skip to main content

This content will become publicly available on March 24, 2023

Title: Photoemission characterization of N-polar III-nitride photocathodes as candidate bright electron beam sources for accelerator applications

We report on the growth and characterization of a new class of photocathode structures for use as electron sources to produce high brightness electron beams for accelerator applications. The sources are realized using III-nitride materials and are designed to leverage the strong polarization field, which is characteristic of this class of materials when grown in their wurtzite crystal structure, to produce a negative electron affinity condition without the use of Cs, possibly allowing these materials to be operated in radio frequency guns. A Quantum Efficiency (QE) of about [Formula: see text] and an emitted electrons’ Mean Transverse Energy (MTE) of about 100 meV are measured at a wavelength of 265 nm. In a vacuum level of [Formula: see text] Torr, the QE does not decrease after more than 24 h of continuous operation. The lowest MTE of about 50 meV is measured at 300 nm along with a QE of [Formula: see text]. Surface characterizations reveal a possible contribution to the MTE from surface morphology, calling for more detailed studies.

Authors:
 ;  ;  ;  ;  ;  ;  ;  ;  
Publication Date:
NSF-PAR ID:
10364227
Journal Name:
Journal of Applied Physics
Volume:
131
Issue:
12
Page Range or eLocation-ID:
Article No. 124902
ISSN:
0021-8979
Publisher:
American Institute of Physics
Sponsoring Org:
National Science Foundation
More Like this
  1. Active acoustic metamaterials are one path to acoustic properties difficult to realize with passive structures, especially for broadband applications. Here, we experimentally demonstrate a 2D metamaterial composed of coupled sensor-driver unit cells with effective bulk modulus ([Formula: see text]) precisely tunable through adjustments of the amplitude and phase of the transfer function between pairs of sensors and drivers present in each cell. This work adopts the concepts of our previous theoretical study on polarized sources to realize acoustic metamaterials in which the active unit cells are strongly interacting with each other. To demonstrate the capability of our active metamaterial to produce on-demand negative, fractional, and large [Formula: see text], we matched the scattered field from an incident pulse measured in a 2D waveguide with the sound scattered by equivalent continuous materials obtained in numerical simulations. Our approach benefits from being highly scalable, as the unit cells are independently controlled and any number of them can be arranged to form arbitrary geometries without added computational complexity.
  2. Microwave loss in niobium metallic structures used for superconducting quantum circuits is limited by a native surface oxide layer formed over a timescale of minutes when exposed to an ambient environment. In this work, we show that nitrogen plasma treatment forms a niobium nitride layer at the metal–air interface, which prevents such oxidation. X-ray photoelectron spectroscopy confirms the doping of nitrogen more than 5 nm into the surface and a suppressed oxygen presence. This passivation remains stable after aging for 15 days in an ambient environment. Cryogenic microwave characterization shows an average filling-factor-adjusted two-level-system loss tangent [Formula: see text] of [Formula: see text] for resonators with a 3 [Formula: see text]m center strip and [Formula: see text] for a 20 [Formula: see text]m center strip, exceeding the performance of unpassivated samples by a factor of four.

  3. Ellipsometry measurements were taken on single-crystalline Ni(100) at various temperatures between 77 and 770 K. DC conductivity and resistivity are extracted from the model optical constants and their temperature dependence is discussed. The authors find only qualitative agreement in the general trend of the resistivity measured by ellipsometry and electrical measurements. The temperature dependence of the main absorption peak at 4.8 eV indicates that the interband transitions are scattered by magnons with an effective energy of about 53 meV. The width of the main absorption peak reduces by 0.38 eV as the temperature rises, which is interpreted as the ferromagnetic exchange energy at the L-point. The small absorption peak at 1.5 eV is prominent only in the ferromagnetic phase and almost disappears in the paramagnetic phase. This peculiarity is explained by assigning the peak to [Formula: see text] transitions, which accounts for the decrease of the magnitude of the peak and its constant energy.

  4. Prussian blue analogs (PBAs) are an important material class for aqueous electrochemical separations and energy storage owing to their ability to reversibly intercalate monovalent cations. However, incorporating interstitial [Formula: see text] molecules in the ab initio study of PBAs is technically challenging, though essential to understanding the interactions between interstitial water, interstitial cations, and the framework lattice that affect intercalation potential and cation intercalation selectivity. Accordingly, we introduce and use a method that combines the efficiency of machine-learning models with the accuracy of ab initio calculations to elucidate mechanisms of (1) lattice expansion upon intercalation of cations of different sizes, (2) selectivity bias toward intercalating hydrophobic cations of large size, and (3) semiconductor–conductor transitions from anhydrous to hydrated lattices. We analyze the PBA nickel hexacyanoferrate [[Formula: see text]] due to its structural stability and electrochemical activity in aqueous electrolytes. Here, grand potential analysis is used to determine the equilibrium degree of hydration for a given intercalated cation (Na[Formula: see text], K[Formula: see text], or Cs[Formula: see text]) and [Formula: see text] oxidation state based on pressure-equilibrated structures determined with the aid of machine learning and simulated annealing. The results imply new directions for the rational design of future cation-intercalation electrodemore »materials that optimize performance in various electrochemical applications, and they demonstrate the importance of choosing an appropriate calculation framework to predict the properties of PBA lattices accurately.

    « less
  5. The materials’ consolidation, especially ceramics, is very important in advanced research development and industrial technologies. Science of sintering with all incoming novelties is the base of all these processes. A very important question in all of this is how to get the more precise structure parameters within the morphology of different ceramic materials. In that sense, the advanced procedure in collecting precise data in submicro-processes is also in direction of advanced miniaturization. Our research, based on different electrophysical parameters, like relative capacitance, breakdown voltage, and [Formula: see text], has been used in neural networks and graph theory successful applications. We extended furthermore our neural network back propagation (BP) on sintering parameters’ data. Prognosed mapping we can succeed if we use the coefficients, implemented by the training procedure. In this paper, we continue to apply the novelty from the previous research, where the error is calculated as a difference between the designed and actual network output. So, the weight coefficients contribute in error generation. We used the experimental data of sintered materials’ density, measured and calculated in the bulk, and developed possibility to calculate the materials’ density inside of consolidated structures. The BP procedure here is like a tool to comemore »down between the layers, with much more precise materials’ density, in the points on morphology, which are interesting for different microstructure developments and applications. We practically replaced the errors’ network by density values, from ceramic consolidation. Our neural networks’ application novelty is successfully applied within the experimental ceramic material density [Formula: see text] [kg/m 3 ], confirming the direction way to implement this procedure in other density cases. There are many different mathematical tools or tools from the field of artificial intelligence that can be used in such or similar applications. We choose to use artificial neural networks because of their simplicity and their self-improvement process, through BP error control. All of this contributes to the great improvement in the whole research and science of sintering technology, which is important for collecting more efficient and faster results.« less