Elemental Scientific, Inc.

Samsung Electronics Co., Ltd.

Cole J Nardini

Daniel R Wiederin

Austin Schultz

Jaeyoung Kim

Sooyeon Kim

So Young Kim

Kyle W Uhlmeyer

David Diaz

Myung Hwan Kim

Min-Soo Suh

Daesung Kim

Junghee Shin

Suyeon Jeong

Systems and methods for iterative removal of outlier data from spectrometry data to determine one or more of a particle baseline and a detection threshold for nanoparticles are described. Ion signal intensity values that exceed an outlier threshold value associated with a sum of a first multiple of an average of the count distribution of ion signal intensity and a first multiple of a standard deviation of the count distribution of ion signal intensity are iteratively removed from the raw data set until no outliers remain, providing a background data set. A nanoparticle baseline intensity value is set as a sum of a second multiple of an average of the background data set and a second multiple of a standard deviation of the background data set to differentiate between signal intensity values that are associated with background interference and that are associated with the presence of nanoparticles in the sample.

Nanoparticle baseline and particle detection threshold determination through iterative outlier removal

Systems and methods for analyzing ultra-low level concentration nanoparticles in semiconductor cleaning chemicals by single particle inductively coupled plasma mass spectrometry (spICP-MS) are described. In aspects, the methods described herein include determination factors that are specific to each of the chemical matrix and the type of analyte associated with the nanoparticles present in a fluid sample for both a particle baseline intensity value and a nanoparticle detection threshold.

Nanoparticle analysis for ultra-low level concentrations of nanoparticles in fluid samples

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Omaha, NE, United States of America

Cole J Nardini