The last decade has seen the rapid growth of interest in materials that are topological because of strong spin-orbit coupling. These samples exhibit electronic surface states that have only half the electrons that a carrier accumulation layer has. The surface states have energy between the occupied valence band and the unoccupied conduction band and the energy/momentum dispersion is linear forming two so-called Dirac cones that meet at the Dirac point, often k=0. They exhibit spin-momentum locking so that a surface state with momentum in some direction has spin...
David Waldeck has been selected as the winner of the Bioelectrochemistry Prize of the International Society of Electrochemistry, in recognition of his fundamental work on charge transport phenomena associated with biomolecules, electron transport through proteins and nucleic acids, and electron transfer at biomolecule/electrode interfaces. The Society will present the Bioelectrochemistry Prize publicly at the 2019 Annual Meeting in Durban, South Africa.
Quantum computation is a promising way to expand computational power as well as perform quantum simulations. There are many proposals on implementing quantum computation, including topological materials, trapped ions, superconducting circuits as well as semiconductor quantum dots. Semiconductor quantum dot qubits are promising candidates for quantum information processing and have recently made substantial experimental progress. One challenge for qubits without topological protection, however, is to suppress decoherence. Performing qubit gate operations as quickly as possible can be important to minimize the effects of decoherence. For resonant gates, this requires applying a strong ac drive. However, strong driving can present control challenges because of the strong driving effects that cannot be described using the rotating-wave approximation. Here we analyze resonant X rotations of a silicon double quantum dot hybrid qubit within a dressed-state formalism. We show that the strong driving effects can be suppressed to the point that gate fidelities above 99.99% are possible, in the absence of decoherence. When coupled to 1/f charge noise typical to our device, we further show that, by applying strong driving, gate fidelities can be above 99.9%. This shows that the quantum operations on silicon quantum dot hybrid qubits can be above the error-correction threshold, which is an important step towards realizing quantum computation.
A low-temperature scanning tunneling microscope (LT-STM) has recently been commissioned at Carnegie Mellon University, and is available for use by external users. The instrument allows atomic-resolution studies of surface structure and spectroscopic studies of electronic states within a few eV on either side of the Fermi energy. Base temperature is 7 K, and there is a magnetic field capability of up to 2T perpendicular to the sample surface.
First results have been obtained by a team led by Randall Feenstra and Ben Hunt, working with postdoc Felix Lupke, grad student Dacen Waters, and undergrads Nicolas Iskos and Nick Speeney. They studied a two-dimensional (2D) material, Tungsten Ditelluride (WTe2), which is a topological insulator, with properties that will likely spur technological innovations such as spintronics and quantum computing.
Users interested in utilized the LT-STM should contact Prof. Feenstra (email@example.com).
Paul Leu is featured as one of the 22 young Pittsburgh leaders paving the way in Pittsburgh's technology field. The honorees are selected by The Incline website in the Who's Next series, which is a monthly series honoring under-40 professionals making Pittsburgh a better place. Paul Leu is awarded for his work on making solar energy economical with new materials for solar cells that are more efficient, lighter, flexible and less expensive.
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.