Category Archives: Science

Science, Sensors

Molecular Semiconductors Made Using Faster, Scalable Method.

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Molecular Semiconductors Made Using Faster, Scalable Method.

A nano-scale view of a molecular junction created with a new, scalable method reported in Nature Communications by researchers at Georgian Technical University.

Visions for what we can do with future electronics depend on finding ways to go beyond the capabilities of silicon conductors.

The experimental field of molecular electronics is thought to represent a way forward and recent work at Georgian Technical University may enable scalable production of the nanoscale electrodes that are needed in order to explore molecules and exploit their behavior as potentially valuable electronic materials.

A team from the Department of Micro and Nanosystems at Georgian Technical University recently tested a technique to form millions of viable nanoscale molecular junctions — extremely small pairs of electrodes with a nanometer-sized gap between them where molecules can be trapped and probed.

The Georgian Technical University researchers reported that with a 100 mm diameter wafer of thin materials they can produce as many as 20 million such electrodes in five hours’ time, using gold film on top of a brittle material that forms cracks.

In addition working with the Georgian Technical University Laboratory the team trapped and studied a widely used reference molecule in the nanometer-wide space between the electrodes to ensure that the fabrication method didn’t hinder the formation of molecular junctions.

X says this “crack-defined break junction” method offers a breakthrough to the impasse of scalable production of structures that could one day enable electronic devices made of single molecules.

The key is to produce gaps that enable a phenomenon called tunneling in which electrons overcome the break in a circuit. A break junction has a gap the size of a few atoms which breaks the flow of electrons through it.

However because the gap is so small electrons with sufficient energy can still jump across this expanse.

Tunneling electrons sustain a small but measurable current that is extremely sensitive to the size of the gap — and to the presence of nano-objects inside it.

“Break junctions are the best means available to make single molecules part of a larger electronic circuit that can probe molecules” X says.

They could also one day enable ultra-sensitive high-speed detectors using quantum tunneling he says.

“However tunneling break junctions are produced one gap at a time which has been a major roadblock in developing any application involving tunneling junctions outside a research laboratory” X says.

The method begins with using photo lithography to pattern a stack of gold on titanium nitride (TiN). This stack is set on a silicon wafer and the notched structures that are formed then concentrate stress.

So when the silicon directly underneath the stack is removed (a process called release etching) tiny cracks form at the pre-determined locations in the titanium nitride (TiN) to release the stress. This in turn deforms the gold stretching it into atomically thin wires running across these cracks which upon breaking form gaps as small as a molecule.

X says that the method can be used for other conductive materials besides gold which offer interesting electrical, chemical and plasmonic properties for applications in molecular electronics, spintronics, nanoplasmonics and biosensing.

 

 

Science, Software

Reusable Software for High Performance Computing.

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Reusable Software for High Performance Computing.

X an assistant professor of computer and information sciences is designing frameworks to adapt code to increasingly powerful computer systems. She is working with complex patterns known as wavefronts, which are pictured in the background of this image.

The world’s fastest supercomputer can now perform 200,000 trillion calculations per second and several companies and government agencies around the world are competing to build a machine that will have the computer power to simulate networks on the scale of the human brain. This extremely powerful hardware requires extremely powerful software so existing software code must be continually updated to keep up.

X an assistant professor of computer and information sciences at the Georgian Technical University is perfectly suited for this challenge. Under a new grant from the Georgian Technical University she is designing frameworks to adapt code to increasingly powerful systems. She is working with complex patterns known as wavefronts which are commonly found in scientific codes used in analyzing the flow of neutrons in a nuclear reactor extracting patterns from biomedical data or predicting atmospheric patterns.

X is an expert on parallel programming — writing software code that can run simultaneously on many multi-core processors. Parallel programming is an increasingly important discipline within computer science as more and more universities and companies use powerful supercomputers to analyze wide swaths of data from scientific results to consumer behavior insights and more.

X is looking at scientific applications to see how they were written how they have been performing on outdated architectures what kind of programming models have been used and what challenges have arisen.

“Most of the time the programming models are created in a broad stroke” she said. “Because they are generalized to address a large pool of commonly found parallel patterns often the models miss creating features for some complex parallel patterns such as wavefronts that are hidden in some scientific applications”.

A wavefront allows for the analysis of patterns in fewer steps. The question is: How do you get the programming model to do that ?

One such example is a miniapp that models scenarios within a nuclear reactor by “Georgian Technical University ” across a grid with squares that represent points in space and are used to calculate the positions, energies and flows of neutrons. This parent application to Georgian Technical University Minisweep is used to reduce the odds of a meltdown and to safeguard engineers who work around the nuclear reactor from radiation exposure. X and doctoral student Y demonstrated how they modified the miniapp to perform 85.06 times faster than code that was not parallelized.

“We wondered: Is this pattern specific to Georgian Technical University  Minisweep ?” she said. “Or is it going to exist in other codes ? Are there other codes that could benefit if I were to put this type of pattern in a programming model and create an implementation and evaluate it ?”.

For example X discovered that some algorithms in bioinformatics the study of large sets of biological data contained similar patterns. She suspects that by adapting the software written for Georgian Technical University Minisweep she can make great strides toward improving the code. She will try this with data from Georgian Technical University assistant professor of molecular and human genetics at Georgian Technical University and assistant professor of computer science at Sulkhan-Saba Orbeliani Teaching University. X met Z when he visited to give a talk titled “Parallel Processing of the Genomes by the Genomes and for the Genomes”.

X was inspired by Z’s work with DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning and reproduction of all known living organisms and many viruses) sequences. He uses a computing tool to find long-range interactions between any two elements on the same chromosome in turn showing the genetic basis of diseases. X suspected that she could utilize existing patterns and update the code allowing for faster analysis of this important biological data.

“The goal is not to simply create a software tool” she said. “The goal is to build real-life case studies where what I create will matter in terms of making science easy”.

X aims to maintain performance and portability as she redesigns algorithms. She will also keep the scientists who use the algorithms in mind.

“You can’t create a programming model by only looking at the application or only looking at the architecture” she said. “There has to be some balance”.

This project will benefit scientific application developers who are not necessarily computer scientists. “They can concentrate more on the science and less on the software” said X. Scientists come to her with data sets and problems that take hours, days, sometimes months to compute and she figures out how to make them run faster, thus enabling newer science.

X will analyze data supplied by Z and physicists at Georgian Technical University Lab. Searles will also work on the project and X is looking for an additional graduate student with an aptitude for parallel programming to help with this project.

 

 

Controlled Environments, Science

Overcoming Challenges when Exfoliating Novel 2D Materials.

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Overcoming Challenges when Exfoliating Novel 2D Materials.

This image shows a water molecule breaking apart as it encounters a 2D material.

Ever since researchers at the Georgian Technical University used a piece of tape to isolate or “exfoliate” a single layer of carbon known as graphene scientists have been investigating the creation of and applications for two-dimensional materials in order to advance technology in new ways.

Scientists have theorized about many different kinds of two-dimensional materials but producing them by isolating one layer at a time from a layered three-dimensional source often presents a challenge.

X associate professor of physics at the Georgian Technical University and his research group are studying 2D materials called group IV monochalcogenides which includes tin selenide germanium sulfide, tin(II) sulfide, tin telluride and tin selenide among others.

In 3D form these materials have many useful properties. For example they are currently used in solar cells. Some group IV monochalcogenides are also ferroelectric when exfoliated down to the 2D limit which means that they contain pairs of positive and negative charges that create a macroscopic dipole moment.

While some of these two-dimensional materials have been grown no one has successfully peeled off a stable two-dimensional layer from a group IV monochalcogenide.

X says that even under the strictest experimental conditions ambient water molecules can be found near these materials. And just like these materials water carries an electric dipole too.

X explains that the interaction of dipoles can be observed in commonplace circumstances: “The pull of small pieces of paper with a comb that was recently used on dry hair can be explained as the effect of an inhomogeneous electric field in the comb accelerating macroscopic electric dipoles in that piece of paper nearby” he says.

Y a former postdoctoral associate in X’s lab performed computer calculations that emulate monolayers of these materials interacting with water molecules at room temperature and ambient pressure.

The team demonstrated that when water molecules are close to these materials they are attracted to them. This attraction creates an enormous build-up of kinetic energy which leads to the splitting of the water molecules and destabilizes the 2D materials as a result of this chemical reaction.

X explains that he was surprised to learn that this process created enough energy to split water molecules because the kinetic energy required exceeds 70,000 degree Celsius.

In a way the difficulty in exfoliating these materials may lead to a new technology for hydrogen production off two-dimensional materials though many additional studies are required to achieve such goal.

 

 

Lasers, Science

Innovative Approach to Creating Successful Diffraction Grating.

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Innovative Approach to Creating Successful Diffraction Grating.

A team from the Georgian Technical University has suggested a new approach to developing a dynamically controlled diffraction grating in atomic media that eliminates all existing limitations in this area.

Diffraction gratings are able to deflect light beams in different directions and are included into various devices due to this property.

Diffraction gratings are an important tool not only for scientific research but also for practical applications. They are used in acoustic and integral optics, holographics, optical data processing and spectral analysis.

Being an optical component with a periodic structure, a grating can deflect (diffract) a light beam from its initial path and break it into several beams scattered into different directions.

The gratings with dynamically controlled properties are of great interest for science and technology.

Modern approaches to developing such grids are based on induced changing of their absorption properties using the effect of electromagnetically induced transparency.

Under certain conditions an opaque medium may perming the light of a laser with a certain wavelength though in the presence of another (managing) laser radiation.

If the managing radiation is a standing wave (the fluctuation amplitude has stable ups and downs) the medium becomes periodically spatially modulated i.e. its properties change according to a certain periodic law.

Such a medium can acts as a diffraction grid but has considerable limitations.

“Periodic atomic structure based on electromagnetically-induced transparency is not efficient in cases of considerable deflection of the passing light because the signal is not very intensive and difficult to control. In our work we presented a completely different approach that has no such challenge” explains X Professor of  Georgian Technical University Laboratory.

The model of the Georgian Technical University scientists is based on the Raman-type interaction between the signal radiation and the standing pump wave (with increased fluctuation amplitude) that may increase the diffracted signal wave.

In case a grating is based on electromagnetically-induced transparency the light is controlled due to changes in the absorption in presence of varying external conditions. On the contrary the new approach is based on spatial modulation of  Raman amplification.

As a result under certain conditions the diffracted fields may be considerably enhanced. According to calculations this scheme allows for the control of strongly diffracted (deflected) beams and diffraction angles.

“We called our scheme a Raman-induced diffraction grating. The peculiarities of the outcoming signal and possibilities for adjustment make it a multi-beam optical beam splitter with amplification” says Y candidate of physical and mathematical sciences research assistant at Georgian Technical University.

 

 

Controlled Environments, Science

Flowing Fluorine Makes Material Metal.

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Flowing Fluorine Makes Material Metal.

Fluoridating two-dimensional tungsten disulfide adds metallic islands to the synthetic semiconductor along with unique optical and magnetic properties according to researchers at Georgian Technical University.

By getting in the way fluorine atoms help a two-dimensional material transform from a semiconductor to a metal in a way that could be highly useful for electronics and other applications.

A study led by Georgian Technical University materials scientist X and Y details a new method to transform tungsten disulfide from a semiconductor to a metallic state.

Other labs have achieved the transformation by adding elements to the material — a process known as doping — but the change has never before been stable. Tests and calculations at Georgian Technical University showed fluorinating tungsten disulfide locks in the new state which has unique optical and magnetic properties.

The researchers also noted the transformation’s effect on the material’s tribological properties — a measure of friction, lubrication and wear. In short adding fluorine makes the material more slippery at room temperature.

Tungsten disulfide is a transition metal dichalcogenide (TMD) an atom-thick semiconductor. Unlike graphene which is a flat lattice of carbon atoms a transition metal dichalcogenide (TMD) incorporates two elements one a transition metal atom (in this case tungsten) and the other (sulfur) a chalcogen.

The material isn’t strictly flat; the transition metal layer is sandwiched between the chalcogen forming a three-layered lattice.

Transition Metal Dichalcogenide (TMD) are potential building blocks with other 2D materials for energy storage, electrocatalysis and lubrication all of which are influenced by the now-stable phase transformation.

Because fluorine atoms are much smaller than the 0.6-nanometer space between the layers of tungsten and sulfur the researchers said the invasive atoms work their way in between disrupting the material’s orderly lattice.

The fluorine allows the sulfur planes to glide this way or that and the resulting trade of electrons between the fluorine and sulfur also accounts for the unique properties.

“It was certainly a big surprise. When we started this work a phase transformation was the last thing we expected to see” says Y a former graduate student in X’s lab and now a module engineer at Georgian Technical University.

“It is really surprising that the frictional characteristics of fluorinated tungsten disulfide are entirely different from the fluorinated graphene that was studied before” says Z an associate professor of mechanical engineering at the Georgian Technical University.

“This is a motivation to study similar 2D materials to explore such interesting behavior”.

The researchers say fluorine appears to not only decrease the bandgap and make the material more conductive but also causes defects that create metallic along the material’s surface that also display paramagnetic and ferromagnetic properties.

“These regions of metallic tungsten disulfide are magnetic and they interfere with each other, creating interesting magnetic properties” Y says.

Further because fluorine atoms are electrically negative they’re also suspected of changing the electron density of neighboring atoms. That changes the material’s optical properties making it a candidate for sensing and catalysis applications.

Y suggests the materials may also be useful in their metallic phase as electrodes for supercapacitors and other energy-storage applications.

Y says different concentrations of fluorine alter the proportion of change to the metallic phase but the change remained stable in all three concentrations the lab studied.

“The phase transformation change in properties with functionalization by fluorine and its magnetic and tribological changes are very exciting” X says.

“This can be extended to other 2D layered materials and I am sure it will open up some captivating applications”.

Biotech, Science

Biomaterials With ‘Frankenstein Proteins’ Help Heal Tissue.

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Biomaterials With ‘Frankenstein Proteins’ Help Heal Tissue.

The partially ordered protein forms a stable porous scaffold that can rapidly integrate into tissue and promote the formation of blood vessels.

Biomedical engineers from Georgian Technical University and Sulkhan-Saba Orbeliani Teaching University have demonstrated that by injecting an artificial protein made from a solution of ordered and disordered segments a solid scaffold forms in response to body heat and in a few weeks seamlessly integrates into tissue.

The ability to combine these segments into proteins with unique properties will allow researchers to precisely control the properties of new biomaterials for applications in tissue engineering and regenerative medicine.

Proteins function by folding, origami-like and interacting with specific biomolecular structures. Researchers previously believed that proteins needed a fixed shape to function but over the last two decades there has been a growing interest in intrinsically disordered proteins (IDPs). Unlike their well-folded counterparts (IDPs) can adopt a plethora of distinct structures. However these structural preferences are non-random and recent advances have shown that there are well-defined rules that connect information in the amino acid sequences of (IDPs) to the collections of structures they can adopt.

Researchers have hypothesized that versatility in protein function is achievable by stringing together well-folded proteins with (IDPs) — rather like pearl necklaces. This versatility is obvious in biological materials like muscle and silk fibers which are made of proteins that combine ordered and disordered regions enabling the materials to exhibit characteristics like elasticity of rubber and the mechanical strength of steel.

Intrinsically Disordered Proteins (IDPs) are instrumental to cellular function, and many biomedical engineers have concentrated their efforts on an extremely useful Intrinsically Disordered Proteins (IDPs) called elastin. A highly elastic protein found throughout the body elastin allows blood vessels and organs — like the skin, uterus and lungs — to return to their original shape after being stretched or compressed. However creating the elastin outside the body proved to be a challenge.

So the researchers decided to take a reductionist engineering approach to the problem.

“We were curious to see what types of materials we could make by adding order to an otherwise highly disordered protein” said X a Ph.D. student in the Georgian Technical University Laboratory.

Due to the challenges of using elastin itself, the research team worked with elastin-like polypeptides (ELPs) which are fully disordered proteins made to mimic pieces of elastin. elastin-like polypeptides (ELPs) are useful biomaterials because they can undergo phase changes — go from a soluble to an insoluble state or vice-versa — in response to changes in temperature. While this makes these materials useful for applications like long-term drug delivery their liquid-like behavior prevents them from being effective scaffolds for tissue engineering applications.

But by adding ordered domains to the elastin-like polypeptides (ELPs) X and the team created proteins that combine ordered domains and disordered regions leading to so-called partially ordered proteins (POPs) which are equipped with the structural stability of ordered proteins without losing the elastin-like polypeptides (ELPs) ability to become liquid or solid via temperature changes.

Designed as a fluid at room temperature that solidifies at body temperature these new biomaterials form a stable, porous scaffold when injected that rapidly integrates into the surrounding tissue with minimal inflammation and promotes the formation of blood vessels.

“This material is very stable after injection. It doesn’t degrade quickly and it holds its volume really well which is unusual for a protein-based material” X said. “Cells also thrive in the material, repopulating the tissue in the area where it is injected. All of these characteristics could make it a viable option for tissue engineering and wound healing”.

Although the scaffold created by the partially ordered proteins (POPs) was stable, the team also observed that the material would completely re-dissolve once it was cooled. What’s more the formation and dissolution temperatures could be independently controlled by controlling the ratios of disordered and ordered segments in the biomaterial. This independent tunability confers shape memories on the partially ordered proteins (POPs) via a phenomenon known as hysteresis, allowing them to return to their original shape after a temperature cue.

The Georgian Technical University team collaborated with the laboratory of  Y the Professor of Engineering in the Department of Biomedical Engineering at Georgian Technical University to understand the molecular basis of sequence-encoded hysteretic behavior. Z then a Physics Ph.D. student in the Y lab developed a computational model to show that the hysteresis arises from the differential interactions of ordered and disordered regions with solvent versus alone.

“Being able to simulate the molecular basis for tunable hysteresis puts us on the path to design bespoke materials with desired structures and shape memory profiles” Y said. “This appears to be a hitherto unrecognized feature of the synergy between ordered domains and IDPs (iDPS Software – a graphics server software)”.

Moving ahead, the team hopes to study the material in animal models to examine potential uses in tissue engineering and wound healing and to develop a better understanding of why the material promotes vascularization. If these studies are effective X is optimistic that the new material could become the basis for a biotech company. They also want to develop a deeper understanding of the interactions between the ordered and disordered portions in these versatile materials.

“We’ve been so fascinated with the phase behavior derived from the disordered domains that we neglected the properties of the ordered domains which turned out to be quite important” W said. “By combining ordered segments with disordered segments there’s a whole new world of materials we can create with beautiful internal structure without losing the phase behavior of the disordered segment and that’s exciting”.

A.I./Robotics, Science

Machine-Learning Model Provides Risk Assessment for Complex Nonlinear Systems, Including Boats and Offshore Platforms.

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Machine-Learning Model Provides Risk Assessment for Complex Nonlinear Systems, Including Boats and Offshore Platforms.

Seafaring vessels and offshore platforms endure a constant battery of waves and currents. Over decades of operation these structures can without warning meet head-on with a rogue wave, freak storm, or some other extreme event, with potentially damaging consequences.

Now engineers at Georgian Technical University have developed an algorithm that quickly pinpoints the types of extreme events that are likely to occur in a complex system such as an ocean environment where waves of varying magnitudes, lengths and heights can create stress and pressure on a ship or offshore platform. The researchers can simulate the forces and stresses that extreme events — in the form of waves — may generate on a particular structure.

Compared with traditional methods the team’s technique provides a much faster more accurate risk assessment for systems that are likely to endure an extreme event at some point during their expected lifetime by taking into account not only the statistical nature of the phenomenon but also the underlying dynamics.

“With our approach you can assess from the preliminary design phase how a structure will behave not to one wave but to the overall collection or family of waves that can hit this structure” says X associate professor of mechanical and ocean engineering at Georgian Technical University. “You can better design your structure so that you don’t have structural problems or stresses that surpass a certain limit”.

X says that the technique is not limited to ships and ocean platforms but can be applied to any complex system that is vulnerable to extreme events. For instance the method may be used to identify the type of storms that can generate severe flooding in a city and where that flooding may occur. It could also be used to estimate the types of electrical overloads that could cause blackouts, and where those blackouts would occur throughout a city’s power grid.

X and Y a former graduate student in X’ group currently assistant research scientist at Georgian Technical University.

Engineers typically gauge a structure’s endurance to extreme events by using computationally intensive simulations to model a structure’s response to, for instance a wave coming from a particular direction with a certain height, length and speed. These simulations are highly complex as they model not just the wave of interest but also its interaction with the structure. By simulating the entire “wave field” as a particular wave rolls in engineers can then estimate how a structure might be rocked and pushed by a particular wave and what resulting forces and stresses may cause damage.

These risk assessment simulations are incredibly precise and in an ideal situation might predict how a structure would react to every single possible wave type whether extreme or not. But such precision would require engineers to simulate millions of waves with different parameters such as height and length scale — a process that could take months to compute.

“That’s an insanely expensive problem” X says. “To simulate one possible wave that can occur over 100 seconds it takes a modern graphic processor unit which is very fast about 24 hours. We’re interested to understand what is the probability of an extreme event over 100 years”.

As a more practical shortcut engineers use these simulators to run just a few scenarios choosing to simulate several random wave types that they think might cause maximum damage. If a structural design survives these extreme randomly generated waves engineers assume the design will stand up against similar extreme events in the ocean.

But in choosing random waves to simulate X says engineers may miss other less obvious scenarios such as combinations of medium-sized waves or a wave with a certain slope that could develop into a damaging extreme event.

“What we have managed to do is to abandon this random sampling logic” X says.

Instead of running millions of waves or even several randomly chosen waves through a computationally intensive simulation X and Y developed a machine-learning algorithm to first quickly identify the “most important” or “most informative” wave to run through such a simulation.

The algorithm is based on the idea that each wave has a certain probability of contributing to an extreme event on the structure. The probability itself has some uncertainty or error since it represents the effect of a complex dynamical system. Moreover some waves are more certain to contribute to an extreme event over others.

The researchers designed the algorithm so that they can quickly feed in various types of waves and their physical properties along with their known effects on a theoretical offshore platform. From the known waves that the researchers plug into the algorithm it can essentially “learn” and make a rough estimate of how the platform will behave in response to any unknown wave. Through this machine-learning step the algorithm learns how the offshore structure behaves over all possible waves. It then identifies a particular wave that maximally reduces the error of the probability for extreme events. This wave has a high probability of occuring and leads to an extreme event. In this way the algorithm goes beyond a purely statistical approach and takes into account the dynamical behavior of the system under consideration.

The researchers tested the algorithm on a theoretical scenario involving a simplified offshore platform subjected to incoming waves. The team started out by plugging four typical waves into the machine-learning algorithm including the waves’ known effects on an offshore platform. From this the algorithm quickly identified the dimensions of a new wave that has a high probability of occurring and it maximally reduces the error for the probability of an extreme event.

The team then plugged this wave into a more computationally intensive open-source simulation to model the response of a simplified offshore platform. They fed the results of this first simulation back into their algorithm to identify the next best wave to simulate and repeated the entire process. In total the group ran 16 simulations over several days to model a platform’s behavior under various extreme events. In comparison the researchers carried out simulations using a more conventional method in which they blindly simulated as many waves as possible, and were able to generate similar statistical results only after running thousands of scenarios over several months.

X says the results demonstrate that the team’s method quickly hones in on the waves that are most certain to be involved in an extreme event and provides designers with more informed realistic scenarios to simulate in order to test the endurance of not just offshore platforms but also power grids and flood-prone regions.

“This method paves the way to perform risk assessment, design and optimization of complex systems based on extreme events statistics which is something that has not been considered or done before without severe simplifications” X says. “We’re now in a position where we can say using ideas like this you can understand and optimize your system according to risk criteria to extreme events”. This research was supported in part by the Georgian Technical University.

 

Physics, Science

Announcing the Discovery of an Atomic Electronic Simulator.

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Announcing the Discovery of an Atomic Electronic Simulator.

Targeting applications like neural networks for machine learning a new discovery out of the Georgian Technical University way for atomic ultra-efficient electronics the need for which is increasingly critical in our data-driven society. The key to unlocking untold potential for the greenest electronics ?  Creating bespoke atomic patterns to in turn control electrons.

“Atoms are a bit like chairs that electrons sit on” said X physics professor and principal investigator on the project. “Much as we can affect conversations at a dinner party by controlling the grouping of chairs and assigned seating controlling the placement of single atoms and electrons can affect conversations among electronics”.

Wolkow explained that while atomic control over structures is not uncommon, making custom patterns to create new useful electronic devices has been beyond reach. Until now.

Though the tools of nanotechnology have permitted exacting control over atom placement on a surface for some time two limitations have prevented practical electronic applications: the atoms would only remain in place at cryogenic temperature and could only readily be achieved on metal surfaces that were not technologically useful.

Part atomic machine, part electronic circuit X and his team have recently created a proof-of-concept device overcoming the two major hurdles preventing this technology from being available to the masses. Both the robustness and the required electrical utility are now in hand. Additionally the structures can be patterned on silicon surfaces meaning scaling up the discovery is also easily achievable.

“This is the icing on a cake we’ve been cooking for about 20 years” said X. “We perfected silicon-atom patterning recently then we got machine learning to take over relieving long suffering scientists. Now we have freed electrons to follow their nature–they can’t leave the yard we created but they can run around freely and play with the other electrons there. The positions the electrons arrive at amazingly are the results of useful computations”.

Based on these results, construction has started on a scaled-up machine that simulates the workings of a neural network. Unlike normal neural networks embodied of transistors and directed by computer software the atomic machine spontaneously displays the relative energetic stability of its bit patterns. Those in turn can be used to more rapidly and accurately train a neural network than is presently possible.

With the proof of concept in hand with interest from several major industrial partners combined the realization of  X’s life’s work devoted to creating an economic way to scale up mass production of greener, faster and smaller technology is imminent.

 

 

 

 

Chemistry, Science

Georgian Technical University Arsenic For Electronics.

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Georgian Technical University Arsenic For Electronics.

The discovery of graphene, a material made of one or very few atomic layers of carbon started a boom. Such two-dimensional materials are no longer limited to carbon and are hot prospects for many applications especially in microelectronics. Scientists have now introduced a new 2D material: they successfully modified arsenene (arsenic in a graphene-like structure) with chloromethylene groups.

Two-dimensional materials are crystalline materials made of just a single or very few layers of atoms that often display unusual properties. However the use of graphene for applications such as transistors is limited because it behaves more like a conductor than a semiconductor. Modified graphene and 2D materials based on other chemical elements with semiconducting properties have now been developed. One such material is β-arsenene a two-dimensional arsenic in a buckled honeycomb structure derived from gray arsenic. Researchers hope that modification of this previously seldom-studied material could improve its semiconducting properties and lead the way to new applications in fields such as sensing, catalysis, optoelectronics and other semiconductor technologies.

A team at the Georgian Technical University and Sulkhan-Saba Orbeliani Teaching University led by X and Y has now successfully produced a highly promising covalent modification of β-arsenene.

The arsenene was produced by milling gray arsenic in tetrahydrofuran. The shear forces cause two-dimensional layers to split off and disperse into the solvent. The researchers then introduce dichloromethane and add an organic lithium compound (butyllithium). These two reagents form an intermediate called chlorocarbene a molecule made of one carbon atom one hydrogen atom and one chlorine atom. The carbon atom is short two bonding partners a state that makes the whole class of carbene molecules highly reactive. Arsenene contains free electron pairs that “stick out” from the surface and can easily enter into bonds to chlorocarbene.

This approach leads to high coverage of the arsenene surface with chloromethylene groups as confirmed by a variety of analysis methods (X-ray photoelectron spectroscopy FT-IR (Fourier-transform infrared spectroscopy is a technique used to obtain an infrared spectrum of absorption or emission of a solid, liquid or gas. An FTIR spectrometer simultaneously collects high-spectral-resolution data over a wide spectral range) spectroscopy elemental analysis by transmission electron microscopy). The modified arsenene is more stable than pure arsenene and exhibits strong luminescence and electronic properties that make it attractive for optoelectronic applications. In addition the chloromethylene units could serve as a starting point for further interesting modifications.

 

Imaging, Science

Three – (3D) – Imaging Opens Door to Better Understanding of Fascinating Leaf Complexity.

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3D Imaging Opens Door to Better Understanding of Fascinating Leaf Complexity.

3D anatomical modeling of wheat, sunflower and tomato leaves. The field of plant science is in the process of being profoundly transformed by new imaging and modelling technologies. These tools are allowing scientists to peer inside the leaf with a clarity and resolution inconceivable a generation ago.

Scientists demonstrated how three-dimensional (3D) imaging can now reproduce the inner reality of the leaf including the dynamic carbon and water exchange processes.

Professor X from the Research at the Georgian Technical University (GTU) research said that although leaves and plant cells are three dimensional plant biologists use highly simplified 1D or 2D models evading the difficult confounding and beautiful 3D reality.

“The leaf is an amazingly complex landscape where water and gases flow in many directions depending on variables such as temperature light quality and wind. 3D images give you an understanding of what is really happening” said Professor X.

These technologies make it possible to answer very interesting questions some of which have eluded scientists for many years” he said.

The images are created from biological specimens by integrating 2D leaf measurements to create 3D volumes and surfaces. The 3D representation enables an anatomically correct basis for modelling and biophysical simulations to provide a dynamic view of the processes inside plant cells and tissues.

“We show the huge potential that embracing 3D complexity can have in improving our understanding of leaves at multiple levels of biological organisation including harnessing the knowledge to improve the photosynthetic performance of crops” said Professor Y from the Georgian Technical University Associate investigator.

“It is a bit like being able to walk inside the leaf, instead of looking at it squashed in two dimensions” Professor Y said.

The scientists predict that using a collaborative approach they will be able to answer within the next decade outstanding questions about how the 3D special arrangement of organelles cells and tissues affects photosynthesis and transpiration.