Progress in High Resolution Scanning Ion Microscopy and Secondary Ion Mass Spectrometry Imaging Microanalysis

The performance of a new high resolution scanning ion microprobe (SIM) is elucidated with regard to imaging capabilities using the ion-induced secondary electron (ISE) or secondary ion (ISI) signals, and the mass-resolved signal from a secondary ion mass spectrometry (SIMS) system. The new instrument focuses a beam extracted from a liquid metal ion source (LMIS) to a range of spot sizes reaching the 20 nm level. The probe current (1.6 pA) available at this level of lateral resolution, which approaches the theoretical resolution limits of the SIMS method, is still adequate to obtain detailed isotopic maps for surfaces rich in the elements of low ionization potential (positive ISI), or high electron affinity (negative ISI). In addition to examples of high resolution ISE and ISI images of objects displaying sufficiently small topographic detail, mass spectra and isotopic maps are shown, testing both the lateral and depth resolution attained. The latter results belong with a program of interdisciplinary research applications of the new microprobe, which include studies of e.g., the monolayer lateral distribution of intercalant in SbCl5 intercalated graphite and of silicate minerals and iron distribution in sections of chondrules and their rims (components of chondrites, a class of stoney meteorites). In the biomedical field, the new microprobe finds application in e.g., the study of human renal calculi and bone. Most promising is the use of stable isotope tracers (e.g., Ca44) to unravel the dynamics of bone mineralization, as thus far shown with the in-vitro culture of the skull bone of neonatal mice

Dendritic Oxide Growth on the Surface of Liquid Gallium

We have studied the oxidation of a liquid gallium surface with a high spatial resolution scanning ion microprobe. A 40 keV focused gallium ion beam, extracted from a liquid metal ion source, was employed, first, to sputter clean a 40 x 40 μm2 area on a drop of liquid gallium, in a ultra high vacuum (UHV) specimen chamber. It was then used to monitor the oxide growth by secondary ion mass spectrometry imaging microanalysis while the chamber was gradually back-filled with oxygen. In the initial stages, gallium oxide grew in a dendritic pattern from the edge of the cleaned area where oxide preexisted. Gradually the oxide layer grew in thickness and covered the entire area leaving only small islands and channels uncovered. Computer simulations based on aggregation of two dimensional random walkers (or diffusion limited aggregation) show similar dendritic patterns in the initial stages of growth. The similarity is also reflected by their comparable fractal dimensions. For the final stages, qualitative discrepancies between the experimental and simulated patterns are discernible

A Proposal for a High Resolution Scanning Ion Microprobe Based on Laser Non-Resonant Post-Ionization of Sputtered Atoms

A new high resolution scanning ion microprobe (SIM) is described which combines laser non-resonant multiphoton ionization (MPI) and time of flight (ToF) spectroscopy. The proposed instrument is designed to overcome limitations of the conventional secondary ion mass spectrometry (SIMS) method. A pulsed ion probe (with current 1 - 100 pA) is extracted from a liquid metal ion source (LMIS). This beam is purified by a Wien filter, focused to a spot (15 - 150 nm), and scanned across a sample in a raster pattern (512 x 512 pixels). A high power (200 mJ, 193 nm, 500 Hz) ArF pulsed laser -an off-axis ellipsoidal reflector is planned to boost its power density - intercepts the sputtered neutrals, ionizing a large fraction for detection. The resultant ions are collected by a spherical sector energy analyzer and mass resolved by either a reflectron or Poschenrieder type ToF spectrometer. The laser pulse defines the time base for the spectrometer; mass resolution of more than 3000 is feasible. Detailed calculations of the neutrals\u27 sputtering and photoionization yields are given. In particular, an analytical expression for two-photon ionization of sputtered atoms, which optimizes the photon-neutrals overlap, is derived and results plotted. This technique, post-ionization of sputtered atoms (PISA), is shown to permit several high statistical accuracy, high mass and lateral resolution images to be obtained simultaneously, even for elements with high ionization potential or low electron affinity, elements difficult to examine with SIMS. Compared to SIMS, PISA greatly reduces the range of the variation in detected signal as a function of atomic number, facilitating quantification

The resolved fraction of the Cosmic X-ray Background

We present the X-ray source number counts in two energy bands (0.5-2 and 2-10 keV) from a very large source sample: we combine data of six different surveys, both shallow wide field and deep pencil beam, performed with three different satellites (ROSAT, Chandra and XMM-Newton). The sample covers with good statistics the largest possible flux range so far: [2.4*10^-17 - 10^-11] cgs in the soft band and [2.1*10^-16 - 8*10^{-12}]cgs in the hard band. Integrating the flux distributions over this range and taking into account the (small) contribution of the brightest sources we derive the flux density generated by discrete sources in both bands. After a critical review of the literature values of the total Cosmic X--Ray Background (CXB) we conclude that, with the present data, the 94.3%, and 88.8% of the soft and hard CXB can be ascribed to discrete source emission. If we extrapolate the analytical form of the Log N--Log S distribution beyond the flux limit of our catalog in the soft band we find that the flux from discrete sources at ~3*10^-18 cgs is consistent with the entire CXB, whereas in the hard band it accounts for only 93% of the total CXB at most, hinting for a faint and obscured population to arise at even fainter fluxes.Comment: Accepted for publication in Ap

Cosmic rays and Radio Halos in galaxy clusters : new constraints from radio observations

Clusters of galaxies are sites of acceleration of charged particles and sources of non-thermal radiation. We report on new constraints on the population of cosmic rays in the Intra Cluster Medium (ICM) obtained via radio observations of a fairly large sample of massive, X-ray luminous, galaxy clusters in the redshift interval 0.2--0.4. The bulk of the observed galaxy clusters does not show any hint of Mpc scale synchrotron radio emission at the cluster center (Radio Halo). We obtained solid upper limits to the diffuse radio emission and discuss their implications for the models for the origin of Radio Halos. Our measurements allow us to derive also a limit to the content of cosmic ray protons in the ICM. Assuming spectral indices of these protons delta =2.1-2.4 and microG level magnetic fields, as from Rotation Measures, these limits are one order of magnitude deeper than present EGRET upper limits, while they are less stringent for steeper spectra and lower magnetic fields.Comment: 14 pages, 5 figures, ApJ Letter, accepte

Resonance parameters from K-matrix and T-matrix poles

We extract K-matrix poles from our fits to elastic pion-nucleon scattering and eta-nucleon production data in order to test a recently proposed method for the determination of resonance properties, based on the trace of the K-matrix. We have considered issues associated with the separation of background and resonance contributions, the correspondence between K-matrix and T-matrix poles, and the complicated behavior of eigenphases.Comment: 11 pages, 1 figur

Low-power fixed-point compressed sensing decoder with support oracle

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample

Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices

Subspace analysis is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal processing tasks. However, traditional subspace analysis often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing subspace analysis to be run on low-power devices such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices