We present a covariant theory for the ageing characteristics of phase-ordering systems that possess dynamical symmetries beyond mere scalings. A chiral spin dynamics which conserves the spin-up (+) and spin-down (−) fractions, and , serves as the emblematic paradigm of our theory. Beyond a parabolic spatio-temporal scaling, we discover a hidden Lorentzian dynamical symmetry therein, and thereby prove that the characteristic length L of spin domainsgrows in time t according to , where (the invariant spin-excess) and βis a universal constant. Furthermore, the normalised length distributions of the spin-up and the spin-down domains each provably adopt a coincident universal (σ-independent) time-invariant form, and this supra-universal probability distribution is empirically verified to assume a form reminiscent of the Wigner surmise.
Mostafa Bedewy has been awarded a new research grant from the National Science Foundation (NSF) for $330,000 as a single principal investigator (PI). The award, titled “Functionally Graded Carbon Nanotubes by Dynamic Control of Morphology during Chemical Vapor Deposition”, will fund research in the NanoProduct Lab (Bedewy Research Group) for three years focusing on studying and controlling the catalytic activation and deactivation during the chemical synthesis of vertically aligned nanotubes.
Manufacturers use carbon nanotubes in a variety of commercial products from baseball bats and bicycle frames to aerospace structures. Attributes such as a tensile strength 20 times higher than steel and an electrical conductivity 10 times that of copper have caused the global carbon nanotube market to soar to $3.43 billion in 2016, and it is projected to double by 2022.
Bedewy will employ a combination of experimental and modeling techniques to reveal the kinetics of activation and deactivation in large populations of carbon nanotubes known as “nanotube forests.”
Artificial neural networks are performing tasks, image recognition and natural language processing, for artificial intelligence. However, these algorithms run on traditional computers and consume orders of magnitude more energy more than the brain does at the same task. One promising path to reduce the energy consumption is to build dedicated hardware to perform artificial intelligence. Nanodevices are particularly interesting because they allow for complex functionality with low energy consumption and small size. I discuss two nanodevices. First, I focus on stochastic magnetic tunnel junctions, which can emulate the spike trains emitted by neurons with a switching rate that can be controlled by an input. junctions can be combined with CMOS circuitry to implement population coding to build low power computing systems capable of controlling output behavior. Second, I turn to different nanodevices, memristors, to implement a different type of computation occurring in nature: swarm intelligence. A broad class of algorithms inspired by the behavior of swarms have been proven successful at solving optimization problems (for example an ant colony can solve a maze). Networks of memristors can perform swarm intelligence and find the shortest paths in mazes, without any supervision or training. These results are striking illustrations of how matching the functionalities of nanodevices with relevant properties of natural systems open the way to low power hardware implementations of difficult computing problems.
We present our recent work on the computational investigations on the charge carrier transport and the excited state decay processes for organic energy materials. We developed a time-dependent vibration correlation function formalism for evaluating the molecular excited state non-radiative decay rate combining non-adiabatic coupling and spin-orbit coupling, to make quantitative prediction for light-emitting quantum efficiency. We proposed a nuclear tunneling enabled hopping model to describe the charge transport in organic semiconductors. An efficient time-dependent DMRG approach is proposed to calculate the optical spectra and carrier dynamics for molecular aggregates.
Feng Xiong and his group developed an “artificial synapse” that does not process information like a digital computer but rather mimics the analog way the human brain completes tasks.
For applications in neuromorphic computing, Xiong and his team focuses on the design of computational hardware inspired by the human brain and built graphene-based artificial synapses in a two-dimensional honeycomb configuration of carbon atoms. Graphene’s conductive properties allowed the researchers to finely tune its electrical conductance, which is the strength of the synaptic connection or the synaptic weight.
Their work was published in the recent issue of the journal Advanced Materials. Other co-authors include Mohammad Sharbati (first author), Yanhao Du, Jorge Torres, Nolan Ardolino, and Minhee Yun.
Giannis Mpourmpakis named as one of 25 researchers around the world as Emerging Investigators in a special issue of the American Chemical Society (ACS) Journal of Chemical & Engineering Data.
Mpourmpakis leads the Computer-Aided Nano and Energy Lab (CANELA) where his group researches the physicochemical properties of nanomaterials with potential applications in diverse nanotechnological areas ranging from energy generation and storage to materials design and catalysis.
Mpourmpakis contributed his paper “Understanding the Gas Phase Chemistry of Alkanes with First-Principles Calculations” to the ACS special issue.
Noa Marom has been selected as the leader of a data science project that's part of the Argonne Leadership Computing Facility’s (ALCF) Aurora Early Science Program (ESP).
The program’s goal is to prepare key applications, libraries, and infrastructure for the architecture and scale of exascale computing.
Marom’s project will combine quantum-mechanical simulations with machine learning and data science, to advance physical understanding of singlet fission and discover materials to create more efficient organic solar cells.
An artistic depiction of research from John Keith's lab was featured on the back cover of Royal Society of Chemistry journal Chemical Science. Yasemin Basdogan, a PhD student in Keith’s lab, designed the back cover image, which shows several molecules reacting in a cross-shaped container slowly filling with a liquid.
Their study titled “A paramedic treatment for modeling explicitly solvated chemical reaction mechanisms” analyzed a very complex chemical system called the Morita-Baylis Hillman reaction. Basdogan and Keith brought improvements to the modeling that allows better understanding of these types of chemical reactions that will impact areas of chemical engineering and chemistry.
Giannis Mpourmpakis is collaborating with with Lubrizol Corporation, a specialty chemical manufacturer, on a project applying high-performance computing simulations to address a problem known as viscosity creep, a phenomenon which affects the performance of lubricating fluids.
Mpourmpakis focuses much of his research on the properties and potential applications of nanomaterials, employing Pitt CRC clusters for high-performance computation to create molecular level simulations.
The results were published in June in the journal Industrial & Engineering Chemistry Research.
Qi Li has received the Bright Futures Student Award from the International Precious Metals Institute for his work on gold nanoparticles. The $5,000 prize sponsored by the Gero Family Trust will be presented this month at the institute's annual conference in Texas.
Li, who works in the lab of Rongchao Jin, also has researched different models of "doping" gold nanoparticles, a process where a small amount of another metal is added to the nanoparticle.