founded in Montreal, Quebec, in 1821, McGill is a leading Canadian post-secondary institution. It has two campuses, 11 faculties, 13 professional schools, 300 programs of study and over 40,000 students, including more than 10,200 graduate students. McGill attracts students from over 150 countries around the world, its 12,800 international students making up 31% per cent of the student body. Over half of McGill students claim a first language other than English, including approximately 19% of our students who say French is their mother tongue.

About the INRS
The Institut National de la Recherche Scientifique (INRS) is the only institution in Québec dedicated exclusively to graduate level university research and training. The impacts of its faculty and students are felt around the world. INRS proudly contributes to societal progress in partnership with industry and community stakeholders, both through its discoveries and by training new researchers and technicians to deliver scientific, social, and technological breakthroughs in the future.

Lakehead University
Lakehead University is a fully comprehensive university with approximately 9,700 full-time equivalent students and over 2,000 faculty and staff at two campuses in Orillia and Thunder Bay, Ontario. Lakehead has 10 faculties, including Business Administration, Education, Engineering, Graduate Studies, Health & Behavioural Sciences, Law, Natural Resources Management, the Northern Ontario School of Medicine, Science & Environmental Studies, and Social Sciences & Humanities. In 2019, Maclean’s 2020 University Rankings, once again, included Lakehead University among Canada’s Top 10 primarily undergraduate universities, while Research Infosource named Lakehead ‘Research University of the Year’ in its category for the fifth consecutive year. Visit www.lakeheadu.ca

I’m a little surprised there wasn’t a quote from one of the Italian researchers in the McGill news release but then there isn’t a quote in this slightly more accessible May 18, 2020 Consiglio Nazionale delle Ricerche press release either,

Graphene’s isolation took the world by surprise and was meant to revolutionize modern electronics. However, it was soon realized that its intrinsic properties limit the utilization in our daily electronic devices. When a concept of Mathematics, namely Topology, met the field of on-surface chemistry, new materials with exotic features were theoretically discovered. Topological materials exhibit technological relevant properties such as quantum hall conductivity that are protected by a concept similar to the comparison of a coffee mug and a donut.  These structures can be synthesized by the versatile molecular engineering toolbox that surface reactions provide. Nevertheless, the realization of such a material yields access to properties that suit the figure of merits for modern electronic application and could eventually for example lead to solve the ever-increasing heat conflict in chip design. However, problems such as low crystallinity and defect rich structures prevented the experimental observation and kept it for more than a decade a playground only investigated theoretically.

An international team of scientists from Institut National de la Recherche Scientifique (Centre Energie, Matériaux et Télécommunications), McGill University and Lakehead University, both located in Canada, and the SAMOS laboratory of the Istituto di Struttura della Materia (Cnr), led by Giorgio Contini, demonstrates, in a recent publication on Nature Materials, that the synthesis of two-dimensional π-conjugated polymers with topological Dirac cone and flats bands became a reality allowing a sneak peek into the world of organic topological materials.

Complementary work of organic chemists and surface scientists lead to two-dimensional polymers on a mesoscopic scale and granted access to their electronic properties. The band structure of the topological polymer reveals both flat bands and a Dirac cone confirming the prediction of theory. The observed coexistence of both structures is of particular interest, since whereas Dirac cones yield massless charge carriers (a band velocity of the same order of magnitude of graphene has been obtained), necessary for technological applications, flat bands quench the kinetic energy of charge carriers and could give rise to intriguing phenomena such as the anomalous Hall effect, surface superconductivity or superfluid transport.

This work paths multiple new roads – both theoretical and experimental nature. The integration of this topological polymer into a device such as transistors possibly reveals immense performance. On the other hand, it will foster many researchers to explore a wide range of two-dimensional polymers with different lattice symmetries, obtaining insight into the relationship between geometrical and electrical topology, which would in return be beneficial to fine tune a-priori theoretical studies. These materials – beyond graphene – could be then used for both their intrinsic properties as well as their interplay in new heterostructure designs.

The authors are currently exploring the practical use of the realized material trying to integrate it into transistors, pushing toward a complete designing of artificial topological lattices.

This work was partially supported by a project Grande Rilevanza Italy-Quebec of the Italian Ministero degli Affari Esteri e della Cooperazione Internazionale (MAECI), Direzione Generale per la Promozione del Sistema Paese.

The Italians also included an image to accompany their press release,

Image of the synthesized material and its band structure Courtesy: Consiglio Nazionale delle Ricerche

My heart sank when I saw the number of authors for this paper (WordPress no longer [since their Christmas 2018 update] makes it easy to add the author’s names quickly to the ‘tags field’). Regardless and in keeping with my practice, here’s a link to and a citation for the paper,

Synthesis of mesoscale ordered two-dimensional π-conjugated polymers with semiconducting properties by G. Galeotti, F. De Marchi, E. Hamzehpoor, O. MacLean, M. Rajeswara Rao, Y. Chen, L. V. Besteiro, D. Dettmann, L. Ferrari, F. Frezza, P. M. Sheverdyaeva, R. Liu, A. K. Kundu, P. Moras, M. Ebrahimi, M. C. Gallagher, F. Rosei, D. F. Perepichka & G. Contini. Nature Materials (2020) DOI: https://doi.org/10.1038/s41563-020-0682-z Published 18 May 2020

This paper is behind a paywall.

Handling massive digital datasets the quantum way

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A Jan. 25, 2016 news item on phys.org describes a new approach to analyzing and managing huge datasets,

From gene mapping to space exploration, humanity continues to generate ever-larger sets of data—far more information than people can actually process, manage, or understand.

Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.

Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at [Massachusetts Institute of Technology] MIT, the University of Waterloo, and the University of Southern California [USC}.

A Jan. 25, 2016 MIT news release (*also on EurekAlert*), which originated the news item, describes the theory in more detail,

… Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.

In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd [ explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”

It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”

Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.

Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.

“That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.

“Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.

There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.

The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.

While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”

Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”

Here’s a link to and a citation for the paper,

Quantum algorithms for topological and geometric analysis of data by Seth Lloyd, Silvano Garnerone, & Paolo Zanardi. Nature Communications 7, Article number: 10138 doi:10.1038/ncomms10138 Published 25 January 2016

This paper is open access.

ETA Jan. 25, 2016 1245 hours PST,

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

*’also on EurekAlert’ text and link added Jan. 26, 2016.