Dataset Information

Reducing the Matrix Effect in Organic Cluster SIMS Using Dynamic Reactive Ionization.

ABSTRACT: Dynamic reactive ionization (DRI) utilizes a reactive molecule, HCl, which is doped into an Ar cluster projectile and activated to produce protons at the bombardment site on the cold sample surface with the presence of water. The methodology has been shown to enhance the ionization of protonated molecular ions and to reduce salt suppression in complex biomatrices. In this study, we further examine the possibility of obtaining improved quantitation with DRI during depth profiling of thin films. Using a trehalose film as a model system, we are able to define optimal DRI conditions for depth profiling. Next, the strategy is applied to a multilayer system consisting of the polymer antioxidants Irganox 1098 and 1010. These binary mixtures have demonstrated large matrix effects, making quantitative SIMS measurement not feasible. Systematic comparisons of depth profiling of this multilayer film between directly using GCIB, and under DRI conditions, show that the latter enhances protonated ions for both components by 4- to ~15-fold, resulting in uniform depth profiling in positive ion mode and almost no matrix effect in negative ion mode. The methodology offers a new strategy to tackle the matrix effect and should lead to improved quantitative measurement using SIMS. Graphical Abstract ?.

SUBMITTER: Tian H

PROVIDER: S-EPMC5218814 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Reducing the Matrix Effect in Organic Cluster SIMS Using Dynamic Reactive Ionization.

Tian Hua H Wucher Andreas A Winograd Nicholas N

Journal of the American Society for Mass Spectrometry 20160922 12

Dynamic reactive ionization (DRI) utilizes a reactive molecule, HCl, which is doped into an Ar cluster projectile and activated to produce protons at the bombardment site on the cold sample surface with the presence of water. The methodology has been shown to enhance the ionization of protonated molecular ions and to reduce salt suppression in complex biomatrices. In this study, we further examine the possibility of obtaining improved quantitation with DRI during depth profiling of thin films. U ...[more]

PMID: 27659034

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Project description:The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster.We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data.When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging.We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Dataset Information

Reducing the Matrix Effect in Organic Cluster SIMS Using Dynamic Reactive Ionization.

Publications

Reducing the Matrix Effect in Organic Cluster SIMS Using Dynamic Reactive Ionization.

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