Tag Archives: US Army Research Office

Canadian and Italian researchers go beyond graphene with 2D polymers

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.

Automated science writing?

It seems that automated science writing is not ready—yet. Still, an April 18, 2019 news item on ScienceDaily suggests that progress is being made,

The work of a science writer, including this one, includes reading journal papers filled with specialized technical terminology, and figuring out how to explain their contents in language that readers without a scientific background can understand.

Now, a team of scientists at MIT [Massachusetts Institute of Technology] and elsewhere has developed a neural network, a form of artificial intelligence (AI), that can do much the same thing, at least to a limited extent: It can read scientific papers and render a plain-English summary in a sentence or two.

An April 17, 2019 MIT news release, which originated the news item, delves into the research and its implications,

Even in this limited form, such a neural network could be useful for helping editors, writers, and scientists [emphasis mine] scan a large number of papers to get a preliminary sense of what they’re about. But the approach the team developed could also find applications in a variety of other areas besides language processing, including machine translation and speech recognition.

The work is described in the journal Transactions of the Association for Computational Linguistics, in a paper by Rumen Dangovski and Li Jing, both MIT graduate students; Marin Soljačić, a professor of physics at MIT; Preslav Nakov, a principal scientist at the Qatar Computing Research Institute, HBKU; and Mićo Tatalović, a former Knight Science Journalism fellow at MIT and a former editor at New Scientist magazine.

From AI for physics to natural language

The work came about as a result of an unrelated project, which involved developing new artificial intelligence approaches based on neural networks, aimed at tackling certain thorny problems in physics. However, the researchers soon realized that the same approach could be used to address other difficult computational problems, including natural language processing, in ways that might outperform existing neural network systems.

“We have been doing various kinds of work in AI for a few years now,” Soljačić says. “We use AI to help with our research, basically to do physics better. And as we got to be  more familiar with AI, we would notice that every once in a while there is an opportunity to add to the field of AI because of something that we know from physics — a certain mathematical construct or a certain law in physics. We noticed that hey, if we use that, it could actually help with this or that particular AI algorithm.”

This approach could be useful in a variety of specific kinds of tasks, he says, but not all. “We can’t say this is useful for all of AI, but there are instances where we can use an insight from physics to improve on a given AI algorithm.”

Neural networks in general are an attempt to mimic the way humans learn certain new things: The computer examines many different examples and “learns” what the key underlying patterns are. Such systems are widely used for pattern recognition, such as learning to identify objects depicted in photos.

But neural networks in general have difficulty correlating information from a long string of data, such as is required in interpreting a research paper. Various tricks have been used to improve this capability, including techniques known as long short-term memory (LSTM) and gated recurrent units (GRU), but these still fall well short of what’s needed for real natural-language processing, the researchers say.

The team came up with an alternative system, which instead of being based on the multiplication of matrices, as most conventional neural networks are, is based on vectors rotating in a multidimensional space. The key concept is something they call a rotational unit of memory (RUM).

Essentially, the system represents each word in the text by a vector in multidimensional space — a line of a certain length pointing in a particular direction. Each subsequent word swings this vector in some direction, represented in a theoretical space that can ultimately have thousands of dimensions. At the end of the process, the final vector or set of vectors is translated back into its corresponding string of words.

“RUM helps neural networks to do two things very well,” Nakov says. “It helps them to remember better, and it enables them to recall information more accurately.”

After developing the RUM system to help with certain tough physics problems such as the behavior of light in complex engineered materials, “we realized one of the places where we thought this approach could be useful would be natural language processing,” says Soljačić,  recalling a conversation with Tatalović, who noted that such a tool would be useful for his work as an editor trying to decide which papers to write about. Tatalović was at the time exploring AI in science journalism as his Knight fellowship project.

“And so we tried a few natural language processing tasks on it,” Soljačić says. “One that we tried was summarizing articles, and that seems to be working quite well.”

The proof is in the reading

As an example, they fed the same research paper through a conventional LSTM-based neural network and through their RUM-based system. The resulting summaries were dramatically different.

The LSTM system yielded this highly repetitive and fairly technical summary: “Baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat.

Based on the same paper, the RUM system produced a much more readable summary, and one that did not include the needless repetition of phrases: Urban raccoons may infect people more than previously assumed. 7 percent of surveyed individuals tested positive for raccoon roundworm antibodies. Over 90 percent of raccoons in Santa Barbara play host to this parasite.

Already, the RUM-based system has been expanded so it can “read” through entire research papers, not just the abstracts, to produce a summary of their contents. The researchers have even tried using the system on their own research paper describing these findings — the paper that this news story is attempting to summarize.

Here is the new neural network’s summary: Researchers have developed a new representation process on the rotational unit of RUM, a recurrent memory that can be used to solve a broad spectrum of the neural revolution in natural language processing.

It may not be elegant prose, but it does at least hit the key points of information.

Çağlar Gülçehre, a research scientist at the British AI company Deepmind Technologies, who was not involved in this work, says this research tackles an important problem in neural networks, having to do with relating pieces of information that are widely separated in time or space. “This problem has been a very fundamental issue in AI due to the necessity to do reasoning over long time-delays in sequence-prediction tasks,” he says. “Although I do not think this paper completely solves this problem, it shows promising results on the long-term dependency tasks such as question-answering, text summarization, and associative recall.”

Gülçehre adds, “Since the experiments conducted and model proposed in this paper are released as open-source on Github, as a result many researchers will be interested in trying it on their own tasks. … To be more specific, potentially the approach proposed in this paper can have very high impact on the fields of natural language processing and reinforcement learning, where the long-term dependencies are very crucial.”

The research received support from the Army Research Office, the National Science Foundation, the MIT-SenseTime Alliance on Artificial Intelligence, and the Semiconductor Research Corporation. The team also had help from the Science Daily website, whose articles were used in training some of the AI models in this research.

As usual, this ‘automated writing system’ is framed as a ‘helper’ not an usurper of anyone’s job. However, its potential for changing the nature of the work is there. About five years ago I featured another ‘automated writing’ story in a July 16, 2014 posting titled: ‘Writing and AI or is a robot writing this blog?’ You may have been reading ‘automated’ news stories for years. At the time, the focus was on sports and business.

Getting back to 2019 and science writing, here’s a link to and a citation for the paper,

Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications by Rumen Dangovski, Li Jing, Preslav Nakov, Mićo Tatalović and Marin Soljačić. Transactions of the Association for Computational Linguistics Volume 07, 2019 pp.121-138 DOI: https://doi.org/10.1162/tacl_a_00258 Posted Online 2019

© 2019 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license.

This paper is open access.

Crowdsourcing brain research at Princeton University to discover 6 new neuron types

Spritely music!

There were already 1/4M registered players as of May 17, 2018 but I’m sure there’s room for more should you be inspired. A May 17, 2018 Princeton University news release (also on EurekAlert) reveals more about the game and about the neurons,

With the help of a quarter-million video game players, Princeton researchers have created and shared detailed maps of more than 1,000 neurons — and they’re just getting started.

“Working with Eyewirers around the world, we’ve made a digital museum that shows off the intricate beauty of the retina’s neural circuits,” said Sebastian Seung, the Evnin Professor in Neuroscience and a professor of computer science and the Princeton Neuroscience Institute (PNI). The related paper is publishing May 17 [2018] in the journal Cell.

Seung is unveiling the Eyewire Museum, an interactive archive of neurons available to the general public and neuroscientists around the world, including the hundreds of researchers involved in the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative.

“This interactive viewer is a huge asset for these larger collaborations, especially among people who are not physically in the same lab,” said Amy Robinson Sterling, a crowdsourcing specialist with PNI and the executive director of Eyewire, the online gaming platform for the citizen scientists who have created this data set.

“This museum is something like a brain atlas,” said Alexander Bae, a graduate student in electrical engineering and one of four co-first authors on the paper. “Previous brain atlases didn’t have a function where you could visualize by individual cell, or a subset of cells, and interact with them. Another novelty: Not only do we have the morphology of each cell, but we also have the functional data, too.”

The neural maps were developed by Eyewirers, members of an online community of video game players who have devoted hundreds of thousands of hours to painstakingly piecing together these neural cells, using data from a mouse retina gathered in 2009.

Eyewire pairs machine learning with gamers who trace the twisting and branching paths of each neuron. Humans are better at visually identifying the patterns of neurons, so every player’s moves are recorded and checked against each other by advanced players and Eyewire staffers, as well as by software that is improving its own pattern recognition skills.

Since Eyewire’s launch in 2012, more than 265,000 people have signed onto the game, and they’ve collectively colored in more than 10 million 3-D “cubes,” resulting in the mapping of more than 3,000 neural cells, of which about a thousand are displayed in the museum.

Each cube is a tiny subset of a single cell, about 4.5 microns across, so a 10-by-10 block of cubes would be the width of a human hair. Every cell is reviewed by between 5 and 25 gamers before it is accepted into the system as complete.

“Back in the early years it took weeks to finish a single cell,” said Sterling. “Now players complete multiple neurons per day.” The Eyewire user experience stays focused on the larger mission — “For science!” is a common refrain — but it also replicates a typical gaming environment, with achievement badges, a chat feature to connect with other players and technical support, and the ability to unlock privileges with increasing skill. “Our top players are online all the time — easily 30 hours a week,” Sterling said.

Dedicated Eyewirers have also contributed in other ways, including donating the swag that gamers win during competitions and writing program extensions “to make game play more efficient and more fun,” said Sterling, including profile histories, maps of player activity, a top 100 leaderboard and ever-increasing levels of customizability.

“The community has really been the driving force behind why Eyewire has been successful,” Sterling said. “You come in, and you’re not alone. Right now, there are 43 people online. Some of them will be admins from Boston or Princeton, but most are just playing — now it’s 46.”

For science!

With 100 billion neurons linked together via trillions of connections, the brain is immeasurably complex, and neuroscientists are still assembling its “parts list,” said Nicholas Turner, a graduate student in computer science and another of the co-first authors. “If you know what parts make up the machine you’re trying to break apart, you’re set to figure out how it all works,” he said.

The researchers have started by tackling Eyewire-mapped ganglion cells from the retina of a mouse. “The retina doesn’t just sense light,” Seung said. “Neural circuits in the retina perform the first steps of visual perception.”

The retina grows from the same embryonic tissue as the brain, and while much simpler than the brain, it is still surprisingly complex, Turner said. “Hammering out these details is a really valuable effort,” he said, “showing the depth and complexity that exists in circuits that we naively believe are simple.”

The researchers’ fundamental question is identifying exactly how the retina works, said Bae. “In our case, we focus on the structural morphology of the retinal ganglion cells.”

“Why the ganglion cells of the eye?” asked Shang Mu, an associate research scholar in PNI and fellow first author. “Because they’re the connection between the retina and the brain. They’re the only cell class that go back into the brain.” Different types of ganglion cells are known to compute different types of visual features, which is one reason the museum has linked shape to functional data.

Using Eyewire-produced maps of 396 ganglion cells, the researchers in Seung’s lab successfully classified these cells more thoroughly than has ever been done before.

“The number of different cell types was a surprise,” said Mu. “Just a few years ago, people thought there were only 15 to 20 ganglion cell types, but we found more than 35 — we estimate between 35 and 50 types.”

Of those, six appear to be novel, in that the researchers could not find any matching descriptions in a literature search.

A brief scroll through the digital museum reveals just how remarkably flat the neurons are — nearly all of the branching takes place along a two-dimensional plane. Seung’s team discovered that different cells grow along different planes, with some reaching high above the nucleus before branching out, while others spread out close to the nucleus. Their resulting diagrams resemble a rainforest, with ground cover, an understory, a canopy and an emergent layer overtopping the rest.

All of these are subdivisions of the inner plexiform layer, one of the five previously recognized layers of the retina. The researchers also identified a “density conservation principle” that they used to distinguish types of neurons.

One of the biggest surprises of the research project has been the extraordinary richness of the original sample, said Seung. “There’s a little sliver of a mouse retina, and almost 10 years later, we’re still learning things from it.”

Of course, it’s a mouse’s brain that you’ll be examining and while there are differences between a mouse brain and a human brain, mouse brains still provide valuable data as they did in the case of some groundbreaking research published in October 2017. James Hamblin wrote about it in an Oct. 7, 2017 article for The Atlantic (Note: Links have been removed),

 

Scientists Somehow Just Discovered a New System of Vessels in Our Brains

It is unclear what they do—but they likely play a central role in aging and disease.

A transparent model of the brain with a network of vessels filled in
Daniel Reich / National Institute of Neurological Disorders and Stroke

You are now among the first people to see the brain’s lymphatic system. The vessels in the photo above transport fluid that is likely crucial to metabolic and inflammatory processes. Until now, no one knew for sure that they existed.

Doctors practicing today have been taught that there are no lymphatic vessels inside the skull. Those deep-purple vessels were seen for the first time in images published this week by researchers at the U.S. National Institute of Neurological Disorders and Stroke.

In the rest of the body, the lymphatic system collects and drains the fluid that bathes our cells, in the process exporting their waste. It also serves as a conduit for immune cells, which go out into the body looking for adversaries and learning how to distinguish self from other, and then travel back to lymph nodes and organs through lymphatic vessels.

So how was it even conceivable that this process wasn’t happening in our brains?

Reich (Daniel Reich, senior investigator) started his search in 2015, after a major study in Nature reported a similar conduit for lymph in mice. The University of Virginia team wrote at the time, “The discovery of the central-nervous-system lymphatic system may call for a reassessment of basic assumptions in neuroimmunology.” The study was regarded as a potential breakthrough in understanding how neurodegenerative disease is associated with the immune system.

Around the same time, researchers discovered fluid in the brains of mice and humans that would become known as the “glymphatic system.” [emphasis mine] It was described by a team at the University of Rochester in 2015 as not just the brain’s “waste-clearance system,” but as potentially helping fuel the brain by transporting glucose, lipids, amino acids, and neurotransmitters. Although since “the central nervous system completely lacks conventional lymphatic vessels,” the researchers wrote at the time, it remained unclear how this fluid communicated with the rest of the body.

There are occasional references to the idea of a lymphatic system in the brain in historic literature. Two centuries ago, the anatomist Paolo Mascagni made full-body models of the lymphatic system that included the brain, though this was dismissed as an error. [emphases mine]  A historical account in The Lancet in 2003 read: “Mascagni was probably so impressed with the lymphatic system that he saw lymph vessels even where they did not exist—in the brain.”

I couldn’t resist the reference to someone whose work had been dismissed summarily being proved right, eventually, and with the help of mouse brains. Do read Hamblin’s article in its entirety if you have time as these excerpts don’t do it justice.

Getting back to Princeton’s research, here’s their research paper,

Digital museum of retinal ganglion cells with dense anatomy and physiology,” by Alexander Bae, Shang Mu, Jinseop Kim, Nicholas Turner, Ignacio Tartavull, Nico Kemnitz, Chris Jordan, Alex Norton, William Silversmith, Rachel Prentki, Marissa Sorek, Celia David, Devon Jones, Doug Bland, Amy Sterling, Jungman Park, Kevin Briggman, Sebastian Seung and the Eyewirers, was published May 17 in the journal Cell with DOI 10.1016/j.cell.2018.04.040.

The research was supported by the Gatsby Charitable Foundation, National Institute of Health-National Institute of Neurological Disorders and Stroke (U01NS090562 and 5R01NS076467), Defense Advanced Research Projects Agency (HR0011-14-2- 0004), Army Research Office (W911NF-12-1-0594), Intelligence Advanced Research Projects Activity (D16PC00005), KT Corporation, Amazon Web Services Research Grants, Korea Brain Research Institute (2231-415) and Korea National Research Foundation Brain Research Program (2017M3C7A1048086).

This paper is behind a paywall. For the players amongst us, here’s the Eyewire website. Go forth,  play, and, maybe, discover new neurons!

How to prevent your scanning tunneling microscope probe’s ‘tip crashes’

The microscopes used for nanoscale research were invented roughly 35 years ago and as fabulous as they’ve been, there is a problem (from a February 12, 2018 news item on Nanowerk),

A University of Texas at Dallas graduate student, his advisor and industry collaborators believe they have addressed a long-standing problem troubling scientists and engineers for more than 35 years: How to prevent the tip of a scanning tunneling microscope from crashing into the surface of a material during imaging or lithography.

The researchers have prepared this video describing their work,

For those who like text, there’s more in this February 12, 2018 University of Texas at Dallas news release,

Scanning tunneling microscopes (STMs) operate in an ultra-high vacuum, bringing a fine-tipped probe with a single atom at its apex very close to the surface of a sample. When voltage is applied to the surface, electrons can jump or tunnel across the gap between the tip and sample.

“Think of it as a needle that is very sharp, atomically sharp,” said Farid Tajaddodianfar, a mechanical engineering graduate student in the Erik Jonsson School of Engineering and Computer Science. “The microscope is like a robotic arm, able to reach atoms on the sample surface and manipulate them.”

The problem is, sometimes the tungsten tip crashes into the sample. If it physically touches the sample surface, it may inadvertently rearrange the atoms or create a “crater,” which could damage the sample. Such a “tip crash” often forces operators to replace the tip many times, forfeiting valuable time.

Dr. John Randall is an adjunct professor at UT Dallas and president of Zyvex Labs, a Richardson, Texas-based nanotechnology company specializing in developing tools and products that fabricate structures atom by atom. Zyvex reached out to Dr. Reza Moheimani, a professor of mechanical engineering, to help address STMs’ tip crash problem. Moheimani’s endowed chair was a gift from Zyvex founder James Von Ehr MS’81, who was honored as a distinguished UTD alumnus in 2004.

“What they’re trying to do is help bring atomically precise manufacturing into reality,” said Randall, who co-authored the article with Tajaddodianfar, Moheimani and Zyvex Labs’ James Owen. “This is considered the future of nanotechnology, and it is extremely important work.”

Randall said such precise manufacturing will lead to a host of innovations.

“By building structures atom by atom, you’re able to create new, extraordinary materials,” said Randall, who is co-chair of the Jonsson School’s Industry Engagement Committee. “We can remove impurities and make materials stronger and more heat resistant. We can build quantum computers. It could radically lower costs and expand capabilities in medicine and other areas. For example, if we can better understand DNA at an atomic and molecular level, that will help us fine-tune and tailor health care according to patients’ needs. The possibilities are endless.”

In addition, Moheimani, a control engineer and expert in nanotechnology, said scientists are attempting to build transistors and quantum computers from a single atom using this technology.

“There’s an international race to build machines, devices and 3-D equipment from the atom up,” said Moheimani, the James Von Ehr Distinguished Chair in Science and Technology.

‘It’s a Big, Big Problem’

Randall said Zyvex Labs has spent a lot of time and money trying to understand what happens to the tips when they crash.

“It’s a big, big problem,” Randall said. “If you can’t protect the tip, you’re not going to build anything. You’re wasting your time.”

Tajaddodianfar and Moheimani said the issue is the controller.

“There’s a feedback controller in the STM that measures the current and moves the needle up and down,” Moheimani said. “You’re moving from one atom to another, across an uneven surface. It is not flat. Because of that, the distance between the sample and tip changes, as does the current between them. While the controller tries to move the tip up and down to maintain the current, it does not always respond well, nor does it regulate the tip correctly. The resulting movement of the tip is often unstable.”

It’s the feedback controller that fails to protect the tip from crashing into the surface, Tajaddodianfar said.

“When the electronic properties are variable across the sample surface, the tip is more prone to crash under conventional control systems,” he said. “It’s meant to be really, really sharp. But when the tip crashes into the sample, it breaks, curls backward and flattens.

“Once the tip crashes into the surface, forget it. Everything changes.”

The Solution

According to Randall, Tajaddodianfar took logical steps for creating the solution.

“The brilliance of Tajaddodianfar is that he looked at the problem and understood the physics of the tunneling between the tip and the surface, that there is a small electronic barrier that controls the rate of tunneling,” Randall said. “He figured out a way of measuring that local barrier height and adjusting the gain on the control system that demonstrably keeps the tip out of trouble. Without it, the tip just bumps along, crashing into the surface. Now, it adjusts to the control parameters on the fly.”

Moheimani said the group hopes to change their trajectory when it comes to building new devices.

“That’s the next thing for us. We set out to find the source of this problem, and we did that. And, we’ve come up with a solution. It’s like everything else in science: Time will tell how impactful our work will be,” Moheimani said. “But I think we have solved the big problem.”

Randall said Tajaddodianfar’s algorithm has been integrated with its system’s software but is not yet available to customers. The research was made possible by funding from the Army Research Office and the Defense Advanced Research Projects Agency.

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

On the effect of local barrier height in scanning tunneling microscopy: Measurement methods and control implications by Farid Tajaddodianfar, S. O. Reza Moheimani, James Owen, and John N. Randall. Review of Scientific Instruments 89, 013701 (2018); https://doi.org/10.1063/1.5003851 Published Online: January 2018

This paper is behind a paywall.

Colliding organic nanoparticles caught on camera for the first time

There is high excitement about this development in a November 17, 2017 news item on Nanowerk,

A Northwestern University research team is the first to capture on video organic nanoparticles colliding and fusing together. This unprecedented view of “chemistry in motion” will aid Northwestern nanoscientists developing new drug delivery methods as well as demonstrate to researchers around the globe how an emerging imaging technique opens a new window on a very tiny world.

A November 17, 2017 Northwestern University news release (also on EurekAlert) by Megan Fellman, which originated the news item, further illuminates the matter,

This is a rare example of particles in motion. The dynamics are reminiscent of two bubbles coming together and merging into one: first they join and have a membrane between them, but then they fuse and become one larger bubble.

“I had an image in my mind, but the first time I saw these fusing nanoparticles in black and white was amazing,” said professor Nathan C. Gianneschi, who led the interdisciplinary study and works at the intersection of nanotechnology and biomedicine.

“To me, it’s literally a window opening up to this world you have always known was there, but now you’ve finally got an image of it. I liken it to the first time I saw Jupiter’s moons through a telescope. Nothing compares to actually seeing,” he said.

Gianneschi is the Jacob and Rosaline Cohn Professor in the department of chemistry in the Weinberg College of Arts and Sciences and in the departments of materials science and engineering and of biomedical engineering in the McCormick School of Engineering.

The study, which includes videos of different nanoparticle fusion events, was published today (Nov. 1 [2017]7) by the Journal of the American Chemical Society.

The research team used liquid-cell transmission electron microscopy to directly image how polymer-based nanoparticles, or micelles, that Gianneschi’s lab is developing for treating cancer and heart attacks change over time. The powerful new technique enabled the scientists to directly observe the particles’ transformation and characterize their dynamics.

“We can see on the molecular level how the polymeric matter rearranges when the particles fuse into one object,” said Lucas R. Parent, first author of the paper and a National Institutes of Health Postdoctoral Fellow in Gianneschi’s research group. “This is the first study of many to come in which researchers will use this method to look at all kinds of dynamic phenomena in organic materials systems on the nanoscale.”

In the Northwestern study, organic particles in water bounce off each other, and some collide and merge, undergoing a physical transformation. The researchers capture the action by shining an electron beam through the sample. The tiny particles — the largest are only approximately 200 nanometers in diameter — cast shadows that are captured directly by a camera below.

“We’ve observed classical fusion behavior on the nanoscale,” said Gianneschi, a member of Northwestern’s International Institute for Nanotechnology. “Capturing the fundamental growth and evolution processes of these particles in motion will help us immensely in our work with synthetic materials and their interactions with biological systems.”

The National Institutes of Health, the National Science Foundation, the Air Force Office of Scientific Research and the Army Research Office supported the research.

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

Directly Observing Micelle Fusion and Growth in Solution by Liquid-Cell Transmission Electron Microscopy by Lucas R. Parent, Evangelos Bakalis, Abelardo Ramírez-Hernández, Jacquelin K. Kammeyer, Chiwoo Park, Juan de Pablo, Francesco Zerbetto, Joseph P. Patterson, and Nathan C. Gianneschi. J. Am. Chem. Soc., Article ASAP DOI: 10.1021/jacs.7b09060 Publication Date (Web): November 17, 2017

Copyright © 2017 American Chemical Society

This paper is behind a paywall.

From flubber to thubber

Flubber (flying rubber) is an imaginary material that provided a plot point for two Disney science fiction comedies, The Absent-Minded Professor in 1961 which was remade in 1997 as Flubber. By contrast, ‘thubber’ (thermally conductive rubber) is a real life new material developed at Carnegie Mellon University (US).

A Feb. 13, 2017 news item on phys.org makes the announcement (Note: A link has been removed),

Carmel Majidi and Jonathan Malen of Carnegie Mellon University have developed a thermally conductive rubber material that represents a breakthrough for creating soft, stretchable machines and electronics. The findings were published in Proceedings of the National Academy of Sciences this week.

The new material, nicknamed “thubber,” is an electrically insulating composite that exhibits an unprecedented combination of metal-like thermal conductivity, elasticity similar to soft, biological tissue, and can stretch over six times its initial length.

A Feb.13, 2017 Carnegie Mellon University news release (also on EurekAlert), which originated the news item, provides more detail (Note A link has been removed),

“Our combination of high thermal conductivity and elasticity is especially critical for rapid heat dissipation in applications such as wearable computing and soft robotics, which require mechanical compliance and stretchable functionality,” said Majidi, an associate professor of mechanical engineering.

Applications could extend to industries like athletic wear and sports medicine—think of lighted clothing for runners and heated garments for injury therapy. Advanced manufacturing, energy, and transportation are other areas where stretchable electronic material could have an impact.

“Until now, high power devices have had to be affixed to rigid, inflexible mounts that were the only technology able to dissipate heat efficiently,” said Malen, an associate professor of mechanical engineering. “Now, we can create stretchable mounts for LED lights or computer processors that enable high performance without overheating in applications that demand flexibility, such as light-up fabrics and iPads that fold into your wallet.”

The key ingredient in “thubber” is a suspension of non-toxic, liquid metal microdroplets. The liquid state allows the metal to deform with the surrounding rubber at room temperature. When the rubber is pre-stretched, the droplets form elongated pathways that are efficient for heat travel. Despite the amount of metal, the material is also electrically insulating.

To demonstrate these findings, the team mounted an LED light onto a strip of the material to create a safety lamp worn around a jogger’s leg. The “thubber” dissipated the heat from the LED, which would have otherwise burned the jogger. The researchers also created a soft robotic fish that swims with a “thubber” tail, without using conventional motors or gears.

“As the field of flexible electronics grows, there will be a greater need for materials like ours,” said Majidi. “We can also see it used for artificial muscles that power bio-inspired robots.”

Majidi and Malen acknowledge the efforts of lead authors Michael Bartlett, Navid Kazem, and Matthew Powell-Palm in performing this multidisciplinary work. They also acknowledge funding from the Air Force, NASA, and the Army Research Office.

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

High thermal conductivity in soft elastomers with elongated liquid metal inclusions by Michael D. Bartlett, Navid Kazem, Matthew J. Powell-Palm, Xiaonan Huang, Wenhuan Sun, Jonathan A. Malen, and Carmel Majidi.  Proceedings of the National Academy of Sciences of the United States of America (PNAS, Proceedings of the National Academy of Sciences) doi: 10.1073/pnas.1616377114

This paper is open access.

Carbon nanotubes that can outperform silicon

According to a Sept. 2, 2016 news item on phys.org, researchers at the University of Wisconsin-Madison have produced carbon nanotube transistors that outperform state-of-the-art silicon transistors,

For decades, scientists have tried to harness the unique properties of carbon nanotubes to create high-performance electronics that are faster or consume less power—resulting in longer battery life, faster wireless communication and faster processing speeds for devices like smartphones and laptops.

But a number of challenges have impeded the development of high-performance transistors made of carbon nanotubes, tiny cylinders made of carbon just one atom thick. Consequently, their performance has lagged far behind semiconductors such as silicon and gallium arsenide used in computer chips and personal electronics.

Now, for the first time, University of Wisconsin-Madison materials engineers have created carbon nanotube transistors that outperform state-of-the-art silicon transistors.

Led by Michael Arnold and Padma Gopalan, UW-Madison professors of materials science and engineering, the team’s carbon nanotube transistors achieved current that’s 1.9 times higher than silicon transistors. …

A Sept. 2, 2016 University of Wisconsin-Madison news release (also on EurekAlert) by Adam Malecek, which originated the news item, describes the research in more detail and notes that the technology has been patented,

“This achievement has been a dream of nanotechnology for the last 20 years,” says Arnold. “Making carbon nanotube transistors that are better than silicon transistors is a big milestone. This breakthrough in carbon nanotube transistor performance is a critical advance toward exploiting carbon nanotubes in logic, high-speed communications, and other semiconductor electronics technologies.”

This advance could pave the way for carbon nanotube transistors to replace silicon transistors and continue delivering the performance gains the computer industry relies on and that consumers demand. The new transistors are particularly promising for wireless communications technologies that require a lot of current flowing across a relatively small area.

As some of the best electrical conductors ever discovered, carbon nanotubes have long been recognized as a promising material for next-generation transistors.

Carbon nanotube transistors should be able to perform five times faster or use five times less energy than silicon transistors, according to extrapolations from single nanotube measurements. The nanotube’s ultra-small dimension makes it possible to rapidly change a current signal traveling across it, which could lead to substantial gains in the bandwidth of wireless communications devices.

But researchers have struggled to isolate purely carbon nanotubes, which are crucial, because metallic nanotube impurities act like copper wires and disrupt their semiconducting properties — like a short in an electronic device.

The UW–Madison team used polymers to selectively sort out the semiconducting nanotubes, achieving a solution of ultra-high-purity semiconducting carbon nanotubes.

“We’ve identified specific conditions in which you can get rid of nearly all metallic nanotubes, where we have less than 0.01 percent metallic nanotubes,” says Arnold.

Placement and alignment of the nanotubes is also difficult to control.

To make a good transistor, the nanotubes need to be aligned in just the right order, with just the right spacing, when assembled on a wafer. In 2014, the UW–Madison researchers overcame that challenge when they announced a technique, called “floating evaporative self-assembly,” that gives them this control.

The nanotubes must make good electrical contacts with the metal electrodes of the transistor. Because the polymer the UW–Madison researchers use to isolate the semiconducting nanotubes also acts like an insulating layer between the nanotubes and the electrodes, the team “baked” the nanotube arrays in a vacuum oven to remove the insulating layer. The result: excellent electrical contacts to the nanotubes.

The researchers also developed a treatment that removes residues from the nanotubes after they’re processed in solution.

“In our research, we’ve shown that we can simultaneously overcome all of these challenges of working with nanotubes, and that has allowed us to create these groundbreaking carbon nanotube transistors that surpass silicon and gallium arsenide transistors,” says Arnold.

The researchers benchmarked their carbon nanotube transistor against a silicon transistor of the same size, geometry and leakage current in order to make an apples-to-apples comparison.

They are continuing to work on adapting their device to match the geometry used in silicon transistors, which get smaller with each new generation. Work is also underway to develop high-performance radio frequency amplifiers that may be able to boost a cellphone signal. While the researchers have already scaled their alignment and deposition process to 1 inch by 1 inch wafers, they’re working on scaling the process up for commercial production.

Arnold says it’s exciting to finally reach the point where researchers can exploit the nanotubes to attain performance gains in actual technologies.

“There has been a lot of hype about carbon nanotubes that hasn’t been realized, and that has kind of soured many people’s outlook,” says Arnold. “But we think the hype is deserved. It has just taken decades of work for the materials science to catch up and allow us to effectively harness these materials.”

The researchers have patented their technology through the Wisconsin Alumni Research Foundation.

Interestingly, at least some of the research was publicly funded according to the news release,

Funding from the National Science Foundation, the Army Research Office and the Air Force supported their work.

Will the public ever benefit financially from this research?

Artificial intelligence used for wildlife protection

PAWS (Protection Assistant for Wildlife Security), an artificial intelligence (AI) program, has been tested in Uganda and Malaysia. according to an April 22, 2016 US National Science Foundation (NSF) news release (also on EurekAlert but dated April 21, 2016), Note: Links have been removed,

A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200. Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction. The same applies to other large animal species like elephants and rhinoceros that play unique and crucial roles in the ecosystems where they live.

Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks. However, protection agencies have limited resources for patrols.

With support from the National Science Foundation (NSF) and the Army Research Office, researchers are using artificial intelligence (AI) and game theory to solve poaching, illegal logging and other problems worldwide, in collaboration with researchers and conservationists in the U.S., Singapore, Netherlands and Malaysia.

“In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual,” according to Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California (USC).

Fang is part of an NSF-funded team at USC led by Milind Tambe, professor of computer science and industrial and systems engineering and director of the Teamcore Research Group on Agents and Multiagent Systems.

Their research builds on the idea of “green security games” — the application of game theory to wildlife protection. Game theory uses mathematical and computer models of conflict and cooperation between rational decision-makers to predict the behavior of adversaries and plan optimal approaches for containment. The Coast Guard and Transportation Security Administration have used similar methods developed by Tambe and others to protect airports and waterways.

“This research is a step in demonstrating that AI can have a really significant positive impact on society and allow us to assist humanity in solving some of the major challenges we face,” Tambe said.

PAWS puts the claws in anti-poaching

The team presented papers describing how they use their methods to improve the success of human patrols around the world at the AAAI Conference on Artificial Intelligence in February [2016].

The researchers first created an AI-driven application called PAWS (Protection Assistant for Wildlife Security) in 2013 and tested the application in Uganda and Malaysia in 2014. Pilot implementations of PAWS revealed some limitations, but also led to significant improvements.

Here’s a video describing the issues and PAWS,

For those who prefer to read about details rather listen, there’s more from the news release,

PAWS uses data on past patrols and evidence of poaching. As it receives more data, the system “learns” and improves its patrol planning. Already, the system has led to more observations of poacher activities per kilometer.

Its key technical advance lies in its ability to incorporate complex terrain information, including the topography of protected areas. That results in practical patrol routes that minimize elevation changes, saving time and energy. Moreover, the system can also take into account the natural transit paths that have the most animal traffic – and thus the most poaching – creating a “street map” for patrols.

“We need to provide actual patrol routes that can be practically followed,” Fang said. “These routes need to go back to a base camp and the patrols can’t be too long. We list all possible patrol routes and then determine which is most effective.”

The application also randomizes patrols to avoid falling into predictable patterns.

“If the poachers observe that patrols go to some areas more often than others, then the poachers place their snares elsewhere,” Fang said.

Since 2015, two non-governmental organizations, Panthera and Rimbat, have used PAWS to protect forests in Malaysia. The research won the Innovative Applications of Artificial Intelligence award for deployed application, as one of the best AI applications with measurable benefits.

The team recently combined PAWS with a new tool called CAPTURE (Comprehensive Anti-Poaching Tool with Temporal and Observation Uncertainty Reasoning) that predicts attacking probability even more accurately.

In addition to helping patrols find poachers, the tools may assist them with intercepting trafficked wildlife products and other high-risk cargo, adding another layer to wildlife protection. The researchers are in conversations with wildlife authorities in Uganda to deploy the system later this year. They will present their findings at the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016) in May.

“There is an urgent need to protect the natural resources and wildlife on our beautiful planet, and we computer scientists can help in various ways,” Fang said. “Our work on PAWS addresses one facet of the problem, improving the efficiency of patrols to combat poaching.”

There is yet another potential use for PAWS, the prevention of illegal logging,

While Fang and her colleagues work to develop effective anti-poaching patrol planning systems, other members of the USC team are developing complementary methods to prevent illegal logging, a major economic and environmental problem for many developing countries.

The World Wildlife Fund estimates trade in illegally harvested timber to be worth between $30 billion and $100 billion annually. The practice also threatens ancient forests and critical habitats for wildlife.

Researchers at USC, the University of Texas at El Paso and Michigan State University recently partnered with the non-profit organization Alliance Vohoary Gasy to limit the illegal logging of rosewood and ebony trees in Madagascar, which has caused a loss of forest cover on the island nation.

Forest protection agencies also face limited budgets and must cover large areas, making sound investments in security resources critical.

The research team worked to determine the balance of security resources in which Madagascar should invest to maximize protection, and to figure out how to best deploy those resources.

Past work in game theory-based security typically involved specified teams — the security workers assigned to airport checkpoints, for example, or the air marshals deployed on flight tours. Finding optimal security solutions for those scenarios is difficult; a solution involving an open-ended team had not previously been feasible.

To solve this problem, the researchers developed a new method called SORT (Simultaneous Optimization of Resource Teams) that they have been experimentally validating using real data from Madagascar.

The research team created maps of the national parks, modeled the costs of all possible security resources using local salaries and budgets, and computed the best combination of resources given these conditions.

“We compared the value of using an optimal team determined by our algorithm versus a randomly chosen team and the algorithm did significantly better,” said Sara Mc Carthy, a Ph.D. student in computer science at USC.

The algorithm is simple and fast, and can be generalized to other national parks with different characteristics. The team is working to deploy it in Madagascar in association with the Alliance Vohoary Gasy.

“I am very proud of what my PhD students Fei Fang and Sara Mc Carthy have accomplished in this research on AI for wildlife security and forest protection,” said Tambe, the team lead. “Interdisciplinary collaboration with practitioners in the field was key in this research and allowed us to improve our research in artificial intelligence.”

Moreover, the project shows other computer science researchers the potential impact of applying their research to the world’s problems.

“This work is not only important because of the direct beneficial impact that it has on the environment, protecting wildlife and forests, but on the way that it can inspire other to dedicate their efforts into making the world a better place,” Mc Carthy said.

The curious can find out more about Panthera here and about Alliance Vohoary Gasy here (be prepared to use your French language skills). Unfortunately, I could not find more information about Rimbat.

Carbon nanotubes sense spoiled food

CNT_FoodSpolage

Courtesy: MIT (Massachusetts Institute of Technology)

I love this .gif; it says a lot without a word. However for details, you need words and here’s what an April 15, 2015 news item on Nanowerk has to say about the research illustrated by the .gif,

MIT [Massachusetts Institute of Technology] chemists have devised an inexpensive, portable sensor that can detect gases emitted by rotting meat, allowing consumers to determine whether the meat in their grocery store or refrigerator is safe to eat.

The sensor, which consists of chemically modified carbon nanotubes, could be deployed in “smart packaging” that would offer much more accurate safety information than the expiration date on the package, says Timothy Swager, the John D. MacArthur Professor of Chemistry at MIT.

An April 14, 2015 MIT news release (also on EurekAlert), which originated the news item, offers more from Dr. Swager,

It could also cut down on food waste, he adds. “People are constantly throwing things out that probably aren’t bad,” says Swager, who is the senior author of a paper describing the new sensor this week in the journal Angewandte Chemie.

This latest study is builds on previous work at Swager’s lab (Note: Links have been removed),

The sensor is similar to other carbon nanotube devices that Swager’s lab has developed in recent years, including one that detects the ripeness of fruit. All of these devices work on the same principle: Carbon nanotubes can be chemically modified so that their ability to carry an electric current changes in the presence of a particular gas.

In this case, the researchers modified the carbon nanotubes with metal-containing compounds called metalloporphyrins, which contain a central metal atom bound to several nitrogen-containing rings. Hemoglobin, which carries oxygen in the blood, is a metalloporphyrin with iron as the central atom.

For this sensor, the researchers used a metalloporphyrin with cobalt at its center. Metalloporphyrins are very good at binding to nitrogen-containing compounds called amines. Of particular interest to the researchers were the so-called biogenic amines, such as putrescine and cadaverine, which are produced by decaying meat.

When the cobalt-containing porphyrin binds to any of these amines, it increases the electrical resistance of the carbon nanotube, which can be easily measured.

“We use these porphyrins to fabricate a very simple device where we apply a potential across the device and then monitor the current. When the device encounters amines, which are markers of decaying meat, the current of the device will become lower,” Liu says.

In this study, the researchers tested the sensor on four types of meat: pork, chicken, cod, and salmon. They found that when refrigerated, all four types stayed fresh over four days. Left unrefrigerated, the samples all decayed, but at varying rates.

There are other sensors that can detect the signs of decaying meat, but they are usually large and expensive instruments that require expertise to operate. “The advantage we have is these are the cheapest, smallest, easiest-to-manufacture sensors,” Swager says.

“There are several potential advantages in having an inexpensive sensor for measuring, in real time, the freshness of meat and fish products, including preventing foodborne illness, increasing overall customer satisfaction, and reducing food waste at grocery stores and in consumers’ homes,” says Roberto Forloni, a senior science fellow at Sealed Air, a major supplier of food packaging, who was not part of the research team.

The new device also requires very little power and could be incorporated into a wireless platform Swager’s lab recently developed that allows a regular smartphone to read output from carbon nanotube sensors such as this one.

The funding sources are interesting, as I am appreciating with increasing frequency these days (from the news release),

The researchers have filed for a patent on the technology and hope to license it for commercial development. The research was funded by the National Science Foundation and the Army Research Office through MIT’s Institute for Soldier Nanotechnologies.

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

Single-Walled Carbon Nanotube/Metalloporphyrin Composites for the Chemiresistive Detection of Amines and Meat Spoilage by Sophie F. Liu, Alexander R. Petty, Dr. Graham T. Sazama, and Timothy M. Swager. Angewandte Chemie International Edition DOI: 10.1002/anie.201501434 Article first published online: 13 APR 2015

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This article is behind a paywall.

There are other posts here about the quest to create food sensors including this Sept. 26, 2013 piece which features a critique (by another blogger) about trying to create food sensors that may be more expensive than the item they are protecting, a problem Swager claims to have overcome in an April 17, 2015 article by Ben Schiller for Fast Company (Note: Links have been removed),

Swager has set up a company to commercialize the technology and he expects to do the first demonstrations to interested clients this summer. The first applications are likely to be for food workers working with meat and fish, but there’s no reason why consumers shouldn’t get their own devices in due time.

There are efforts to create visual clues for food status. But Swager says his method is better because it doesn’t rely on perception: it produces hard data that can be logged and tracked. And it also has potential to be very cheap.

“The resistance method is a game-changer because it’s two to three orders of magnitude cheaper than other technology. It’s hard to imagine doing this cheaper,” he says.