Tag Archives: National Institute for Materials Science (NIMS)

A tangle of silver nanowires for brain-like action

I’ve been meaning to get to this news item from late 2019 as it features work from a team that I’ve been following for a number of years now. First mentioned here in an October 17, 2011 posting, James Gimzewski has been working with researchers at the University of California at Los Angeles (UCLA) and researchers at Japan’s National Institute for Materials Science (NIMS) on neuromorphic computing.

This particular research had a protracted rollout with the paper being published in October 2019 and the last news item about it being published in mid-December 2019.

A December 17, 2029 news item on Nanowerk was the first to alert me to this new work (Note: A link has been removed),

UCLA scientists James Gimzewski and Adam Stieg are part of an international research team that has taken a significant stride toward the goal of creating thinking machines.

Led by researchers at Japan’s National Institute for Materials Science, the team created an experimental device that exhibited characteristics analogous to certain behaviors of the brain — learning, memorization, forgetting, wakefulness and sleep. The paper, published in Scientific Reports (“Emergent dynamics of neuromorphic nanowire networks”), describes a network in a state of continuous flux.

A December 16, 2019 UCLA news release, which originated the news item, offers more detail (Note: A link has been removed),

“This is a system between order and chaos, on the edge of chaos,” said Gimzewski, a UCLA distinguished professor of chemistry and biochemistry, a member of the California NanoSystems Institute at UCLA and a co-author of the study. “The way that the device constantly evolves and shifts mimics the human brain. It can come up with different types of behavior patterns that don’t repeat themselves.”

The research is one early step along a path that could eventually lead to computers that physically and functionally resemble the brain — machines that may be capable of solving problems that contemporary computers struggle with, and that may require much less power than today’s computers do.

The device the researchers studied is made of a tangle of silver nanowires — with an average diameter of just 360 nanometers. (A nanometer is one-billionth of a meter.) The nanowires were coated in an insulating polymer about 1 nanometer thick. Overall, the device itself measured about 10 square millimeters — so small that it would take 25 of them to cover a dime.

Allowed to randomly self-assemble on a silicon wafer, the nanowires formed highly interconnected structures that are remarkably similar to those that form the neocortex, the part of the brain involved with higher functions such as language, perception and cognition.

One trait that differentiates the nanowire network from conventional electronic circuits is that electrons flowing through them cause the physical configuration of the network to change. In the study, electrical current caused silver atoms to migrate from within the polymer coating and form connections where two nanowires overlap. The system had about 10 million of these junctions, which are analogous to the synapses where brain cells connect and communicate.

The researchers attached two electrodes to the brain-like mesh to profile how the network performed. They observed “emergent behavior,” meaning that the network displayed characteristics as a whole that could not be attributed to the individual parts that make it up. This is another trait that makes the network resemble the brain and sets it apart from conventional computers.

After current flowed through the network, the connections between nanowires persisted for as much as one minute in some cases, which resembled the process of learning and memorization in the brain. Other times, the connections shut down abruptly after the charge ended, mimicking the brain’s process of forgetting.

In other experiments, the research team found that with less power flowing in, the device exhibited behavior that corresponds to what neuroscientists see when they use functional MRI scanning to take images of the brain of a sleeping person. With more power, the nanowire network’s behavior corresponded to that of the wakeful brain.

The paper is the latest in a series of publications examining nanowire networks as a brain-inspired system, an area of research that Gimzewski helped pioneer along with Stieg, a UCLA research scientist and an associate director of CNSI.

“Our approach may be useful for generating new types of hardware that are both energy-efficient and capable of processing complex datasets that challenge the limits of modern computers,” said Stieg, a co-author of the study.

The borderline-chaotic activity of the nanowire network resembles not only signaling within the brain but also other natural systems such as weather patterns. That could mean that, with further development, future versions of the device could help model such complex systems.

In other experiments, Gimzewski and Stieg already have coaxed a silver nanowire device to successfully predict statistical trends in Los Angeles traffic patterns based on previous years’ traffic data.

Because of their similarities to the inner workings of the brain, future devices based on nanowire technology could also demonstrate energy efficiency like the brain’s own processing. The human brain operates on power roughly equivalent to what’s used by a 20-watt incandescent bulb. By contrast, computer servers where work-intensive tasks take place — from training for machine learning to executing internet searches — can use the equivalent of many households’ worth of energy, with the attendant carbon footprint.

“In our studies, we have a broader mission than just reprogramming existing computers,” Gimzewski said. “Our vision is a system that will eventually be able to handle tasks that are closer to the way the human being operates.”

The study’s first author, Adrian Diaz-Alvarez, is from the International Center for Material Nanoarchitectonics at Japan’s National Institute for Materials Science. Co-authors include Tomonobu Nakayama and Rintaro Higuchi, also of NIMS; and Zdenka Kuncic at the University of Sydney in Australia.

Caption: (a) Micrograph of the neuromorphic network fabricated by this research team. The network contains of numerous junctions between nanowires, which operate as synaptic elements. When voltage is applied to the network (between the green probes), current pathways (orange) are formed in the network. (b) A Human brain and one of its neuronal networks. The brain is known to have a complex network structure and to operate by means of electrical signal propagation across the network. Credit: NIMS

A November 11, 2019 National Institute for Materials Science (Japan) press release (also on EurekAlert but dated December 25, 2019) first announced the news,

An international joint research team led by NIMS succeeded in fabricating a neuromorphic network composed of numerous metallic nanowires. Using this network, the team was able to generate electrical characteristics similar to those associated with higher order brain functions unique to humans, such as memorization, learning, forgetting, becoming alert and returning to calm. The team then clarified the mechanisms that induced these electrical characteristics.

The development of artificial intelligence (AI) techniques has been rapidly advancing in recent years and has begun impacting our lives in various ways. Although AI processes information in a manner similar to the human brain, the mechanisms by which human brains operate are still largely unknown. Fundamental brain components, such as neurons and the junctions between them (synapses), have been studied in detail. However, many questions concerning the brain as a collective whole need to be answered. For example, we still do not fully understand how the brain performs such functions as memorization, learning and forgetting, and how the brain becomes alert and returns to calm. In addition, live brains are difficult to manipulate in experimental research. For these reasons, the brain remains a “mysterious organ.” A different approach to brain research?in which materials and systems capable of performing brain-like functions are created and their mechanisms are investigated?may be effective in identifying new applications of brain-like information processing and advancing brain science.

The joint research team recently built a complex brain-like network by integrating numerous silver (Ag) nanowires coated with a polymer (PVP) insulating layer approximately 1 nanometer in thickness. A junction between two nanowires forms a variable resistive element (i.e., a synaptic element) that behaves like a neuronal synapse. This nanowire network, which contains a large number of intricately interacting synaptic elements, forms a “neuromorphic network”. When a voltage was applied to the neuromorphic network, it appeared to “struggle” to find optimal current pathways (i.e., the most electrically efficient pathways). The research team measured the processes of current pathway formation, retention and deactivation while electric current was flowing through the network and found that these processes always fluctuate as they progress, similar to the human brain’s memorization, learning, and forgetting processes. The observed temporal fluctuations also resemble the processes by which the brain becomes alert or returns to calm. Brain-like functions simulated by the neuromorphic network were found to occur as the huge number of synaptic elements in the network collectively work to optimize current transport, in the other words, as a result of self-organized and emerging dynamic processes..

The research team is currently developing a brain-like memory device using the neuromorphic network material. The team intends to design the memory device to operate using fundamentally different principles than those used in current computers. For example, while computers are currently designed to spend as much time and electricity as necessary in pursuit of absolutely optimum solutions, the new memory device is intended to make a quick decision within particular limits even though the solution generated may not be absolutely optimum. The team also hopes that this research will facilitate understanding of the brain’s information processing mechanisms.

This project was carried out by an international joint research team led by Tomonobu Nakayama (Deputy Director, International Center for Materials Nanoarchitectonics (WPI-MANA), NIMS), Adrian Diaz Alvarez (Postdoctoral Researcher, WPI-MANA, NIMS), Zdenka Kuncic (Professor, School of Physics, University of Sydney, Australia) and James K. Gimzewski (Professor, California NanoSystems Institute, University of California Los Angeles, USA).

Here at last is a link to and a citation for the paper,

Emergent dynamics of neuromorphic nanowire networks by Adrian Diaz-Alvarez, Rintaro Higuchi, Paula Sanz-Leon, Ido Marcus, Yoshitaka Shingaya, Adam Z. Stieg, James K. Gimzewski, Zdenka Kuncic & Tomonobu Nakayama. Scientific Reports volume 9, Article number: 14920 (2019) DOI: https://doi.org/10.1038/s41598-019-51330-6 Published: 17 October 2019

This paper is open access.

May 28, 2018 release date for inorganic material database ‘AtomWork-Adv’

Announced in a May 23, 2018 news item on Nanowerk,

[Japan National Institute for Materials Science] NIMS will make its inorganic materials database, AtomWork-Adv (pronounced “atom work advanced”), available to the general public as a fee-based service starting Monday, May 28, 2018. This service will be provided by the Data Platform from the Center for Materials Research by Information Integration (CMI2), Research and Services Division of the Materials Data and Integrated System (MaDIS), NIMS.

A May 23, 2018 NIMS press release, which originated the news item, fills out the details (Note: Paragraph 1 is largely repetitive but there is contact information in there),

  1. NIMS will make its inorganic materials database, “AtomWork-Adv” (pronounced “atom work advanced”), available to the general public as a fee-based service starting Monday, May 28, 2018. This service will be provided by the Data Platform (Yibin Xu, Director) from the Center for Materials Research by Information Integration (CMI2), Research and Services Division of the Materials Data and Integrated System (MaDIS), NIMS. AtomWork-Adv markedly improves upon the amount of data available and the usability of the current web-based “AtomWork” database. We hope that this service will promote data-driven materials development using AI and machine learning.
  2. The increasingly popular use of AI and machine learning in materials development requires a high-quality materials database. NIMS publicized the AtomWork inorganic materials database—constructed using literature published up to 2002—on its MatNavi webpage (http://mits.nims.go.jp/index.html). While AtomWork is available free of charge, the data it contains is not updated and its functions, such as allowing users to copy, download and search data, are limited.
  3. The AtomWork-Adv database compiles crystal structure data (approx. 274,000 datasets), X-ray diffraction data (approx. 496,000 datasets), material properties data (approx. 298,000 datasets) and phase diagram data (approx. 40,000 datasets) collected from literature published up to 2014. The number of datasets in these categories are about three to five times greater than those in the Atom Work database. In addition, AtomWork-Adv is equipped with user-friendly functions, such as a versatile search tool which enables searches by element, composition, crystal structure and material property, a matrix function which identifies the number of datasets available for a specific binary material combination and an automatic charting function which allows the plotting of a graph between two material property variables and which displays material names in relation to these variables. Users can download data for use in data science-driven materials research and development (the number of downloads is restricted).
  4. Data will be continuously added to and updated in the fee-based AtomWork-Adv database. We are planning to add new data collected from literature between 2015 and 2016 to the database during FY2018.
  5. This database project was supported by the “Materials Research by Information Integration” Initiative (MI2I) sponsored by the Japan Science and Technology Agency (JST)’s Support Program for Starting up Innovation Hub.

Happy atom hunting!

International NanoCar race: 1st ever to be held in Autumn 2016

They have a very intriguing set of rules for the 1st ever International NanoCar Race to be held in Toulouse, France in October 2016. From the Centre d’Élaboration de Matériaux et d’Études Structurales (CEMES) Molecule-car Race International page (Note: A link has been removed),

1) General regulations

The molecule-car of a registered team has at its disposal a runway prepared on a small portion of the (111) face of the same crystalline gold surface. The surface is maintained at a very low temperature that is 5 Kelvin = – 268°C (LT) in ultra-high vacuum that is 10-8 Pa or 10-10 mbar 10-10 Torr (UHV) for at least the duration of the competition. The race itself last no more than 2 days and 2 nights including the construction time needed to build up atom by atom the same identical runway for each competitor. The construction and the imaging of a given runway are obtained by a low temperature scanning tunneling microscope (LT-UHV-STM) and certified by independent Track Commissioners before the starting of the race itself.

On this gold surface and per competitor, one runway is constructed atom by atom using a few surface gold metal ad-atoms. A molecule-car has to circulate around those ad-atoms, from the starting to the arrival lines, each line being delimited by 2 gold ad-atoms. The spacing between two metal ad-atoms along a runway is less than 4 nm. A minimum of 5 gold ad-atoms line has to be constructed per team and per runway.

The organizers have included an example of a runway,

A preliminary runway constructed by C. Manzano and We Hyo Soe (A*Star, IMRE) in Singapore, with the 2 starting gold ad-atoms, the 5 gold ad-atoms for the track and the 2 gold ad-atoms had been already constructed atom by atom.

A preliminary runway constructed by C. Manzano and We Hyo Soe (A*Star, IMRE) in Singapore, with the 2 starting gold ad-atoms, the 5 gold ad-atoms for the track and the 2 gold ad-atoms had been already constructed atom by atom.

A November 25, 2015 [France] Centre National de la Recherche Scientifique (CNRS) press release notes that five teams presented prototypes at the Futurapolis 2015 event preparatory to the upcoming Autumn 2016 race,

The French southwestern town of Toulouse is preparing for the first-ever international race of molecule-cars: five teams will present their car prototype during the Futurapolis event on November 27, 2015. These cars, which only measure a few nanometers in length and are propelled by an electric current, are scheduled to compete on a gold atom surface next year. Participants will be able to synthesize and test their molecule-car until October 2016 prior to taking part in the NanoCar Race organized at the CNRS Centre d’élaboration des matériaux et d’études structurales (CEMES) by Christian Joachim, senior researcher at the CNRS and Gwénaël Rapenne, professor at Université Toulouse III-Paul Sabatier, with the support of the CNRS.

There is a video describing the upcoming 2016 race (English, spoken and in subtitles),

NanoCar Race, the first-ever race of molecule-cars by CNRS-en

A Dec. 14, 2015 Rice University news release provides more detail about the event and Rice’s participation,

Rice University will send an entry to the first international NanoCar Race, which will be held next October at Pico-Lab CEMES-CNRS in Toulouse, France.

Nobody will see this miniature grand prix, at least not directly. But cars from five teams, including a collaborative effort by the Rice lab of chemist James Tour and scientists at the University of Graz, Austria, will be viewable through sophisticated microscopes developed for the event.

Time trials will determine which nanocar is the fastest, though there may be head-to-head races with up to four cars on the track at once, according to organizers.

A nanocar is a single-molecule vehicle of 100 or so atoms that incorporates a chassis, axles and freely rotating wheels. Each of the entries will be propelled across a custom-built gold surface by an electric current supplied by the tip of a scanning electron microscope. The track will be cold at 5 kelvins (minus 450 degrees Fahrenheit) and in a vacuum.

Rice’s entry will be a new model and the latest in a line that began when Tour and his team built the world’s first nanocar more than 10 years ago.

“It’s challenging because, first of all, we have to design a car that can be manipulated on that specific surface,” Tour said. “Then we have to figure out the driving techniques that are appropriate for that car. But we’ll be ready.”

Victor Garcia, a graduate student at Rice, is building what Tour called his group’s Model 1, which will be driven by members of Professor Leonhard Grill’s group at Graz. The labs are collaborating to optimize the design.

The races are being organized by the Center for Materials Elaboration and Structural Studies (CEMES) of the French National Center for Scientific Research (CNRS).

The race was first proposed in a 2013 ACS Nano paper by Christian Joachim, a senior researcher at CNRS, and Gwénaël Rapenne, a professor at Paul Sabatier University.

Joining Rice are teams from Ohio University; Dresden University of Technology; the National Institute for Materials Science, Tsukuba, Japan; and Paul Sabatier [Université Toulouse III-Paul Sabatier].

I believe there’s still time to register an entry (from the Molecule-car Race International page; Note: Links have been removed),

To register for the first edition of the molecule-car Grand Prix in Toulouse, a team has to deliver to the organizers well before March 2016:

  • The detail of its institution (Academic, public, private)
  • The design of its molecule-vehicle including the delivery of the xyz file coordinates of the atomic structure of its molecule-car
  • The propulsion mode, preferably by tunneling inelastic effects
  • The evaporation conditions of the molecule-vehicles
  • If possible a first UHV-STM image of the molecule-vehicle
  • The name and nationality of the LT-UHV-STM driver

Those information are used by the organizers for selecting the teams and for organizing training sessions for the accepted teams in a way to optimize their molecule-car design and to learn the driving conditions on the LT-Nanoprobe instrument in Toulouse. Then, the organizers will deliver an official invitation letter for a given team to have the right to experiment on the Toulouse LT-Nanoprobe instrument with their own drivers. A detail training calendar will be determined starting September 2015.

The NanoCar Race website’s homepage notes that it will be possible to view the race in some fashion,

The NanoCar Race is a race where molecular machines compete on a nano-sized track. A NanoCar is a single molecule-car that has wheels and a chassis… and is propelled by a small electric shock.

The race will be invisible to the naked eye: a unique microscope based in Toulouse, France, will make it possible to watch the competition.

The NanoCar race is mostly a fantastic human and scientific adventure that will be broadcast worldwide. [emphasis mine]

Good luck to all the competitors.

Brain-on-a-chip 2014 survey/overview

Michael Berger has written another of his Nanowerk Spotlight articles focussing on neuromorphic engineering and the concept of a brain-on-a-chip bringing it up-to-date April 2014 style.

It’s a topic he and I have been following (separately) for years. Berger’s April 4, 2014 Brain-on-a-chip Spotlight article provides a very welcome overview of the international neuromorphic engineering effort (Note: Links have been removed),

Constructing realistic simulations of the human brain is a key goal of the Human Brain Project, a massive European-led research project that commenced in 2013.

The Human Brain Project is a large-scale, scientific collaborative project, which aims to gather all existing knowledge about the human brain, build multi-scale models of the brain that integrate this knowledge and use these models to simulate the brain on supercomputers. The resulting “virtual brain” offers the prospect of a fundamentally new and improved understanding of the human brain, opening the way for better treatments for brain diseases and for novel, brain-like computing technologies.

Several years ago, another European project named FACETS (Fast Analog Computing with Emergent Transient States) completed an exhaustive study of neurons to find out exactly how they work, how they connect to each other and how the network can ‘learn’ to do new things. One of the outcomes of the project was PyNN, a simulator-independent language for building neuronal network models.

Scientists have great expectations that nanotechnologies will bring them closer to the goal of creating computer systems that can simulate and emulate the brain’s abilities for sensation, perception, action, interaction and cognition while rivaling its low power consumption and compact size – basically a brain-on-a-chip. Already, scientists are working hard on laying the foundations for what is called neuromorphic engineering – a new interdisciplinary discipline that includes nanotechnologies and whose goal is to design artificial neural systems with physical architectures similar to biological nervous systems.

Several research projects funded with millions of dollars are at work with the goal of developing brain-inspired computer architectures or virtual brains: DARPA’s SyNAPSE, the EU’s BrainScaleS (a successor to FACETS), or the Blue Brain project (one of the predecessors of the Human Brain Project) at Switzerland’s EPFL [École Polytechnique Fédérale de Lausanne].

Berger goes on to describe the raison d’être for neuromorphic engineering (attempts to mimic biological brains),

Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications – but useful and practical implementations do not yet exist.

Researchers are mostly interested in emulating neural plasticity (aka synaptic plasticity), from Berger’s April 4, 2014 article,

Independent from military-inspired research like DARPA’s, nanotechnology researchers in France have developed a hybrid nanoparticle-organic transistor that can mimic the main functionalities of a synapse. This organic transistor, based on pentacene and gold nanoparticles and termed NOMFET (Nanoparticle Organic Memory Field-Effect Transistor), has opened the way to new generations of neuro-inspired computers, capable of responding in a manner similar to the nervous system  (read more: “Scientists use nanotechnology to try building computers modeled after the brain”).

One of the key components of any neuromorphic effort, and its starting point, is the design of artificial synapses. Synapses dominate the architecture of the brain and are responsible for massive parallelism, structural plasticity, and robustness of the brain. They are also crucial to biological computations that underlie perception and learning. Therefore, a compact nanoelectronic device emulating the functions and plasticity of biological synapses will be the most important building block of brain-inspired computational systems.

In 2011, a team at Stanford University demonstrates a new single element nanoscale device, based on the successfully commercialized phase change material technology, emulating the functionality and the plasticity of biological synapses. In their work, the Stanford team demonstrated a single element electronic synapse with the capability of both the modulation of the time constant and the realization of the different synaptic plasticity forms while consuming picojoule level energy for its operation (read more: “Brain-inspired computing with nanoelectronic programmable synapses”).

Berger does mention memristors but not in any great detail in this article,

Researchers have also suggested that memristor devices are capable of emulating the biological synapses with properly designed CMOS neuron components. A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. It has the special property that its resistance can be programmed (resistor) and subsequently remains stored (memory).

One research project already demonstrated that a memristor can connect conventional circuits and support a process that is the basis for memory and learning in biological systems (read more: “Nanotechnology’s road to artificial brains”).

You can find a number of memristor articles here including these: Memristors have always been with us from June 14, 2013; How to use a memristor to create an artificial brain from Feb. 26, 2013; Electrochemistry of memristors in a critique of the 2008 discovery from Sept. 6, 2012; and many more (type ‘memristor’ into the blog search box and you should receive many postings or alternatively, you can try ‘artificial brains’ if you want everything I have on artificial brains).

Getting back to Berger’s April 4, 2014 article, he mentions one more approach and this one stands out,

A completely different – and revolutionary – human brain model has been designed by researchers in Japan who introduced the concept of a new class of computer which does not use any circuit or logic gate. This artificial brain-building project differs from all others in the world. It does not use logic-gate based computing within the framework of Turing. The decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.

Berger wrote about this work in much more detail in a Feb. 10, 2014 Nanowerk Spotlight article titled: Brain jelly – design and construction of an organic, brain-like computer, (Note: Links have been removed),

In a previous Nanowerk Spotlight we reported on the concept of a full-fledged massively parallel organic computer at the nanoscale that uses extremely low power (“Will brain-like evolutionary circuit lead to intelligent computers?”). In this work, the researchers created a process of circuit evolution similar to the human brain in an organic molecular layer. This was the first time that such a brain-like ‘evolutionary’ circuit had been realized.

The research team, led by Dr. Anirban Bandyopadhyay, a senior researcher at the Advanced Nano Characterization Center at the National Institute of Materials Science (NIMS) in Tsukuba, Japan, has now finalized their human brain model and introduced the concept of a new class of computer which does not use any circuit or logic gate.

In a new open-access paper published online on January 27, 2014, in Information (“Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System”), Bandyopadhyay and his team now describe the fundamental computing principle of a frequency fractal brain like computer.

“Our artificial brain-building project differs from all others in the world for several reasons,” Bandyopadhyay explains to Nanowerk. He lists the four major distinctions:
1) We do not use logic gate based computing within the framework of Turing, our decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.
2) We do not need to write any software, the argument and basic phase transition for decision-making, ‘if-then’ arguments and the transformation of one set of arguments into another self-assemble and expand spontaneously, the system holds an astronomically large number of ‘if’ arguments and its associative ‘then’ situations.
3) We use ‘spontaneous reply back’, via wireless communication using a unique resonance band coupling mode, not conventional antenna-receiver model, since fractal based non-radiative power management is used, the power expense is negligible.
4) We have carried out our own single DNA, single protein molecule and single brain microtubule neurophysiological study to develop our own Human brain model.

I encourage people to read Berger’s articles on this topic as they provide excellent information and links to much more. Curiously (mind you, it is easy to miss something), he does not mention James Gimzewski’s work at the University of California at Los Angeles (UCLA). Working with colleagues from the National Institute for Materials Science in Japan, Gimzewski published a paper about “two-, three-terminal WO3-x-based nanoionic devices capable of a broad range of neuromorphic and electrical functions”. You can find out more about the paper in my Dec. 24, 2012 posting titled: Synaptic electronics.

As for the ‘brain jelly’ paper, here’s a link to and a citation for it,

Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System by Subrata Ghoshemail, Krishna Aswaniemail, Surabhi Singhemail, Satyajit Sahuemail, Daisuke Fujitaemail and Anirban Bandyopadhyay. Information 2014, 5(1), 28-100; doi:10.3390/info5010028

It’s an open access paper.

As for anyone who’s curious about why the US BRAIN initiative ((Brain Research through Advancing Innovative Neurotechnologies, also referred to as the Brain Activity Map Project) is not mentioned, I believe that’s because it’s focussed on biological brains exclusively at this point (you can check its Wikipedia entry to confirm).

Anirban Bandyopadhyay was last mentioned here in a January 16, 2014 posting titled: Controversial theory of consciousness confirmed (maybe) in  the context of a presentation in Amsterdam, Netherlands.

Single-element quasicrystal created in laboratory for the first time

There’s a background story which gives this breakthrough a fabulous aspect but, first, here’s the research breakthrough from a Dec. 24, 2013 news item on Nanowerk (Note: A link has been removed),

A research group led by Assistant Professor Kazuki Nozawa and Professor Yasushi Ishii from the Department of Physics, Faculty of Science and Engineering, Chuo University, Chief Researcher Masahiko Shimoda from the National Institute for Materials Science (NIMS) and Professor An-Pang Tsai from the Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, succeeded for the first time in the world in fabricating a three-dimensional structure of a quasicrystal composed of a single element, through joint research with a group led by Dr. Hem Raj Sharma from the University of Liverpool, the United Kingdom.

The Dec. 2, 2013 National Institute for Materials Science (NIMS; Japan) press release, which originated the news item, describes quasicrystals and the reasons why this particular achievement is such a breakthrough,

Quasicrystals are substances discovered in 1984 by Dr. Dan Shechtman (who was awarded the Nobel Prize in Chemistry in 2011). [emphasis mine] To date, quasicrystals have been found in more than one hundred kinds of alloy, polymer and nanoparticle systems. However, a quasicrystal composed of a single element has not been found yet. Quasicrystals have a beautiful crystalline structure which is closely related to the golden ratio, called a quasiperiodic structure. This structure is made of a pentagonal or decagonal atomic arrangement that is not found in ordinary periodic crystals (see the reference illustrations). Due to the complexity of the crystalline structure and chemical composition, much about quasicrystals is still veiled in mystery, including the mechanism for stabilizing a quasiperiodic structure and the novel properties of this unique type of crystalline structure. For these reasons, efforts have been made for a long time in the quest for a chemically simple type of quasicrystal composed only of a single element. The joint research group has recently succeeded in growing a crystal of lead with a quasiperiodic structure which is modeled on the structure of a substrate quasicrystal, by vapor-depositing lead atoms on the quasicrystal substrate of an existing alloy made of silver (Ag), indium (In) and ytterbium (Yb). Success using this approach had been reported for fabricating a single-element quasiperiodic film consisting of a single atomic layer (two-dimensional structure), but there had been no successful case of fabricating a single-element quasiperiodic structure consisting of multiple atomic layers (three-dimensional structure). This recent success by the joint research group is a significant step forward toward achieving single-element quasicrystals. It is also expected to lead to advancement in various fields, such as finding properties unique to quasiperiodic structures that cannot be found in periodic crystals and elucidating the mechanism of stabilization of quasiperiodic structures.

Here’s an image illustrating the researchers’ achievement,

Illustrations of the deposition structure of lead. The Tsai cluster in the substrate quasicrystal which is near the surface of the substrate is cut at the point where it contacts the surface. While lead usually has a face-centered cubic structure, it is deposited on the quasicrystal substrate in a manner that it recovers Tsai clusters which are cut near the surface of the substrate. This indicates that a crystal of lead is grown with the same structure as the structure of the quasicrystal substrate. (Courtesy National Institute for Materials Science, Japan)

Illustrations of the deposition structure of lead. The Tsai cluster in the substrate quasicrystal which is near the surface of the substrate is cut at the point where it contacts the surface. While lead usually has a face-centered cubic structure, it is deposited on the quasicrystal substrate in a manner that it recovers Tsai clusters which are cut near the surface of the substrate. This indicates that a crystal of lead is grown with the same structure as the structure of the quasicrystal substrate. (Courtesy National Institute for Materials Science, Japan)

I suggested earlier that this achievement has a fabulous quality and the Daniel Schechtman backstory is the reason. The winner of the 2011 Nobel Prize for Chemistry, Schechtman was reviled for years within his scientific community as Ian Sample notes in his Oct. 5, 2011 article on the announcement of Schechtman’s Nobel win written for the Guardian newspaper (Note: A link has been removed),

A scientist whose work was so controversial he was ridiculed and asked to leave his research group has won the Nobel Prize in Chemistry.

Daniel Shechtman, 70, a researcher at Technion-Israel Institute of Technology in Haifa, received the award for discovering seemingly impossible crystal structures in frozen gobbets of metal that resembled the beautiful patterns seen in Islamic mosaics.

Images of the metals showed their atoms were arranged in a way that broke well-establised rules of how crystals formed, a finding that fundamentally altered how chemists view solid matter.

On the morning of 8 April 1982, Shechtman saw something quite different while gazing at electron microscope images of a rapidly cooled metal alloy. The atoms were packed in a pattern that could not be repeated. Shechtman said to himself in Hebrew, “Eyn chaya kazo,” which means “There can be no such creature.”

The bizarre structures are now known as “quasicrystals” and have been seen in a wide variety of materials. Their uneven structure means they do not have obvious cleavage planes, making them particularly hard.

In an interview this year with the Israeli newspaper, Haaretz, Shechtman said: “People just laughed at me.” He recalled how Linus Pauling, a colossus of science and a double Nobel laureate, mounted a frightening “crusade” against him. After telling Shechtman to go back and read a crystallography textbook, the head of his research group asked him to leave for “bringing disgrace” on the team. “I felt rejected,” Shachtman said.

It takes a lot to persevere when most, if not all, of your colleagues are mocking and rejecting your work so bravo to Schechtman! And,bravo to the Japan-UK project researchers who have persevered to help solve at least part of a complex problem requiring that our basic notions of matter be rethought.

I encourage you to read Sample’s article in its entirety as it is well written and I’ve excerpted only bits of the story as it relates to a point I’m making in this post, i.e., perseverance in the face of extreme resistance.