Tag Archives: Australia

The Australians talk about wood and nanotechnology

It’s a bit of a mystery but somehow a wood product from Australia is nanotechnology-enabled. The company is RT Holdings (apparently no website) and the speaker, Albert Golier, is the chairman of the board for the company (since April 2015). According to the interview on the Breakfast with Stuart Stansfield programme for 891 ABC (Australian Broadcasting Corporation) Adelaide, the idea for the product was inspired by bamboo, which is woven and glued together to create flooring products. Golier whose previous experience is in the field of electronics was surprised (and somewhat horrified) to learn that only about 30% of a tree is actually used after processing, the rest being waste. The first part of the July 14, 2015 interview was posted here. The second part (July 15, 2015) is here. The third and final part (July 16, 2015) of the interview is here.

I have found some company information for RT Holdings, it was officially registered in 2014 according to allcompanydata.com. There’s also this 2014 RT Holdings slide deck on the Forest & Wood Products of Australia website.

SINGLE (3D Structure Identification of Nanoparticles by Graphene Liquid Cell Electron Microscopy) and the 3D structures of two individual platinum nanoparticles in solution

It seems to me there’s been an explosion of new imaging techniques lately. This one from the Lawrence Berkelely National Laboratory is all about imaging colloidal nanoparticles (nanoparticles in solution), from a July 20, 2015 news item on Azonano,

Just as proteins are one of the basic building blocks of biology, nanoparticles can serve as the basic building blocks for next generation materials. In keeping with this parallel between biology and nanotechnology, a proven technique for determining the three dimensional structures of individual proteins has been adapted to determine the 3D structures of individual nanoparticles in solution.

A multi-institutional team of researchers led by the U.S. Department of Energy (DOE)’s Lawrence Berkeley National Laboratory (Berkeley Lab), has developed a new technique called “SINGLE” that provides the first atomic-scale images of colloidal nanoparticles. SINGLE, which stands for 3D Structure Identification of Nanoparticles by Graphene Liquid Cell Electron Microscopy, has been used to separately reconstruct the 3D structures of two individual platinum nanoparticles in solution.

A July 16, 2015 Berkeley Lab news release, which originated the news item, reveals more details about the reason for the research and the research itself,

“Understanding structural details of colloidal nanoparticles is required to bridge our knowledge about their synthesis, growth mechanisms, and physical properties to facilitate their application to renewable energy, catalysis and a great many other fields,” says Berkeley Lab director and renowned nanoscience authority Paul Alivisatos, who led this research. “Whereas most structural studies of colloidal nanoparticles are performed in a vacuum after crystal growth is complete, our SINGLE method allows us to determine their 3D structure in a solution, an important step to improving the design of nanoparticles for catalysis and energy research applications.”

Alivisatos, who also holds the Samsung Distinguished Chair in Nanoscience and Nanotechnology at the University of California Berkeley, and directs the Kavli Energy NanoScience Institute at Berkeley (Kavli ENSI), is the corresponding author of a paper detailing this research in the journal Science. The paper is titled “3D Structure of Individual Nanocrystals in Solution by Electron Microscopy.” The lead co-authors are Jungwon Park of Harvard University, Hans Elmlund of Australia’s Monash University, and Peter Ercius of Berkeley Lab. Other co-authors are Jong Min Yuk, David Limmer, Qian Chen, Kwanpyo Kim, Sang Hoon Han, David Weitz and Alex Zettl.

Colloidal nanoparticles are clusters of hundreds to thousands of atoms suspended in a solution whose collective chemical and physical properties are determined by the size and shape of the individual nanoparticles. Imaging techniques that are routinely used to analyze the 3D structure of individual crystals in a material can’t be applied to suspended nanomaterials because individual particles in a solution are not static. The functionality of proteins are also determined by their size and shape, and scientists who wanted to image 3D protein structures faced a similar problem. The protein imaging problem was solved by a technique called “single-particle cryo-electron microscopy,” in which tens of thousands of 2D transmission electron microscope (TEM) images of identical copies of an individual protein or protein complex frozen in random orientations are recorded then computationally combined into high-resolution 3D reconstructions. Alivisatos and his colleagues utilized this concept to create their SINGLE technique.

“In materials science, we cannot assume the nanoparticles in a solution are all identical so we needed to develop a hybrid approach for reconstructing the 3D structures of individual nanoparticles,” says co-lead author of the Science paper Peter Ercius, a staff scientist with the National Center for Electron Microscopy (NCEM) at the Molecular Foundry, a DOE Office of Science User Facility.

“SINGLE represents a combination of three technological advancements from TEM imaging in biological and materials science,” Ercius says. “These three advancements are the development of a graphene liquid cell that allows TEM imaging of nanoparticles rotating freely in solution, direct electron detectors that can produce movies with millisecond frame-to-frame time resolution of the rotating nanocrystals, and a theory for ab initio single particle 3D reconstruction.”

The graphene liquid cell (GLC) that helped make this study possible was also developed at Berkeley Lab under the leadership of Alivisatos and co-author Zettl, a physicist who also holds joint appointments with Berkeley Lab, UC Berkeley and Kavli ENSI. TEM imaging uses a beam of electrons rather than light for illumination and magnification but can only be used in a high vacuum because molecules in the air disrupt the electron beam. Since liquids evaporate in high vacuum, samples in solutions must be hermetically sealed in special solid containers – called cells – with a very thin viewing window before being imaged with TEM. In the past, liquid cells featured silicon-based viewing windows whose thickness limited resolution and perturbed the natural state of the sample materials. The GLC developed at Berkeley lab features a viewing window made from a graphene sheet that is only a single atom thick.

“The GLC provides us with an ultra-thin covering of our nanoparticles while maintaining liquid conditions in the TEM vacuum,” Ercius says. “Since the graphene surface of the GLC is inert, it does not adsorb or otherwise perturb the natural state of our nanoparticles.”

Working at NCEM’s TEAM I, the world’s most powerful electron microscope, Ercius, Alivisatos and their colleagues were able to image in situ the translational and rotational motions of individual nanoparticles of platinum that were less than two nanometers in diameter. Platinum nanoparticles were chosen because of their high electron scattering strength and because their detailed atomic structure is important for catalysis.

“Our earlier GLC studies of platinum nanocrystals showed that they grow by aggregation, resulting in complex structures that are not possible to determine by any previously developed method,” Ercius says. “Since SINGLE derives its 3D structures from images of individual nanoparticles rotating freely in solution, it enables the analysis of heterogeneous populations of potentially unordered nanoparticles that are synthesized in solution, thereby providing a means to understand the structure and stability of defects at the nanoscale.”

The next step for SINGLE is to recover a full 3D atomic resolution density map of colloidal nanoparticles using a more advanced camera installed on TEAM I that can provide 400 frames-per-second and better image quality.

“We plan to image defects in nanoparticles made from different materials, core shell particles, and also alloys made of two different atomic species,” Ercius says. [emphasis mine]

“Two different atomic species?”, they really are pushing that biology analogy.

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

3D structure of individual nanocrystals in solution by electron microscopy by Jungwon Park, Hans Elmlund, Peter Ercius, Jong Min Yuk, David T. Limme, Qian Chen, Kwanpyo Kim, Sang Hoon Han, David A. Weitz, A. Zettl, A. Paul Alivisatos. Science 17 July 2015: Vol. 349 no. 6245 pp. 290-295 DOI: 10.1126/science.aab1343

This paper is behind a paywall.

Australia and regulation of nanotechnology re: agriculture, animal husbandry, pesticides, and veterinary medicines

The Australian Pesticides and Veterinary Medicines Authority (APVMA) has release a final report with recommendations regarding nanotechnology regulation according to a July 13, 2015 news item on Nanowerk (Note: A link has been removed),

Publication of the report Nanotechnologies for pesticides and veterinary medicines: regulatory considerations—final report (July 2015) marks the culmination of four years of Australian Pesticides and Veterinary Medicines Authority (APVMA)-led research, consultation and collaboration.

The report considers the benefits and challenges of regulating nanotechnology for use in agriculture and animal husbandry, as advances in nanoscale science, engineering and technology pave the way for developing novel applications, devices and systems.

The report aims to inform and stimulate discussion about emerging nanotechnology and highlights the key regulatory considerations for agvet chemical nanomaterials based on the current state of knowledge.

It systematically explores the opportunities and risks of these substances in Australian agriculture and animal husbandry and reviews the published work relevant to the registration of nanoscale agvet chemicals.

A July 6, 2015 APVMA press release, which originated the news item, provides a brief history of the deliberations which led to the report and a brief description of the actions which will follow its publication,

In October 2014, the APVMA hosted a symposium on nanotechnology regulation, seeking national and international input from industry, scientists, regulators and the broader community on developing a regulatory framework for nanotechnologies in Australian agriculture and animal husbandry. Discussion was based on the APVMA draft report Regulatory considerations for nanopesticides and veterinary medicines (October 2014), the first of its kind to be made available for public discussion. Input subsequently received was considered in finalising the report.

Next steps

The APVMA will now use the report to finalise the regulatory approach for nanotechnology products, including:

building capability and expertise so new products can be evaluated effectively
analysing the data requirements
enhancing the existing regulatory framework if required as knowledge evolves
continuing to engage with the international scientific community so that the latest

You can find the final report here.

Kill bacterial biofilms and activate healing with cinnamon and peppermint

These compounds based on peppermint and cinnamon kill infection (bacterial biofilm) while helping the wound to heal according to a July 8, 2015 news item on ScienceDaily,

Infectious colonies of bacteria called biofilms that develop on chronic wounds and medical devices can cause serious health problems and are tough to treat. But now scientists have found a way to package antimicrobial compounds from peppermint and cinnamon in tiny capsules that can both kill biofilms and actively promote healing. The researchers say the new material could be used as a topical antibacterial treatment and disinfectant.

A July 8, 2015 American Chemical Society news release on EurekAlert, which originated the news item, provides more detail,

Many bacteria clump together in sticky plaques in a way that makes them difficult to eliminate with traditional antibiotics. Doctors sometimes recommend cutting out infected tissues. This approach is costly, however, and because it’s invasive, many patients opt out of treatment altogether. Essential oils and other natural compounds have emerged recently as alternative substances that can get rid of pathogenic bacteria, but researchers have had a hard time translating their antibacterial activity into treatments. Vincent M. Rotello and colleagues wanted to address this challenge.

The researchers packaged peppermint oil and cinnamaldehyde, the compound in cinnamon responsible for its flavor and aroma, into silica nanoparticles. The microcapsule treatment was effective against four different types of bacteria, including one antibiotic-resistant strain. It also promoted the growth of fibroblasts, a cell type that is important in wound healing.

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

Nanoparticle-Stabilized Capsules for the Treatment of Bacterial Biofilms by Bradley Duncan, Xiaoning Li, Ryan F. Landis, Sung Tae Kim, Akash Gupta, Li-Sheng Wang, Rajesh Ramanathan, Rui Tang, Jeffrey A. Boerth, and Vincent M. Rotello. ACS Nano, Article ASAP
DOI: 10.1021/acsnano.5b01696 Publication Date (Web): June 17, 2015

Copyright © 2015 American Chemical Society

This paper is behind a paywall.

Crowd computing for improved nanotechnology-enabled water filtration

This research is the product of a China/Israel/Switzerland collaboration on water filtration with involvement from the UK and Australia. Here’s some general information about the importance of water and about the collaboration in a July 5, 2015 news item on Nanowerk (Note: A link has been removed),

Nearly 800 million people worldwide don’t have access to safe drinking water, and some 2.5 billion people live in precariously unsanitary conditions, according to the Centers for Disease Control and Prevention. Together, unsafe drinking water and the inadequate supply of water for hygiene purposes contribute to almost 90% of all deaths from diarrheal diseases — and effective water sanitation interventions are still challenging scientists and engineers.

A new study published in Nature Nanotechnology (“Water transport inside carbon nanotubes mediated by phonon-induced oscillating friction”) proposes a novel nanotechnology-based strategy to improve water filtration. The research project involves the minute vibrations of carbon nanotubes called “phonons,” which greatly enhance the diffusion of water through sanitation filters. The project was the joint effort of a Tsinghua University-Tel Aviv University research team and was led by Prof. Quanshui Zheng of the Tsinghua Center for Nano and Micro Mechanics and Prof. Michael Urbakh of the TAU School of Chemistry, both of the TAU-Tsinghua XIN Center, in collaboration with Prof. Francois Grey of the University of Geneva.

A July 5, 2015 American Friends of Tel Aviv University news release (also on EurekAlert), which originated the news item, provides more details about the work,

“We’ve discovered that very small vibrations help materials, whether wet or dry, slide more smoothly past each other,” said Prof. Urbakh. “Through phonon oscillations — vibrations of water-carrying nanotubes — water transport can be enhanced, and sanitation and desalination improved. Water filtration systems require a lot of energy due to friction at the nano-level. With these oscillations, however, we witnessed three times the efficiency of water transport, and, of course, a great deal of energy saved.”

The research team managed to demonstrate how, under the right conditions, such vibrations produce a 300% improvement in the rate of water diffusion by using computers to simulate the flow of water molecules flowing through nanotubes. The results have important implications for desalination processes and energy conservation, e.g. improving the energy efficiency for desalination using reverse osmosis membranes with pores at the nanoscale level, or energy conservation, e.g. membranes with boron nitride nanotubes.

Crowdsourcing the solution

The project, initiated by IBM’s World Community Grid, was an experiment in crowdsourced computing — carried out by over 150,000 volunteers who contributed their own computing power to the research.

“Our project won the privilege of using IBM’s world community grid, an open platform of users from all around the world, to run our program and obtain precise results,” said Prof. Urbakh. “This was the first project of this kind in Israel, and we could never have managed with just four students in the lab. We would have required the equivalent of nearly 40,000 years of processing power on a single computer. Instead we had the benefit of some 150,000 computing volunteers from all around the world, who downloaded and ran the project on their laptops and desktop computers.

“Crowdsourced computing is playing an increasingly major role in scientific breakthroughs,” Prof. Urbakh continued. “As our research shows, the range of questions that can benefit from public participation is growing all the time.”

The computer simulations were designed by Ming Ma, who graduated from Tsinghua University and is doing his postdoctoral research in Prof. Urbakh’s group at TAU. Ming catalyzed the international collaboration. “The students from Tsinghua are remarkable. The project represents the very positive cooperation between the two universities, which is taking place at XIN and because of XIN,” said Prof. Urbakh.

Other partners in this international project include researchers at the London Centre for Nanotechnology of University College London; the University of Geneva; the University of Sydney and Monash University in Australia; and the Xi’an Jiaotong University in China. The researchers are currently in discussions with companies interested in harnessing the oscillation knowhow for various commercial projects.

 

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

Water transport inside carbon nanotubes mediated by phonon-induced oscillating friction by Ming Ma, François Grey, Luming Shen, Michael Urbakh, Shuai Wu,    Jefferson Zhe Liu, Yilun Liu, & Quanshui Zheng. Nature Nanotechnology (2015) doi:10.1038/nnano.2015.134 Published online 06 July 2015

This paper is behind a paywall.

Final comment, I find it surprising that they used labour and computing power from 150,000 volunteers and didn’t offer open access to the paper. Perhaps the volunteers got their own copy? I certainly hope so.

Is it time to invest in a ‘brain chip’ company?

This story take a few twists and turns. First, ‘brain chips’ as they’re sometimes called would allow, theoretically, computers to learn and function like human brains. (Note: There’s another type of ‘brain chip’ which could be implanted in human brains to help deal with diseases such as Parkinson’s and Alzheimer’s. *Today’s [June 26, 2015] earlier posting about an artificial neuron points at some of the work being done in this areas.*)

Returning to the ‘brain ship’ at hand. Second, there’s a company called BrainChip, which has one patent and another pending for, yes, a ‘brain chip’.

The company, BrainChip, founded in Australia and now headquartered in California’s Silicon Valley, recently sparked some investor interest in Australia. From an April 7, 2015 article by Timna Jacks for the Australian Financial Review,

Former mining stock Aziana Limited has whet Australian investors’ appetite for science fiction, with its share price jumping 125 per cent since it announced it was acquiring a US-based tech company called BrainChip, which promises artificial intelligence through a microchip that replicates the neural system of the human brain.

Shares in the company closed at 9¢ before the Easter long weekend, having been priced at just 4¢ when the backdoor listing of BrainChip was announced to the market on March 18.

Creator of the patented digital chip, Peter Van Der Made told The Australian Financial Review the technology has the capacity to learn autonomously, due to its composition of 10,000 biomimic neurons, which, through a process known as synaptic time-dependent plasticity, can form memories and associations in the same way as a biological brain. He said it works 5000 times faster and uses a thousandth of the power of the fastest computers available today.

Mr Van Der Made is inviting technology partners to license the technology for their own chips and products, and is donating the technology to university laboratories in the US for research.

The Netherlands-born Australian, now based in southern California, was inspired to create the brain-like chip in 2004, after working at the IBM Internet Security Systems for two years, where he was chief scientist for behaviour analysis security systems. …

A June 23, 2015 article by Tony Malkovic on phys.org provide a few more details about BrainChip and about the deal,

Mr Van der Made and the company, also called BrainChip, are now based in Silicon Valley in California and he returned to Perth last month as part of the company’s recent merger and listing on the Australian Stock Exchange.

He says BrainChip has the ability to learn autonomously, evolve and associate information and respond to stimuli like a brain.

Mr Van der Made says the company’s chip technology is more than 5,000 faster than other technologies, yet uses only 1/1,000th of the power.

“It’s a hardware only solution, there is no software to slow things down,” he says.

“It doesn’t executes instructions, it learns and supplies what it has learnt to new information.

“BrainChip is on the road to position itself at the forefront of artificial intelligence,” he says.

“We have a clear advantage, at least 10 years, over anybody else in the market, that includes IBM.”

BrainChip is aiming at the global semiconductor market involving almost anything that involves a microprocessor.

You can find out more about the company, BrainChip here. The site does have a little more information about the technology,

Spiking Neuron Adaptive Processor (SNAP)

BrainChip’s inventor, Peter van der Made, has created an exciting new Spiking Neural Networking technology that has the ability to learn autonomously, evolve and associate information just like the human brain. The technology is developed as a digital design containing a configurable “sea of biomimic neurons’.

The technology is fast, completely digital, and consumes very low power, making it feasible to integrate large networks into portable battery-operated products, something that has never been possible before.

BrainChip neurons autonomously learn through a process known as STDP (Synaptic Time Dependent Plasticity). BrainChip’s fully digital neurons process input spikes directly in hardware. Sensory neurons convert physical stimuli into spikes. Learning occurs when the input is intense, or repeating through feedback and this is directly correlated to the way the brain learns.

Computing Artificial Neural Networks (ANNs)

The brain consists of specialized nerve cells that communicate with one another. Each such nerve cell is called a Neuron,. The inputs are memory nodes called synapses. When the neuron associates information, it produces a ‘spike’ or a ‘spike train’. Each spike is a pulse that triggers a value in the next synapse. Synapses store values, similar to the way a computer stores numbers. In combination, these values determine the function of the neural network. Synapses acquire values through learning.

In Artificial Neural Networks (ANNs) this complex function is generally simplified to a static summation and compare function, which severely limits computational power. BrainChip has redefined how neural networks work, replicating the behaviour of the brain. BrainChip’s artificial neurons are completely digital, biologically realistic resulting in increased computational power, high speed and extremely low power consumption.

The Problem with Artificial Neural Networks

Standard ANNs, running on computer hardware are processed sequentially; the processor runs a program that defines the neural network. This consumes considerable time and because these neurons are processed sequentially, all this delayed time adds up resulting in a significant linear decline in network performance with size.

BrainChip neurons are all mapped in parallel. Therefore the performance of the network is not dependent on the size of the network providing a clear speed advantage. So because there is no decline in performance with network size, learning also takes place in parallel within each synapse, making STDP learning very fast.

A hardware solution

BrainChip’s digital neural technology is the only custom hardware solution that is capable of STDP learning. The hardware requires no coding and has no software as it evolves learning through experience and user direction.

The BrainChip neuron is unique in that it is completely digital, behaves asynchronously like an analog neuron, and has a higher level of biological realism. It is more sophisticated than software neural models and is many orders of magnitude faster. The BrainChip neuron consists entirely of binary logic gates with no traditional CPU core. Hence, there are no ‘programming’ steps. Learning and training takes the place of programming and coding. Like of a child learning a task for the first time.

Software ‘neurons’, to compromise for limited processing power, are simplified to a point where they do not resemble any of the features of a biological neuron. This is due to the sequential nature of computers, whereby all data has to pass through a central processor in chunks of 16, 32 or 64 bits. In contrast, the brain’s network is parallel and processes the equivalent of millions of data bits simultaneously.

A significantly faster technology

Performing emulation in digital hardware has distinct advantages over software. As software is processed sequentially, one instruction at a time, Software Neural Networks perform slower with increasing size. Parallel hardware does not have this problem and maintains the same speed no matter how large the network is. Another advantage of hardware is that it is more power efficient by several orders of magnitude.

The speed of the BrainChip device is unparalleled in the industry.

For large neural networks a GPU (Graphics Processing Unit) is ~70 times faster than the Intel i7 executing a similar size neural network. The BrainChip neural network is faster still and takes far fewer CPU (Central Processing Unit) cycles, with just a little communication overhead, which means that the CPU is available for other tasks. The BrainChip network also responds much faster than a software network accelerating the performance of the entire system.

The BrainChip network is completely parallel, with no sequential dependencies. This means that the network does not slow down with increasing size.

Endorsed by the neuroscience community

A number of the world’s pre-eminent neuroscientists have endorsed the technology and are agreeing to joint develop projects.

BrainChip has the potential to become the de facto standard for all autonomous learning technology and computer products.

Patented

BrainChip’s autonomous learning technology patent was granted on the 21st September 2008 (Patent number US 8,250,011 “Autonomous learning dynamic artificial neural computing device and brain inspired system”). BrainChip is the only company in the world to have achieved autonomous learning in a network of Digital Neurons without any software.

A prototype Spiking Neuron Adaptive Processor was designed as a ‘proof of concept’ chip.

The first tests were completed at the end of 2007 and this design was used as the foundation for the US patent application which was filed in 2008. BrainChip has also applied for a continuation-in-part patent filed in 2012, the “Method and System for creating Dynamic Neural Function Libraries”, US Patent Application 13/461,800 which is pending.

Van der Made doesn’t seem to have published any papers on this work and the description of the technology provided on the website is frustratingly vague. There are many acronyms for processes but no mention of what this hardware might be. For example, is it based on a memristor or some kind of atomic ionic switch or something else altogether?

It would be interesting to find out more but, presumably, van der Made, wishes to withhold details. There are many companies following the same strategy while pursuing what they view as a business advantage.

* Artificial neuron link added June 26, 2015 at 1017 hours PST.

Nanoscale imaging gets rough

Smooth is easier than rough when imaging at the nanoscale according to a June 17, 2015 Northwestern University news release by Megan Fellman (also on EurekAlert),

A multi-institutional team of scientists has taken an important step in understanding where atoms are located on the surfaces of rough materials, information that could be very useful in diverse commercial applications, such as developing green energy and understanding how materials rust.

Researchers from Northwestern University, Brookhaven National Laboratory, Lawrence Berkeley National Laboratory and the University of Melbourne, Australia, have developed a new imaging technique that uses atomic resolution secondary electron images in a quantitative way to determine the arrangement of atoms on the surface.

Many important processes take place at surfaces, ranging from the catalysis used to generate energy-dense fuels from sunlight and carbon dioxide to how bridges and airplanes corrode, or rust. Every material interacts with the world through its surface, which is often different in both structure and chemistry from the bulk of the material.

The real focus of the work is on corrosion, according to the news release,

“We are excited by the possibilities of applying our imaging technique to corrosion and catalysis problems,” said Laurence Marks, a co-author of the paper and a professor of materials science and engineering at Northwestern’s McCormick School of Engineering and Applied Science. “The cost of corrosion to industry and the military is enormous, and we do not understand everything that is taking place. We must learn more, so we can produce materials that will last longer.”

To understand these processes and improve material performance, it is vital to know how the atoms are arranged on surfaces. While there are many good methods for obtaining this information for rather flat surfaces, most currently available tools are limited in what they can reveal when the surfaces are rough.

Scanning electron microscopes are widely used to produce images of many different materials, and roughness of the surface is not that important. Until very recently, instruments could not obtain clear atomic images of surfaces until a group at Brookhaven managed in 2011 to get the first images that seemed to show the surfaces very clearly. However, it was not clear to what extent they really were able to image the surface, as there was no theory for the imaging and many uncertainties.

The new work has answered all these questions, Marks said, providing a definitive way of understanding the surfaces in detail. What was needed was to use a carefully controlled sample of strontium titanate and perform a large range of different types of imaging to unravel the precise details of how secondary electron images are produced.

“We started this work by investigating a well-studied material,” said Jim Ciston, a staff scientist at Lawrence Berkeley National Laboratory and the lead author of the paper, who obtained the experimental images. “This new technique is so powerful that we had to revise much of what was already thought to be well-known. This is an exciting prospect because the surface of every material can act as its own nanomaterial coating, which can greatly change the chemistry and behavior.”

“The beauty of the technique is that we can image surface atoms and bulk atoms simultaneously,” said Yimei Zhu, a scientist at Brookhaven National Laboratory. “Currently, no existing methods can achieve that.”

Les Allen, who led the theoretical and modeling aspects of the new imaging technique in Melbourne, said, “We now have a sophisticated understanding of what the images mean. It now will be full steam ahead to apply them to many different types of problems.”

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

Surface determination through atomically resolved secondary-electron imaging by J. Ciston, H. G. Brown, A. J. D’Alfonso, P. Koirala, C. Ophus, Y. Lin, Y. Suzuki, H. Inada, Y. Zhu, L. J. Allen, & L. D. Marks. Nature Communications 6, Article number: 7358 doi:10.1038/ncomms8358 Published 17 June 2015

This paper is open access.

Animation: art and science

Being in the process of developing an art/science piece involving poetry and visual metaphors as realized through video, I was quite fascinated to read about someone else’s process and issues in Stephen Curry’s and Drew Berry’s June 9, 2015 joint post on the Guardian science blogs (Note: Links have been removed),

Yesterday [June 8, 2015] I [Stephen Curry] was trying to figure out why it seems to be so difficult to connect to the biological molecules that we are made of – proteins, DNA and such like. My piece might have ended on a frustrated note but I have no wish to be negative, especially since the problem has only arisen because animators like Drew Berry are now able to use the results of structural biology to make quite exquisite movies of the molecules of life at work inside the cells of our bodies. As I was working though my difficulties, I wrote to ask Berry how he approached the task of representing molecular complexity in ways that would make sense to people. This is his considered and insightful reply:

“The goal of my [Drew Berry] work is to show non-experts – the general public aged 4 to 99, students of biology, journalists and politicians, and so on – what is being discovered in biology, in a format that is accessible, meaningful, and engaging. I hope that my work provides some sense of what biologists and medical researchers are discovering and thinking about, to provide the public with a framework of understanding to discuss these important new discoveries and the impact it will have on us as a society as we head into the future.

These passages, in particular, caught my attention as they are descriptive of the art and the science inherent in Berry’s work,

… I should avoid overstating how accurately I have depicted the reality of the molecular world. It is vastly messier, random and crowded, and it’s physical nature is unimaginably alien to our normal perception of the world around us. That said, my work is not intended to be a lab-bench-calculated model for research use, it is an impressionistic, artist-generated crude sketch of phenomena and structures science is measuring and discovering at the molecular scale.

… I would then assert that the animations are firmly founded on real data and are as accurate as I can possibly make them, while making them watchable and interpretable to a human audience. By far the largest portion of my time is spent conducting broad ranging literature reviews of the topic I am working on, gathering the fragments of data scattered throughout the journals, and holistically reconstructing what currently we know and do not know. Wherever data and models are available, I incorporate them directly into the construction of the animation, including molecular structures, dynamics simulations, speed measurements, and so on. My work is most akin to a ‘review’ paper in the literature, presented in visual form.

Here is one of the problems Berry and other animators struggle with,

… I am friends with the dozen or so people who are at the top of the game at creating biomedical animations (most have a PhD scientific background) and we all struggle with the problem of having a molecule arrive at a particular location from the thick molecular soup of the cytoplasm and not look directed. I can make the molecule wander around in a Brownian type manner, but for story telling and visual explanations, I need it to get to a certain point and do it’s thing at a certain time to move the story along. This can make it look determined and directed.

Berry also discusses the unexpected,

An unexpected outcome I stumbled across more than a decade ago is that the public loves it when ‘real time’ speeds are displayed and the structures and reactions are derived from research data. This takes a lot of time to build, but then the animations have a remarkable longevity of use and strongly resonate with the audience.

For the last excerpt from this essay, I include Berry’s description of one of his most challenging projects and the video he produced,

The most heavily researched and technically challenging animation I have ever built is the kinetochore which can be seen in the video below . The kinetochore is a gigantic structure that assembles on chromosomes just after they have been duplicated and helps them to be pulled apart during cell division (mitosis). It has about 200 proteins of which I depicted about 50. I gathered data from more than 180 scientific papers with everything built as accurately as possible with hundreds of little scientific details built into the structure and dynamics.”

There are more illustrations and one more video embedded along with more from Berry in the essay, which includes these biographical details (Note: Links have been removed),

Drew Berry is the Biomedical Animations Manager at the Walter and Eliza Hall Institute of Medical Research in Melbourne, Australia. @Stephen_Curry is a professor of structural biology at Imperial College [London, UK].

Memristor, memristor, you are popular

Regular readers know I have a long-standing interest in memristor and artificial brains. I have three memristor-related pieces of research,  published in the last month or so, for this post.

First, there’s some research into nano memory at RMIT University, Australia, and the University of California at Santa Barbara (UC Santa Barbara). From a May 12, 2015 news item on ScienceDaily,

RMIT University researchers have mimicked the way the human brain processes information with the development of an electronic long-term memory cell.

Researchers at the MicroNano Research Facility (MNRF) have built the one of the world’s first electronic multi-state memory cell which mirrors the brain’s ability to simultaneously process and store multiple strands of information.

The development brings them closer to imitating key electronic aspects of the human brain — a vital step towards creating a bionic brain — which could help unlock successful treatments for common neurological conditions such as Alzheimer’s and Parkinson’s diseases.

A May 11, 2015 RMIT University news release, which originated the news item, reveals more about the researchers’ excitement and about the research,

“This is the closest we have come to creating a brain-like system with memory that learns and stores analog information and is quick at retrieving this stored information,” Dr Sharath said.

“The human brain is an extremely complex analog computer… its evolution is based on its previous experiences, and up until now this functionality has not been able to be adequately reproduced with digital technology.”

The ability to create highly dense and ultra-fast analog memory cells paves the way for imitating highly sophisticated biological neural networks, he said.

The research builds on RMIT’s previous discovery where ultra-fast nano-scale memories were developed using a functional oxide material in the form of an ultra-thin film – 10,000 times thinner than a human hair.

Dr Hussein Nili, lead author of the study, said: “This new discovery is significant as it allows the multi-state cell to store and process information in the very same way that the brain does.

“Think of an old camera which could only take pictures in black and white. The same analogy applies here, rather than just black and white memories we now have memories in full color with shade, light and texture, it is a major step.”

While these new devices are able to store much more information than conventional digital memories (which store just 0s and 1s), it is their brain-like ability to remember and retain previous information that is exciting.

“We have now introduced controlled faults or defects in the oxide material along with the addition of metallic atoms, which unleashes the full potential of the ‘memristive’ effect – where the memory element’s behaviour is dependent on its past experiences,” Dr Nili said.

Nano-scale memories are precursors to the storage components of the complex artificial intelligence network needed to develop a bionic brain.

Dr Nili said the research had myriad practical applications including the potential for scientists to replicate the human brain outside of the body.

“If you could replicate a brain outside the body, it would minimise ethical issues involved in treating and experimenting on the brain which can lead to better understanding of neurological conditions,” Dr Nili said.

The research, supported by the Australian Research Council, was conducted in collaboration with the University of California Santa Barbara.

Here’s a link to and a citation for this memristive nano device,

Donor-Induced Performance Tuning of Amorphous SrTiO3 Memristive Nanodevices: Multistate Resistive Switching and Mechanical Tunability by  Hussein Nili, Sumeet Walia, Ahmad Esmaielzadeh Kandjani, Rajesh Ramanathan, Philipp Gutruf, Taimur Ahmed, Sivacarendran Balendhran, Vipul Bansal, Dmitri B. Strukov, Omid Kavehei, Madhu Bhaskaran, and Sharath Sriram. Advanced Functional Materials DOI: 10.1002/adfm.201501019 Article first published online: 14 APR 2015

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

This paper is behind a paywall.

The second published piece of memristor-related research comes from a UC Santa Barbara and  Stony Brook University (New York state) team but is being publicized by UC Santa Barbara. From a May 11, 2015 news item on Nanowerk (Note: A link has been removed),

In what marks a significant step forward for artificial intelligence, researchers at UC Santa Barbara have demonstrated the functionality of a simple artificial neural circuit (Nature, “Training and operation of an integrated neuromorphic network based on metal-oxide memristors”). For the first time, a circuit of about 100 artificial synapses was proved to perform a simple version of a typical human task: image classification.

A May 11, 2015 UC Santa Barbara news release (also on EurekAlert)by Sonia Fernandez, which originated the news item, situates this development within the ‘artificial brain’ effort while describing it in more detail (Note: A link has been removed),

“It’s a small, but important step,” said Dmitri Strukov, a professor of electrical and computer engineering. With time and further progress, the circuitry may eventually be expanded and scaled to approach something like the human brain’s, which has 1015 (one quadrillion) synaptic connections.

For all its errors and potential for faultiness, the human brain remains a model of computational power and efficiency for engineers like Strukov and his colleagues, Mirko Prezioso, Farnood Merrikh-Bayat, Brian Hoskins and Gina Adam. That’s because the brain can accomplish certain functions in a fraction of a second what computers would require far more time and energy to perform.

… As you read this, your brain is making countless split-second decisions about the letters and symbols you see, classifying their shapes and relative positions to each other and deriving different levels of meaning through many channels of context, in as little time as it takes you to scan over this print. Change the font, or even the orientation of the letters, and it’s likely you would still be able to read this and derive the same meaning.

In the researchers’ demonstration, the circuit implementing the rudimentary artificial neural network was able to successfully classify three letters (“z”, “v” and “n”) by their images, each letter stylized in different ways or saturated with “noise”. In a process similar to how we humans pick our friends out from a crowd, or find the right key from a ring of similar keys, the simple neural circuitry was able to correctly classify the simple images.

“While the circuit was very small compared to practical networks, it is big enough to prove the concept of practicality,” said Merrikh-Bayat. According to Gina Adam, as interest grows in the technology, so will research momentum.

“And, as more solutions to the technological challenges are proposed the technology will be able to make it to the market sooner,” she said.

Key to this technology is the memristor (a combination of “memory” and “resistor”), an electronic component whose resistance changes depending on the direction of the flow of the electrical charge. Unlike conventional transistors, which rely on the drift and diffusion of electrons and their holes through semiconducting material, memristor operation is based on ionic movement, similar to the way human neural cells generate neural electrical signals.

“The memory state is stored as a specific concentration profile of defects that can be moved back and forth within the memristor,” said Strukov. The ionic memory mechanism brings several advantages over purely electron-based memories, which makes it very attractive for artificial neural network implementation, he added.

“For example, many different configurations of ionic profiles result in a continuum of memory states and hence analog memory functionality,” he said. “Ions are also much heavier than electrons and do not tunnel easily, which permits aggressive scaling of memristors without sacrificing analog properties.”

This is where analog memory trumps digital memory: In order to create the same human brain-type functionality with conventional technology, the resulting device would have to be enormous — loaded with multitudes of transistors that would require far more energy.

“Classical computers will always find an ineluctable limit to efficient brain-like computation in their very architecture,” said lead researcher Prezioso. “This memristor-based technology relies on a completely different way inspired by biological brain to carry on computation.”

To be able to approach functionality of the human brain, however, many more memristors would be required to build more complex neural networks to do the same kinds of things we can do with barely any effort and energy, such as identify different versions of the same thing or infer the presence or identity of an object not based on the object itself but on other things in a scene.

Potential applications already exist for this emerging technology, such as medical imaging, the improvement of navigation systems or even for searches based on images rather than on text. The energy-efficient compact circuitry the researchers are striving to create would also go a long way toward creating the kind of high-performance computers and memory storage devices users will continue to seek long after the proliferation of digital transistors predicted by Moore’s Law becomes too unwieldy for conventional electronics.

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

Training and operation of an integrated neuromorphic network based on metal-oxide memristors by M. Prezioso, F. Merrikh-Bayat, B. D. Hoskins, G. C. Adam, K. K. Likharev,    & D. B. Strukov. Nature 521, 61–64 (07 May 2015) doi:10.1038/nature14441

This paper is behind a paywall but a free preview is available through ReadCube Access.

The third and last piece of research, which is from Rice University, hasn’t received any publicity yet, unusual given Rice’s very active communications/media department. Here’s a link to and a citation for their memristor paper,

2D materials: Memristor goes two-dimensional by Jiangtan Yuan & Jun Lou. Nature Nanotechnology 10, 389–390 (2015) doi:10.1038/nnano.2015.94 Published online 07 May 2015

This paper is behind a paywall but a free preview is available through ReadCube Access.

Dexter Johnson has written up the RMIT research (his May 14, 2015 post on the Nanoclast blog on the IEEE [Institute of Electrical and Electronics Engineers] website). He linked it to research from Mark Hersam’s team at Northwestern University (my April 10, 2015 posting) on creating a three-terminal memristor enabling its use in complex electronics systems. Dexter strongly hints in his headline that these developments could lead to bionic brains.

For those who’d like more memristor information, this June 26, 2014 posting which brings together some developments at the University of Michigan and information about developments in the industrial sector is my suggestion for a starting point. Also, you may want to check out my material on HP Labs, especially prominent in the story due to the company’s 2008 ‘discovery’ of the memristor, described on a page in my Nanotech Mysteries wiki, and the controversy triggered by the company’s terminology (there’s more about the controversy in my April 7, 2010 interview with Forrest H Bennett III).

Changing the vibration of gold nanodisks (acoustic tuning) with light

A May 7, 2015 news item on phys.org describes research that could have a major impact on photonics applications,

In a study that could open doors for new applications of photonics from molecular sensing to wireless communications, Rice University [Texas, US] scientists have discovered a new method to tune the light-induced vibrations of nanoparticles through slight alterations to the surface to which the particles are attached.

n a study published online this week in Nature Communications, researchers at Rice’s Laboratory for Nanophotonics (LANP) used ultrafast laser pulses to induce the atoms in gold nanodisks to vibrate. These vibrational patterns, known as acoustic phonons, have a characteristic frequency that relates directly to the size of the nanoparticle. The researchers found they could fine-tune the acoustic response of the particle by varying the thickness of the material to which the nanodisks were attached.

A May 7, 2015 Rice University news release (also on EurekAlert), which originated the news item, expands on the theme (Note: A link has been removed),

Our results point toward a straightforward method for tuning the acoustic phonon frequency of a nanostructure in the gigahertz range by controlling the thickness of its adhesion layer,” said lead researcher Stephan Link, associate professor of chemistry and in electrical and computer engineering.

Light has no mass, but each photon that strikes an object imparts a miniscule amount of mechanical motion, thanks to a phenomenon known as radiation pressure. A branch of physics known as optomechanics has developed over the past decade to study and exploit radiation pressure for applications like gravity wave detection and low-temperature generation.

Link and colleagues at LANP specialize in another branch of science called plasmonics that is devoted to the study of light-activated nanostructures. Plasmons are waves of electrons that flow like a fluid across a metallic surface.

When a light pulse of a specific wavelength strikes a metal particle like the puck-shaped gold nanodisks in the LANP experiments, the light energy is converted into plasmons. These plasmons slosh across the surface of the particle with a characteristic frequency, in much the same way that each phonon has a characteristic vibrational frequency.

The study’s first author, Wei-Shun Chang, a postdoctoral researcher in Link’s lab, and graduate students Fangfang Wen and Man-Nung Su conducted a series of experiments that revealed a direct connection between the resonant frequencies of the plasmons and phonons in nanodisks that had been exposed to laser pulses.

“Heating nanostructures with a short light pulse launches acoustic phonons that depend sensitively on the structure’s dimensions,” Link said. “Thanks to advanced lithographic techniques, experimentalists can engineer plasmonic nanostructures with great precision. Based on our results, it appears that plasmonic nanostructures may present an interesting alternative to conventional optomechanical oscillators.”

Chang said plasmonics experts often rely on substrates when using electron-beam lithography to pattern plasmonic structures. For example, gold nanodisks like those used in the experiments will not stick to glass slides. But if a thin substrate of titanium or chromium is added to the glass, the disks will adhere and stay where they are placed.

“The substrate layer affects the mechanical properties of the nanostructure, but many questions remain as to how it does this,” Chang said. “Our experiments explored how the thickness of the substrate impacted properties like adhesion and phononic frequency.”

Link said the research was a collaborative effort involving research groups at Rice and the University of Melbourne in Victoria, Australia.

“Wei-Shun and Man-Nung from my lab did the ultrafast spectroscopy,” Link said. “Fangfang, who is in Naomi Halas’ group here at Rice, made the nanodisks. John Sader at the University of Melbourne, and his postdoc Debadi Chakraborty calculated the acoustic modes, and Yue Zhang, a Rice graduate student from Peter Nordlander’s group at Rice simulated the optical/plasmonic properties. Bo Shuang of the Landes’ research group at Rice contributed to the analysis of the experimental data.”

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

Tuning the acoustic frequency of a gold nanodisk through its adhesion layer by Wei-Shun Chang, Fangfang Wen, Debadi Chakraborty, Man-Nung Su, Yue Zhang, Bo Shuang, Peter Nordlander, John E. Sader, Naomi J. Halas, & Stephan Link. Nature Communications 6, Article number: 7022 doi:10.1038/ncomms8022 Published 05 May 2015

This paper is behind a paywall but a free preview is available vie ReadCube Access.