Scott D. Wolter

Field emission characteristics of diamond coated silicon field emitters

Single crystal silicon field emitters have been modified by surface deposition of diamond using bias‐enhanced microwave plasma chemical vapor deposition. Polycrystalline diamond with a high nucleation density (1010/cm2) and small grain size (<20 nm) was achieved on silicon field emitters. Field emission from these diamond coated emitters exhibited significant enhancement both in total emission current and stability compared to pure silicon emitters. A large effective emitting area comparable to the tip surface area was obtained from a Fowler–Nordheim analysis. The effective work function of the polycrystalline diamond coated emitter surface was found to be larger than that of a pure silicon emitter surface.

Towards an x-ray-based coded aperture diffraction system for bulk material identification

The detection of prohibited items at airport checkpoints, especially energetic materials, by means of x-ray imaging technology, is one of the most important tasks in transportation security. Conventional checkpoint X-ray systems exploit the energy dependence of the material- specific attenuation coefficient to estimate an ‘effective’ atomic number (or Zeff ) and, in some cases, the mass density (ρ) of a target material, which are then used to classify it. While this technology provides high quality imaging capabilities and satisfactory objects discrimination in many security applications, it also has known limitations. For example, differentiating objects with similar Zeff and/or ρ, such as is often the case for many benign organic materials and explosives, can be a challenging task. X-ray Diffraction Tomography (XRDT), using a coded mask (down stream from the sample), provides structural information that can further enhance material discrimination from the unique chemical/molecular signatures. Here, we present experimental data obtained using our research prototype or ‘XRDT’ scanner, built with off-the shelf components. Using two different industrial solvents, one benign (H2O or water) and one prohibited chemical precursor (2-butanone or methyl-ethyl-ketone (MEK)), we have evaluated the detection performance against material type, sample size, beam size, and investigated the effects of background. Within the scope of our study, we find that a satisfactory tomographic reconstruction and reliable bulk material identification can be achieved with the XRDT. These results will help guide the future development of coded aperture based screening technology at security checkpoint.

Design and implementation of a fan beam coded aperture x-ray diffraction tomography system for checkpoint baggage scanning

X-ray diffraction tomography (XRDT) enables material identification throughout a volumetric object. We perform a simulation-based study to analyze how the image quality of a fan beam coded aperture XRDT system depends on the specifications of the detector used to measure the scatter signal. Going beyond simulation, we design and implement an XRDT prototype scanner that operates in near real-time to perform slice-by-slice imaging of a target object. The scanner is consistent with the requirements of airport checkpoint lanes and can run inline with existing transmission-based X-ray scanners.

Classification-free threat detection based on material-science-informed clustering

X-ray diffraction (XRD) is well-known for yielding composition and structural information about a material. However, in some applications (such as threat detection in aviation security), the properties of a material are more relevant to the task than is a detailed material characterization. Furthermore, the requirement that one first identify a material before determining its class may be difficult or even impossible for a sufficiently large pool of potentially present materials. We therefore seek to learn relevant composition-structure-property relationships between materials to enable material-identification-free classification. We use an expert-informed, data-driven approach operating on a library of XRD spectra from a broad array of stream of commerce materials. We investigate unsupervised learning techniques in order to learn about naturally emergent groupings, and apply supervised learning techniques to determine how well XRD features can be used to separate user-specified classes in the presence of different types and degrees of signal degradation.

Spectral feature variations in x-ray diffraction imaging systems

Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

High Voltage Compatible Micromachined Vacuum Electronic Devices with Carbon Nanotube Cold Cathodes

Previously, Bower et al. demonstrated a microfabricaied vacuum microtriode with an integrated carbon nanotube field emission cathode. One of the main limitations of the microtriode was the inadequate device insulation which suffered from leakage currents and electrical breakdown at fewer than 200 Volts. Here, we report similar micromachined vacuum electronic devices fabricated to withstand voltages in excess of 1000 Volts. The DC electrical characteristics of a microtriode were obtained at grid and anode voltage levels significantly higher than previously reported. The field emission performance of the MPECVD grown carbon nanotube cold cathode is described. Implications for future on-chip microfabricated vacuum electronic devices were discussed