Fusion: It’s Time to Color outside the Lines

There has been some good news, and some bad news in the controlled fusion community recently. The good news is that the Lawrence Livermore National Laboratory (LLNL) has recently produced a burning plasma. It succeeded on several of its shots where ~1.5 - 2 megajoules from its laser (National Ignition Facility, or NIF) has generated ~1.3 - 3 megajoules of fusion products. The highest ratio of fusion energy to laser energy it achieved, defined as its Q, was 1.5 at the time of this writing. While LLNL is sponsored by nuclear stockpile stewardship, this author sees a likely path from their result to fusion for energy for the world, a path using a very different laser and a very different target configuration. The bad news is that the International Tokamak Experimental Reactor (ITER) has continued to stumble on more and more delays and cost overruns, as its capital cost has mushroomed from ~$5 billion to ~ $25 B. This paper argues that the American fusion effort, for energy for the civilian economy, should switch its emphasis not only from magnetic fusion to inertial fusion but should also take much more seriously fusion breeding. Over the next few decades, the world might well be setting up more and more thermal nuclear reactors, and these might need fuel which only fusion breeders can supply. In other words, fusion should begin to color outside the lines.

Smart Grid Cyber Security： An Overview of Threats and Countermeasures

The smart grid is the next generation of power and distribution systems. The integration of advanced network, communications, and computing techniques allows for the enhancement of efficiency and reliability. The smart grid interconnects the flow of information via the power line, intelligent metering, renewable and distributed energy systems, and a monitoring and controlling infrastructure. For all the advantages that these components come with, they remain at risk to a spectrum of physical and digital attacks. This paper will focus on digital vulnerabilities within the smart grid and how they may be exploited to form full fledged attacks on the system. A number of countermeasures and solutions from the literature will also be reported, to give an overview of the options for dealing with such problems. This paper serves as a triggering point for future research into smart grid cyber security.

Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates

Relational linkages connecting process,structure,and properties are some of the most sought after goals in additive manufacturing(AM).This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured counterparts.Furthermore,data collection at the microscale is costly.Consequently,this work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate models in a directed graphical network capable of achieving near real-time property predictions with single digit error magnitudes when predicting full stress–strain histories of a given microstructure.This methodology is presented and demonstrated using computationally generated microstructures and results from crystal plasticity simulations on those microstructures.The surrogate model uses grain-level microstructural descriptors rather than whole microstructure descriptors so that properties of new,arbitrary microstructures can be predicted.The developed network has the potential to scale to predict mechanical properties of grain structures that would be infeasible to simulate using finite element methods.

Finite-size correction for slab supercell calculations of materials with spontaneous polarization

The repeated slab approach has become a de facto standard to accurately describe surface properties of materials by density functional theory calculations with periodic boundary conditions.For materials exhibiting spontaneous polarization,we show that the conventional scheme of passivation with pseudo hydrogen is unable to realize a charge-neutral surface.The presence of a net surface charge induces via Gauss’s law a macroscopic electric field through the slab and results in poor size convergence with respect to the thickness of the slab.We propose a modified passivation method that accounts for the effect of spontaneous polarization,describes the correct bulk limits and boosts convergence with respect to slab thickness.The robustness,reliability,and superior convergence of energetics and electronic structure achieved by the proposed method are demonstrated using the example of polar ZnO surfaces.

Robust reduction of graphene fluoride using an electrostatically biased scanning probe

We report a novel and easily accessible method to chemically reduce graphene fluoride （GF） sheets with nanoscopic precision using high electrostatic fields generated between an atomic force microscope （AFM） tip and the GF substrate. Reduction of fluorine by the electric field produces graphene nanoribbons （GNR） with a width of 105-1,800 nm with sheet resistivity drastically decreased from 〉1 TΩ.sq.^-1 （GF） down to 46 kΩ.sq.^-1 （GNR）. Fluorine reduction also changes the topography, friction, and work function of the GF. Kelvin probe force microscopy measurements indicate that the work function of GF is 180-280 meV greater than that of graphene. The reduction process was optimized by varying the AFM probe velocity between 1.2 μm.s^-1 and 12 μm.s^-1 and the bias voltage applied to the sample between -8 and -12 V. The electrostatic field required to remove fluorine from carbon is -1.6 V.nm-1. Reduction of the fluorine may be due to the softening of the C-F bond in this intense field or to the accumulation and hydrolysis of adventitious water into a meniscus.

Machine-learned interatomic potentials for alloys and alloy phase diagrams

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions.We compare two different approaches.Moment tensor potentials(MTPs)are polynomial-like functions of interatomic distances and angles.The Gaussian approximation potential(GAP)framework uses kernel regression,and we use the smooth overlap of atomic position(SOAP)representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density.Both types of potentials give excellent accuracy for a wide range of compositions,competitive with the accuracy of cluster expansion,a benchmark for this system.While both models are able to describe small deformations away from the lattice positions,SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations,and MTP allows,due to its lower computational cost,the calculation of compositional phase diagrams.Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models,we expect them to open new avenues in computational materials modeling for alloys.

Near-UV electroluminescence in unipolar-doped, bipolar-tunneling GaN/AlN heterostructures

Cross-gap light emission is reported in n-type unipolar GaN/AlN double-barrier heterostructure diodes at room temperature.Three different designs were grown on semi-insulating bulk GaN substrates using molecular beam epitaxy(MBE).All samples displayed a single electroluminescent spectral peak at 360 nm with full-width at half-maximum(FWHM)values no greater than 16 nm and an external quantum efficiency(EQE)of≈0.0074%at 18.8 mA.In contrast to traditional GaN light emitters,p-type doping and p-contacts are completely avoided,and instead,holes are created in the GaN on the emitter side of the tunneling structure by direct interband(that is,Zener)tunneling from the valence band to the conduction band on the collector side.The Zener tunneling is enhanced by the high electric fields(~5×106 V cm^(−1))created by the notably large polarization-induced sheet charge at the interfaces between the AlN and GaN.

Silk fibroin as an additive for cell-free protein synthesis

Cell-free systems contain many proteins and metabolites required for complex functions such as transcription and translation or multi-step metabolic conversions.Research into expanding the delivery of these systems by drying or by embedding into other materials is enabling new applications in sensing,point-of-need manufacturing,and responsive materials.Meanwhile,silk fibroin from the silk worm,Bombyx mori,has received attention as a protective additive for dried enzyme formulations and as a material to build biocompatible hydrogels for controlled localization or delivery of biomolecular cargoes.In this work,we explore the effects of silk fibroin as an additive in cell-free protein synthesis(CFPS)reactions.Impacts of silk fibroin on CFPS activity and stability after drying,as well as the potential for incorporation of CFPS into hydrogels of crosslinked silk fibroin are assessed.We find that simple addition of silk fibroin increased productivity of the CFPS reactions by up to 42%,which we attribute to macromolecular crowding effects.However,we did not find evidence that silk fibroin provides a protective effects after drying as previously described for purified enzymes.Further,the enzymatic crosslinking transformations of silk fibroin typically used to form hydrogels are inhibited in the presence of the CFPS reaction mixture.Crosslinking attempts did not impact CFPS activity,but did yield localized protein aggregates rather than a hydrogel.We discuss the mechanisms at play in these results and how the silk fibroin-CFPS system might be improved for the design of cell-free devices.

First-principles calculations of hyperfine interaction,binding energy,and quadrupole coupling for shallow donors in silicon

Spin qubits based on shallow donors in silicon are a promising quantum information technology with enormous potential scalability due to the existence of robust silicon-processing infrastructure.However,the most accurate theories of donor electronic structure lack predictive power because of their reliance on empirical fitting parameters,while predictive ab initio methods have so far been lacking in accuracy due to size of the donor wavefunction compared to typical simulation cells.We show that density functional theory with hybrid and traditional functionals working in tandem can bridge this gap.Our first-principles approach allows remarkable accuracy in binding energies(67 meV for bismuth and 54 meV for arsenic)without the use of empirical fitting.We also obtain reasonable hyperfine parameters(1263 MHz for Bi and 133 MHz for As)and superhyperfine parameters.We demonstrate the importance of a predictive model by showing that hydrostatic strain has much larger effect on the hyperfine structure than predicted by effective mass theory,and by elucidating the underlying mechanisms through symmetry analysis of the shallow donor charge density.