Category Archives: Science

Nanotechnology, Science

Georgian Technical University Defects Help Nanomaterial Quickly Soak Up Pollutant.

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Georgian Technical University Defects Help Nanomaterial Quickly Soak Up Pollutant.

By introducing defects into the structure of a metal-organic framework Georgian Technical University researchers found they could increase the amount of toxic pollutants called perfluorooctanesulfonic acid (PFOS) that could hold, as well as the speed with which it could adsorb them from heavily polluted industrial wastewater. Cleaning pollutants from water with a defective filter sounds like a non-starter but a recent study by chemical engineers at Georgian Technical University found that the right-sized defects helped a molecular sieve soak up more perfluorooctanesulfonic acid (PFOS) in less time. Georgian Technical University researchers X, Y and colleagues showed that a highly porous Georgian Technical University cheese-like nanomaterial called a metal-organic framework (MOF) was faster at soaking up from polluted water and that it could hold more PFOS when additional nanometer-sized holes (“Georgian Technical University defects”) were built into the metal-organic framework (MOF). Perfluorooctanesulfonic acid (PFOS) was used for decades in consumer products like stain-resistant fabrics and is the best-known member of a family of toxic chemicals called “per- and polyfluoroalkyl substances” (PFAS) which the Environmental Protection Agency (EPA) describes as “very persistent in the environment and in the human body — meaning they don’t break down and they can accumulate over time”. X professor and chair of Georgian Technical University’s Department of Chemical and Biomolecular Engineering and a professor of chemistry said “We are taking a step in the right direction toward developing materials that can effectively treat industrial wastewaters in the parts-per-billion and parts-per-million level of total PFAS (polyfluoroalkyl substances) contamination which is very difficult to do using current technologies like granular activated carbon or activated sludge-based systems”. X said MOFs (metal-organic framework) three-dimensional structures that self-assemble when metal ions interact with organic molecules called linkers, seemed like good candidates for PFAS (perfluorooctanesulfonic acid) remediation because they are highly porous and have been used to absorb and hold significant amounts of specific target molecules in previous applications. Some MOFs (metal-organic framework) for example have a surface area larger than a football field per gram, and more than 20,000 kinds of MOFs (metal-organic framework) are documented. In addition chemists can tune MOF (metal-organic framework) properties — varying their structure, pore sizes and functions — by tinkering with the synthesis, or chemical recipe that produces them. Such was the case with Georgian Technical University’s PFAS (polyfluoroalkyl substances) sorbent. Clark a graduate student in X’s Catalysis and Nanomaterials Laboratory began with a well-characterized MOF (metal-organic framework) called UiO-66 and conducted dozens of experiments to see how various concentrations of hydrochloric acid changed the properties of the final product. She found she could introduce structural defects of various sizes with the method — like making with extra-big holes. “The large-pore defects are essentially their own sites for Perfluorooctanesulfonic acid (PFOS) adsorption via hydrophobic interactions” Y said. “They improve the adsorption behavior by increasing the space for the Perfluorooctanesulfonic acid (PFOS) molecules”. Clark tested variants of UiO-66 with different sizes and amounts of defects to determine which variety soaked up the most PFAS (polyfluoroalkyl substances) from heavily polluted water in the least amount of time. “We believe that introducing random, large-pore defects while simultaneously maintaining the majority of the porous structure played a large role in improving the adsorption capacity of the MOFs (metal-organic framework)” she said. “This also maintained the fast adsorption kinetics, which is very important for wastewater remediation applications where contact times are short”. X said the study’s focus on industrial concentrations of PFAS (polyfluoroalkyl substances) sets it apart from most previously published work, which has focused on cleaning polluted drinking water to meet the current federal standards of 70 parts per trillion. While treatment technologies like activated carbon and ion exchange resins can be effective for cleaning low-level concentrations of PFAS (polyfluoroalkyl substances) from drinking water they are far less effective for treating high-concentration industrial waste. Although PFAS (polyfluoroalkyl substances) use has been heavily restricted by Georgian Technical University the chemicals are still used in semiconductor manufacturing and chrome plating, where wastewater can contain as much as one gram of PFAS (polyfluoroalkyl substances) per liter of water or about 14 billion times the current limit for safe drinking water. “In general for carbon-based materials and ion-exchange resins, there is a trade-off between adsorption capacity and adsorption rate as you increase the pore size of the material” X said. “In other words the more PFAS (polyfluoroalkyl substances) a material can soak up and trap, the longer it takes to fill up. In addition carbon-based materials have been shown to be mostly ineffective at removing shorter-chain PFAS (polyfluoroalkyl substances) from wastewater. “We found that our material combines high-capacity and fast-adsorption kinetics and also is effective for both long- and short-chain perfluoroalkyl sulfonates” X said. X said it’s difficult to beat carbon-based materials in terms of cost because activated carbon has been a mainstay for environmental filtration for decades. “But it’s possible if MOFs (metal-organic framework) become produced on a large-enough scale” X said. “There are a few companies looking into commercial-scale production of UiO-66 which is one reason we chose to work with it in this study”.

 

A.I./Robotics, Science

Georgian Technical University Machine Learning Tracks Moving Cells.

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Georgian Technical University Machine Learning Tracks Moving Cells.

A software developed by the Micro/Bio/Nanofluidics Unit allows users to easily segment track and analyze the migration of label-free cells. The tool can be used as an all-in-one solution to quantify cell migration, or can be employed as three separate applications (ie for segmentation, tracking, and data analysis, respectively). Using the machine learning infrastructure known as a ‘Georgian Technical University neural network’ the system allows users to train it on different data sets and analyzes images as a simplified human brain would. Both developing babies and elderly adults share a common characteristic: the many cells making up their bodies are always on the move. As we humans commute to work cells migrate through the body to get their jobs done. Biologists have long struggled to quantify the movement and changing morphology of cells through time but now scientists at the Georgian Technical University (GTU) have devised an elegant tool to do just that. Using machine learning, the researchers designed a software to analyze microscopic snapshots of migrating cells. They named the software word that refers to tracing the outlines of objects as the innovative tool detects the changing outlines of individual cells.  In the womb a baby’s cells migrate to precise locations so that each arm, leg and organ grows in its proper place. Our immune cells race through the body to mend wounds after injury. Cancerous cells metastasize by traveling through the body spreading tumors to new tissues. To test the efficacy of new medicines drug developers track the movement of cells before and after treatment. The software finds applications in all these areas of study and more. “This is an all-in-one solution to get us from raw images to quantitative data on cell migration,” said X. Y is a graduate student and led by Prof. Z. “Our software is at least 100 times faster than manual methods which are currently the gold-standard for these types of experiments because computers are not yet powerful enough”. “We’re hoping this software can become quite useful for the scientific community” said Prof. Z principal investigator of the unit. “For any biological study or drug screening that requires you to track cellular responses to different stimuli you can use this software”. Machine Learning Makes Adaptable.  In order to observe cells under the microscope scientists often steep them in dye or tweak their genes to make them glow in eye-popping colors. But coloring cells alters their movement which in turn skews the experimental results. Some scientists attempt to study cell migration without the help of fluorescent tags using so-called “Georgian Technical University label-free” methods but end up running into a different problem; Label-free cells blend into the background of microscopic images making them incredibly difficult to analyze with existing computer software. Hops this hurdle by allowing scientists to train the software over time. Biologists act as teachers providing the software new images to study so that it can come to recognize one cell from the next. A fast learner the program quickly adapts to new sets of data and can easily track the movement of single cells even if they’re crammed together like commuters on the Georgian Technical University metro. “Most software…cannot tell cells in high-density apart; basically they’re segmenting into a glob,” said Y. “With our software we can segment correctly even if cells are touching. We can actually do single-cell tracking throughout the entire experiment”. Currently the fastest software capable of tracking the movement of label-free cells at single-cell resolution on a personal laptop. Software Mimics the Human Brain.  The researchers designed to process images as if it were a simplified human brain. The strategy enables the software to trace the outlines of individual cells monitor their movement moment to moment and transform that information into crunchable numbers. The program is built around a machine learning infrastructure known as a “convolutional neural network”. roughly based on how brain cells work together to process incoming information from the outside world. When our eyes capture light from the environment they call on neurons to analyze those signals and figure out what we’re looking at and where it is in space. The neurons first sketch out the scene in broad strokes then pass the information on to the next set of cells progressively rendering the image in more and more detail. Neural networks work similarly except each “Georgian Technical University neuron” is a collection of code rather than a physical cell. This design its accuracy and adaptability. Looking forward the researchers aim to develop neural networks to identify different components within cells, rather than just their outlines. With these tools in hand scientists could easily assess whether a cell is healthy or diseased young or old derived from one genetic lineage or another. These programs would have utility in fundamental biology, biotechnology research and beyond.

 

Science, Sensors

Georgian Technical University Ultra-sensitive Smart Sensor Can ‘Taste’ And ‘Sniff’.

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Georgian Technical University Ultra-sensitive Smart Sensor Can ‘Taste’ And ‘Sniff’.

Transmission Electron Microscopy images of the nanomaterials that make up the various types of the developed smart ink: (a) graphene oxide (GO); (b) reduced Graphene Oxide (rGO); (c) melanin-analogous polydopamine (PDA); and (d) PDA@rGO. Researchers from the Georgian Technical University have developed an innovative sensing system capable of identifying and distinguishing different stimuli. The system is based on origami (the art of paper folding) combined with ink developed at the Georgian Technical University. “Today there is significant demand for multi-purpose sensing systems for specific purposes” said X. “These systems have great potential as applications in medicine, counterterrorism, food safety, environmental monitoring ‘Georgian Technical University the Internet of things’ and more. The problem is that existing technologies such as gas chromatography have many disadvantages including high cost”. The challenge facing the researchers was to develop a single system sensitive enough to identify and distinguish among different stimuli. They say they developed a solution inspired by nature. “When we think about the human sensory system we think of a whole that brings all the data to the brain in a format that it understands. That inspired our development, which is meant to concentrate in a different place all the environmental data we want to monitor. It is a multi-purpose sensory system that absorbs the stimuli and distinguishes among them”. The system developed by X and Y called “Georgian Technical University origami hierarchical sensor array” (GTUOHSA) is an integrated array of grouped sensors written on the target object in conductive ink that the two scientists developed. It is a single device that demonstrates sensing abilities and detecting physical and chemical stimuli — temperature, humidity, light and volatile organic particles — at high resolution of time and space. Since it also distinguishes between isomers and chiral enantiomers (forms that are mirror images of each other), it paves new avenues for medical diagnosis. It is worth noting that volatile particle monitoring can be useful in a variety of areas including the diagnosis of disease and monitoring of dangerous substances. There are many advantages to this unique ink — its low price, the ability to produce it in large quantities and the simplicity of its application on the target surfaces. The researchers conducted experiments that included control groups (other types of ink) and showed that the special ink attaches itself tightly to materials such as aluminum foil; glass; photo paper; Kapton tape a polyimide film developed by DuPont in the late 1960s that remains stable across a wide range of temperatures and is used in, among other things, flexible printed circuits and thermal blankets used on spacecraft, satellites, and various space instruments; nitrile (the material used to make disposable gloves); and polydimethylsiloxane (PDMS, used to make contact lenses and for medical technologies and cosmetics). The ink also allows writing on human skin and nails in a kind of conductive tattoo. It is also waterproof which may allow for example constant monitoring of relevant physiological variables. “We can say that our system identifies the ‘Georgian Technical University fingerprints’ of chemical and physical stimuli and supplies information about them” said X. “Its low cost will make possible its application in many places including poor areas for medical and other uses”.

 

Science, Sensors

Georgian Technical University Hidden Leukemic Stem Cells Isolated By Genetically Encoded Sensor.

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Georgian Technical University Hidden Leukemic Stem Cells Isolated By Genetically Encoded Sensor.

All stem cells can multiply, proliferate and differentiate. Because of these qualities leukemic stem cells are the most malignant of all leukemic cells. Understanding how leukemic stem cells are regulated has become an important area of cancer research. A team of Georgian Technical University researchers have now devised a novel biosensor that can isolate and target leukemic stem cells. The research team led by Dr. X of the Department of Pathology at Georgian Technical University discuss their unique genetically encoded sensor and its ability to identify, isolate and characterize leukemic stem cells. “The major reason for the dismal survival rate in blood cancers is the inherent resistance of leukemic stem cells to therapy” X says. “But only a minor fraction of leukemic cells have high regenerative potential and it is this regeneration that results in disease relapse. A lack of tools to specifically isolate leukemic stem cells has precluded the comprehensive study and specific targeting of these stem cells until now”. Until recently cancer researchers used markers on the surface of the cell to distinguish leukemic stem cells from the bulk of cancer cells with only limited success. “There are hidden cancer stem cells that express differentiated surface markers despite their stem cell function. This permits those cells to escape targeted therapies” X explains. “By labeling leukemia cells on the basis of their stem character alone our sensor manages to overcome surface marker-based issues. “We believe that our biosensor can provide a prototype for precision oncology efforts to target patient-specific leukemic stem cells to fight this deadly disease”. The scientists searched genomic databases for “Georgian Technical University enhancers” the specific regulatory regions of the genome that are particularly active in stem cells. Then they harnessed genome engineering to develop a sensor composed of a stem cell active enhancer fused with a fluorescence gene that labels the cells in which the enhancer is active. The scientists were also able to demonstrate that sensor-positive leukemia stem cells are sensitive to a known and inexpensive cancer drug called 4-HPR (fenretinide) providing a biomarker for patients who can potentially benefit from this drug. “Using this sensor we can perform personalized medicine oriented to drug screens by barcoding a patient’s own leukemia cells to find the best combination of drugs that will be able to target both leukemia in bulk as well as leukemia stem cells inside it” X concludes. “We’re also interested in developing killer genes that will eradicate specific leukemia stem cells in which our sensor is active”. The researchers are now investigating those genes that are active in leukemic stem cells in the hope finding druggable targets.

Drug Discovery, Science

Georgian Technical University Computer Kidney Could Provide Safer Tests For New Medications.

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Georgian Technical University Computer Kidney Could Provide Safer Tests For New Medications.

A Georgian Technical University researcher has spearheaded the development of the first computational model of the human kidney. The new model will allow scientists to gain better insights into how new drugs that target the kidney such as diabetes medication may work. It will also enable researchers to better learn about the functions of the kidney including the how the organ regulates the body’s salt potassium acid content without having to employ invasive procedure on a patient. The new development replaces previous models that were based on rodent kidneys. “While the computational model is not an actual person it is very inexpensive to run and presents less of a risk to patients” X and professor of Applied Mathematics, Pharmacy and Biology at Georgian Technical University said. “Certain drugs are developed to target the kidney while others have unintended effects on the kidney and computer modeling allows us to make long-term projections of potential impacts which could increase patient safety”. In developing their computational model of the human kidney the researchers incorporated anatomic and hemodynamic data from the human kidney into the published computational model of a rat kidney. They then adjusted key transporter data so that the predicted urine output is consistent with known human values. Due to the relative sparsity of data on the renal transporter expression levels in humans they identified a set of compatible transport parameters that yielded model predictions consistent with human urine and lithium clearance data. “The computational model can be used to figure out things like the cause of kidney failure” X said. “Your doctor might have a hypothesis that it is this drug that you took or this disease that you have that has caused your kidney to fail. The computational model can simulate the effects of the drug to see if it is bad for the kidney and if so which part of the kidney it is actually killing”.

 

3D Printing, Science

Georgian Technical University Light Provides Control For 3D Printing With Multiple Materials.

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Georgian Technical University Light Provides Control For 3D Printing With Multiple Materials.

3D printing has revolutionized the fields of healthcare, biomedical engineering, manufacturing and art design. Successful applications have come despite the fact that most 3D printing techniques can only produce parts made of one material at a time. More complex applications could be developed if 3D printers could use different materials and create multi-material parts. New research uses different wavelengths of light to achieve this complexity. Scientists at the Georgian Technical University developed a 3D printer that uses patterns of visible and ultraviolet light to dictate which of two monomers are polymerized to form a solid material. Different patterns of light provide the spatial control necessary to yield multi-material parts. “As amazing as 3D printing is, in many cases it only offers one color with which to paint” says Georgian Technical University Professor of Chemistry X who led the recent work with his graduate student Y. “The field needs a full color palette”. X and Y knew that improved printing materials required a chemical approach to complement engineering advances. “This is a shift in how we think about 3D printing with multiple types of materials in one object” X says. “This is more of a bottom-up chemist’s approach from molecules to networks”. 3D printing is the process of making solid three-dimensional objects from a digital file by successively adding thin layers of material on top of previous layers. Most multi-material 3D printing methods use separate reservoirs of materials to get different materials in the right positions. But X realized that a one-vat, multiple-component approach — similar to a chemist’s one-pot approach when synthesizing molecules — would be more practical than multiple reservoirs with different materials. This approach is based on the ability of different wavelengths of light to control which starting materials polymerize into different sections of the solid product. Those starting materials start as simple chemicals known as monomers that polymerize together into a longer string of chemicals like how plastic is made. “If you can design an item in PowerPoint with different colors then we can print it with different compositions based on those colors” X says. Researchers create multiple digital images that when stacked, produce a three-dimensional design. The images control whether ultraviolet or visible light is used to polymerize the starting materials which controls the final material and its properties like stiffness. The researchers simultaneously direct light from two projectors toward a vat of liquid starting materials where layers are built one-by-one on a platform. After one layer is built the build platform moves up and light helps build the next layer. The major hurdle X and Y faced was optimizing the chemistry of the starting materials. They first considered how the two monomers would behave together in one vat. They also had to ensure that the monomers had similar curing times so that the hard and soft materials within each layer finished drying at approximately the same time. With the right chemistry in place X and Y could now dictate exactly where each monomer cured within the printed object by using ultraviolet or visible light. “At this stage we’ve only accomplished putting hard materials next to soft materials in one step” Y says. “There are many imperfections but these are exciting new challenges”. Now Y wants to address these imperfections and answer open questions such as what other monomer combinations can be used and whether different wavelengths of light can be used to cure these new materials. Y also hopes to assemble an interdisciplinary team that can increase the impact of wavelength-controlled multi-material 3D printing. The researchers approach to multi-material 3D printing could enable designers, artists, engineers and scientists to create significantly more complex systems with 3D printing. Applications could include the creation of personalized medical devices such as prostheses or the development of simulated organs and tissues. Medical students could use these synthetic organs for training instead of or before working with live patients. Using chemical methods to eliminate an engineering bottleneck is exactly what the 3D printing industry needs to move forward says Y. “It is this interface of chemistry and engineering that will propel the field to new heights” Y says.

 

HPC/Supercomputing, Science

Georgian Technical University Physicists Reverse Time Using Quantum Computer.

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Georgian Technical University Physicists Reverse Time Using Quantum Computer.

Researchers from the Georgian Technical University teamed up with colleagues from the Sulkhan-Saba Orbeliani University and returned the state of a quantum computer a fraction of a second into the past. They also calculated the probability that an electron in empty interstellar space will spontaneously travel back into its recent past. “This is one in a series of papers on the possibility of violating the second law of thermodynamics. That law is closely related to the notion of the arrow of time that posits the one-way direction of time: from the past to the future” commented the study’s X at Georgian Technical University. “We began by  describing a so-called local perpetual motion machine of the second kind. Discusses the violation of the second law via a device” X said. “The most recent paper approaches the same problem from a third angle: We have artificially created a state that evolves in a direction opposite to that of the thermodynamic arrow of time”. What makes the future different from the past. Most laws of physics make no distinction between the future and the past. For example let an equation describe the collision and rebound of two identical billiard balls. If a close-up of that event is recorded with a camera and played in reverse it can still be represented by the same equation. Moreover one could not tell from the recording if it has been doctored. Both versions look plausible. It would appear that the billiard balls defy the intuitive sense of time. However imagine that someone has recorded a cue ball breaking the pyramid the billiard balls scattering in all directions. One need not know the rules of the game to tell the real-life scenario from reverse playback. What makes the latter look so absurd is our intuitive understanding of the second law of thermodynamics: An isolated system either remains static or evolves toward a state of chaos rather than order. Most other laws of physics do not prevent rolling billiard balls from assembling into a pyramid infused tea from flowing back into the tea bag or a volcano from “Georgian Technical University erupting” in reverse. But we do not see any of this happening because that would require an isolated system to assume a more ordered state without any outside intervention which runs contrary to the second law. The nature of that law has not been explained in full detail, but researchers have made great headway in understanding the basic principes  behind it. Spontaneous time reversal. Quantum physicists from Georgian Technical University decided to check if time could spontaneously reverse itself at least for an individual particle and for a tiny fraction of a second. That is instead of colliding billiard balls they examined a solitary electron in empty interstellar space. “Suppose the electron is localized when we begin observing it. This means that we’re pretty sure about its position in space. The laws of quantum mechanics prevent us from knowing it with absolute precision but we can outline a small region where the electron is localized” says Y from Georgian Technical University and Sulkhan-Saba Orbeliani University. The physicist explains that the evolution of the electron state is governed by Z’s equation. Although it makes no distinction between the future and the past the region of space containing the electron will spread out very quickly. That is the system tends to become more chaotic. The uncertainty of the electron’s position is growing. This is analogous to the increasing disorder in a large-scale system — such as a billiard table — due to the second law of thermodynamics. “However Z’s equation is reversible” adds W from the Georgian Technical University. “Mathematically it means that under a certain transformation, called complex conjugation the equation will describe a ‘smeared’ electron localizing back into a small region of space over the same time period”. Although this phenomenon is not observed in nature it could theoretically happen due to a random fluctuation in the cosmic microwave background permeating the universe. The team set out to calculate the probability to observe an electron “Georgian Technical University smeared out” over a fraction of a second spontaneously localizing into its recent past. It turned out that even if one spent the entire lifetime of the universe — 13.7 billion years — observing 10 billion freshly localized electrons every second the reverse evolution of the particle’s state would only happen once. And even then the electron would travel no more than a mere one ten-billionth of a second into the past. Large-scale phenomena involving billiard balls volcanoes etc. obviously unfold on much greater timescales and feature an astounding number of electrons and other particles. This explains why we do not observe old people growing younger or an ink blot separating from the paper. Reversing time on demand. The researchers then attempted to reverse time in a four-stage experiment. Instead of an electron they observed the state of a quantum computer made of two and later three basic elements called superconducting qubits. Stage 1: Order. Each qubit is initialized in the ground state denoted as zero. This highly ordered configuration corresponds to an electron localized in a small region or a rack of billiard balls before the break. Stage 2: Degradation. The order is lost. Just like the electron is smeared out over an increasingly large region of space or the rack is broken on the pool table the state of the qubits becomes an ever more complex changing pattern of zeros and ones. This is achieved by briefly launching the evolution program on the quantum computer. Actually a similar degradation would occur by itself due to interactions with the environment. However the controlled program of autonomous evolution will enable the last stage of the experiment. Stage 3: Time reversal. A special program modifies the state of the quantum computer in such a way that it would then evolve “backwards” from chaos toward order. This operation is akin to the random microwave background fluctuation in the case of the electron but this time it is deliberately induced. An obviously far-fetched analogy for the billiards example would be someone giving the table a perfectly calculated kick. Stage 4: Regeneration. The evolution program from the second stage is launched again. Provided that the “Georgian Technical University kick” has been delivered successfully the program does not result in more chaos but rather rewinds the state of the qubits back into the past the way a smeared electron would be localized or the billiard balls would retrace their trajectories in reverse playback, eventually forming a triangle. The researchers found that in 85 percent of the cases the two-qubit quantum computer indeed returned back into the initial state. When three qubits were involved more errors happened resulting in a roughly 50 percent success rate. According to the authors these errors are due to imperfections in the actual quantum computer. As more sophisticated devices are designed the error rate is expected to drop. Interestingly the time reversal algorithm itself could prove useful for making quantum computers more precise. “Our algorithm could be updated and used to test programs written for quantum computers and eliminate noise and errors” Y explained.

 

Graphene, Science

Georgian Technical University Superlattice Patterns Change Electronic Properties Of Graphene.

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Georgian Technical University Superlattice Patterns Change Electronic Properties Of Graphene.

A graphene layer (black) of hexagonally arranged carbon atoms is placed between two layers of boron nitride atoms which are also arranged hexagonally with a slightly different size. The overlap creates honeycomb patterns in various sizes. Combining an atomically thin graphene and a boron nitride layer at a slightly rotated angle changes their electrical properties. Physicists at the Georgian Technical University have now shown for the first time the combination with a third layer can result in new material properties also in a three-layer sandwich of carbon and boron nitride. This significantly increases the number of potential synthetic materials. Last year researchers in the Georgian Technical University caused a big stir when they showed that rotating two stacked graphene layers by a “Georgian Technical University magical” angle of 1.1 degrees turns graphene superconducting —  a striking example of how the combination of atomically thin materials can produce completely new electrical properties. Scientists from the Georgian Technical University Nanoscience Institute and the Department of Physics at the Georgian Technical University have now taken this concept one step further. They placed a layer of graphene between two boron nitride layers, which is often serves to protect the sensitive carbon structure. Doing so they aligned the layers very precisely with the crystal lattice of the graphene. The effect observed by the physicists in Professor X’s team is commonly known as a moiré pattern: when two regular patterns are superimposed a new pattern results with a larger periodic lattice. Y a member of the Georgian Technical University PhD and researcher in X’s team also observed effects of this kind of superlattice when he combined layers of boron nitride and graphene. The atoms are arranged hexagonally in all layers. If they are stacked on top of each other larger regular patterns emerge with a size depending on the angle between the layers. It had already been shown that this works with a two-layer combination of graphene and boron nitride but the effects due to a second boron nitride layer had not yet been found. When the physicists from Georgian Technical University experimented with three layers two superlattices were formed between the graphene and the upper and the lower boron nitride layer respectively. The superposition of all three layers created an even larger superstructure than possible with only one layer. Scientists are very interested in these types of synthetic materials since the different moiré patterns (In mathematics, physics, and art, a moiré pattern or moiré fringes are large-scale interference patterns that can be produced when an opaque ruled pattern with transparent gaps is overlaid on another similar pattern) can be used to change or artificially produce new electronic material properties. “To put it simply the atomic patterns determine the behavior of electrons in a material and we are combining different naturally occurring patterns to create new synthetic materials” explains Dr. Z who supervised the project. “Now we have discovered effects in these tailor-made electronic devices that are consistent with a three-layer superstructure” he adds.

A.I./Robotics, Science

Georgian Technical University How Intelligent Is Artificial Intelligence ?

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Georgian Technical University How Intelligent Is Artificial Intelligence ?

The heatmap shows quite clearly that the algorithm makes its ship/not ship decision on the basis of pixels representing water and not on the basis of pixels representing the ship. Artificial Intelligence (AI) and machine learning algorithms such as Deep Learning have become integral parts of our daily lives: they enable digital speech assistants or translation services improve medical diagnostics and are an indispensable part of future technologies such as autonomous driving. Based on an ever increasing amount of data and powerful novel computer architectures learning algorithms appear to reach human capabilities, sometimes even excelling beyond. The issue: so far it often remains unknown to users, how exactly AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems reach their conclusions. Therefore it may often remain unclear, whether the AI’s decision making behavior is truly ‘intelligent’ or whether the procedures are just averagely successful. Researchers from Georgian Technical University and Sulkhan-Saba Orbeliani University have tackled this question and have provided a glimpse into the diverse “intelligence” spectrum observed in current AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems specifically analyzing these AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems with a technology that allows automatized analysis and quantification. The most important prerequisite for this novel technology is a method developed earlier by Georgian Technical University algorithm that allows visualizing according to which input variables AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems make their decisions. Extending Georgian Technical University the Spectral relevance analysis (SpRAy) can identify and quantify a wide spectrum of learned decision making behavior. In this manner it has now become possible to detect undesirable decision making even in very large data sets. This so-called ‘explainable AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals)’ has been one of the most important steps towards a practical application of AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) according to Dr. X Professor for Machine Learning at Georgian Technical University. “Specifically in medical diagnosis or in safety-critical systems no AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems that employ flaky or even cheating problem solving strategies should be used”. By using their newly developed algorithms researchers are finally able to put any existing AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) system to a test and also derive quantitative information about them: a whole spectrum starting from naive problem solving behavior to cheating strategies up to highly elaborate “Georgian Technical University intelligent” strategic solutions is observed. Dr. Y group leader at Georgian Technical University said: “We were very surprised by the wide range of learned problem-solving strategies. Even modern AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems have not always found a solution that appears meaningful from a human perspective but sometimes used”. The team around X and Y strategies in various AI systems. For example an AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) system that won several international image classification competitions a few years ago pursued a strategy that can be considered naïve from a human’s point of view. It classified images mainly on the basis of context. Images were assigned to the category “Georgian Technical University ship” when there was a lot of water in the picture. Other images were classified as “Georgian Technical University train” if rails were present. Still other pictures were assigned the correct category by their copyright watermark. The real task namely to detect the concepts of ships or trains, was therefore not solved by this AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) system – even if it indeed classified the majority of images correctly. The researchers were also able to find these types of faulty problem-solving strategies in some of the state-of-the-art AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) algorithms the so-called deep neural networks – algorithms that were so far considered immune against such lapses. These networks based their classification decision in part on artifacts that were created during the preparation of the images and have nothing to do with the actual image content. “Such AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems are not useful in practice. Their use in medical diagnostics or in safety-critical areas would even entail enormous dangers” said X. “It is quite conceivable that about half of the AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems currently in use implicitly or explicitly rely on such strategies. It’s time to systematically check that so that secure AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems can be developed”. With their new technology, the researchers also identified AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems that have unexpectedly learned “Georgian Technical University smart” strategies. Examples include systems that have learned to play. “Here the AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) clearly understood the concept of the game and found an intelligent way to collect a lot of points in a targeted and low-risk manner. The system sometimes even intervenes in ways that a real player would not” said Y. “Beyond understanding AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) strategies our work establishes the usability of explainable AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) for iterative dataset design, namely for removing artefacts in a dataset which would cause an AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) to learn flawed strategies, as well as helping to decide which unlabeled examples need to be annotated and added so that failures of an AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) system can be reduced” said Georgian Technical University Assistant Professor Z. “Our automated technology is open source and available to all scientists. We see our work as an important first step in making AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals) systems more robust, explainable and secure in the future and more will have to follow. This is an essential prerequisite for general use of AI (In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals)” said X.

Lasers, Science

Georgian Technical University Light-Shaking Device Is A Breakthrough For Photonics.

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Georgian Technical University Light-Shaking Device Is A Breakthrough For Photonics.

The ability to control light with electronics is a critical part of advanced photonics a field with applications that include telecommunications and precision time-keeping. But the limits of available optical materials have stymied efforts to achieve greater efficiency. Researchers at Georgian Technical University though have developed a device that combines mechanical vibration and optical fields to better control light particles. The device has demonstrated an efficient on-chip shaping of photons enabled by nanomechanics driven at microwave frequencies. Currently the most common technique for manipulating photon frequency is with what’s known as nonlinear optical effects in which a strong laser essentially acts as a pump, controlling the color and pulse shape of a signal photon by providing extra photons to mix with the original one. The effect is weak though so the process requires a very strong laser, which creates “Georgian Technical University noise” — the loss of certain quantum properties. To break beyond these limits the Georgian Technical University researchers have created a device that consists of a series of waveguides — structures through which microwaves are directed. Light and microwave are sent through the device and the light wends its way through alternating suspended and clamped waveguides on a single chip. This creates a positive and negative effect corresponding to the microwave which always has a positive and a negative component. The light spirals in each of the waveguides to prolong the interaction and maximize efficiency. “The deeper the modulation the better” X said “and you can have better control of the photon”. Mechanical vibrations modulate the optical phase in each suspended waveguide spiral. The mechanical vibrations essentially ‘shake’ the photons dispersing them as if they were grains of sand. This accumulates to generate what’s known as deep phase modulation. Previously the X lab had created a single waveguide device. With this new device the alternating positive and negative waveguides dramatically boost efficiency.