Data mining for isotope discrimination in atom probe tomography

Large-Scale Atom Probe Tomography Data Mining: Methods and Application to Inform Hydrogen Behavior

A large number of atom probe tomography (APT) datasets from past experiments were collected into a database to conduct statistical analyses. An effective way of handling the data is shown, and a study on hydrogen is conducted to illustrate the usefulness of this approach. We propose to handle a large collection of APT spectra as a point cloud and use a city block distance-based metric to measure dissimilarity between spectra. This enables quick and automated searching for spectra by similarity. Since spectra from APT experiments on similar materials are similar, the point cloud of spectra contains clusters. Analysis of these clusters of spectra in this point cloud allows us to infer the sample materials. The behavior of contaminant hydrogen is analyzed and correlated with voltage, electric field, and sample base material. Across several materials, the H2+ /H+ ratio is found to decrease with increasing field, likely an indication of postionization of H2+ ions. The absolute amounts of H2+ and H+ are found to frequently increase throughout APT experiments.

RNA Nanovaccine Protects against White Spot Syndrome Virus in Shrimp

In the last 15 years, crustacean fisheries have experienced billions of dollars in economic losses, primarily due to viral diseases caused by such pathogens as white spot syndrome virus (WSSV) in the Pacific white shrimp Litopenaeus vannamei and Asian tiger shrimp Penaeus monodon. To date, no effective measures are available to prevent or control disease outbreaks in these animals, despite their economic importance. Recently, double-stranded RNA-based vaccines have been shown to provide specific and robust protection against WSSV infection in cultured shrimp. However, the limited stability of double-stranded RNA is the most significant hurdle for the field application of these vaccines with respect to delivery within an aquatic system. Polyanhydride nanoparticles have been successfully used for the encapsulation and release of vaccine antigens. We have developed a double-stranded RNA-based nanovaccine for use in shrimp disease control with emphasis on the Pacific white shrimp L. vannamei. Nanoparticles based on copolymers of sebacic acid, 1,6-bis(p-carboxyphenoxy)hexane, and 1,8-bis(p-carboxyphenoxy)-3,6-dioxaoctane exhibited excellent safety profiles, as measured by shrimp survival and histological evaluation. Furthermore, the nanoparticles localized to tissue target replication sites for WSSV and persisted through 28 days postadministration. Finally, the nanovaccine provided ~80% protection in a lethal WSSV challenge model. This study demonstrates the exciting potential of a safe, effective, and field-applicable RNA nanovaccine that can be rationally designed against infectious diseases affecting aquaculture.

Development of an Energy-Sensitive Detector for the Atom Probe Tomography

A position and energy-sensitive detector has been developed for atom probe tomography (APT) instruments in order to deal with some mass peak overlap issues encountered in APT experiments. Through this new type of detector, quantitative and qualitative improvements could be considered for critical materials with mass peak overlaps, such as nitrogen and silicon in TiSiN systems, or titanium and carbon in cemented carbide materials. This new detector is based on a thin carbon foil positioned on the front panel of a conventional MCP-DLD detector. According to several studies, it has been demonstrated that the impact of ions on thin carbon foils has the effect of generating a number of transmitted and reflected secondary electrons. The number generated mainly depends on both the kinetic energy and the mass of incident particles. Despite the fact that this phenomenon is well known and has been widely discussed for decades, no studies have been performed to date for using it as a means to discriminate particles energy. Therefore, this study introduces the first experiments on a potential new generation of APT detectors that would be able to resolve mass peak overlaps through the energy-sensitivity of thin carbon foils.

Combination Nanovaccine Demonstrates Synergistic Enhancement in Efficacy against Influenza

H5N1 influenza virus has the potential to become a significant global health threat, and next generation vaccine technologies are needed. In this work, the combined efficacy of two nanoadjuvant platforms (polyanhydride nanoparticles and pentablock copolymer-based hydrogels) to induce protective immunity against H5N1 influenza virus was examined. Mice received two subcutaneous vaccinations (day 0 and 21) containing 10 μg of H5 hemagglutinin trimer alone or in combination with the nanovaccine platforms. Nanovaccine immunization induced high neutralizing antibody titers that were sustained through 70 days postimmunization. Finally, mice were intranasally challenged with A/H5N1 VNH5N1-PR8CDC-RG virus and monitored for 14 days. Animals receiving the combination nanovaccine had lower viral loads in the lung and weight loss after challenge in comparison to animals vaccinated with each platform alone. These data demonstrate the synergy between polyanhydride nanoparticles and pentablock copolymer-based hydrogels as adjuvants in the design of a more efficacious influenza vaccine.

Mining information from atom probe data

Whilst atom probe tomography (APT) is a powerful technique with the capacity to gather information containing hundreds of millions of atoms from a single specimen, the ability to effectively use this information creates significant challenges. The main technological bottleneck lies in handling the extremely large amounts of data on spatial-chemical correlations, as well as developing new quantitative computational foundations for image reconstruction that target critical and transformative problems in materials science. The power to explore materials at the atomic scale with the extraordinary level of sensitivity of detection offered by atom probe tomography has not been not fully harnessed due to the challenges of dealing with missing, sparse and often noisy data. Hence there is a profound need to couple the analytical tools to deal with the data challenges with the experimental issues associated with this instrument. In this paper we provide a summary of some key issues associated with the challenges, and solutions to extract or "mine" fundamental materials science information from that data.

Extracting features buried within high density atom probe point cloud data through simplicial homology

Feature extraction from Atom Probe Tomography (APT) data is usually performed by repeatedly delineating iso-concentration surfaces of a chemical component of the sample material at different values of concentration threshold, until the user visually determines a satisfactory result in line with prior knowledge. However, this approach allows for important features, buried within the sample, to be visually obscured by the high density and volume (~10(7) atoms) of APT data. This work provides a data driven methodology to objectively determine the appropriate concentration threshold for classifying different phases, such as precipitates, by mapping the topology of the APT data set using a concept from algebraic topology termed persistent simplicial homology. A case study of Sc precipitates in an Al-Mg-Sc alloy is presented demonstrating the power of this technique to capture features, such as precise demarcation of Sc clusters and Al segregation at the cluster boundaries, not easily available by routine visual adjustment.

Informatics derived materials databases for multifunctional properties

In this review, we provide an overview of the development of quantitative structure-property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure-property relationships; and even more importantly we are now in a position to build materials databases based on design 'intent' and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done. This review provides a summary of the computational logic of building such virtual databases and gives some examples in the field of complex inorganic solids for scintillator applications.