Item Details

abstract

The combination of (trace) elemental analysis with data science techniques is an important contribution to atomic spectrometry and its application to a broad range of research fields. With temperatures as high as 10,000 K, the inductively coupled plasma (ICP) is a robust source of atomization, excitation and ionization, capable of providing trace level information for the vast majority of elements on the periodic table. Recently, data science has catalyzed research across science by helping to process and interpret large amounts of complex data acquired from instrumental techniques. The proposed body of work shows the merit of using data-driven algorithms to enhance the analytical capabilities of modern atomic spectrometry in two ways: (i) furthering the interpretation and use of data acquired using traditional calibration methods for environmental and health-related applications, and (ii) identifying and resolving matrix effects that can compromise analyses based on the traditional external standard calibration method (EC).

subject

Drinking water

Inductively coupled plasma

Lead

Machine learning

Matrix effects

Non-invasive diagnostics

contributor

Carter, Jake Alexander (author)

Jones, Bradley T (committee chair)

Calloway, Clifton P (committee member)

Colyer, Christa L (committee member)

Geyer, Scott M (committee member)

King, Stephen B (committee member)

date

2020-08-28T08:35:23Z (accessioned)

2020-08-28T08:35:23Z (available)

2020 (issued)

degree

Chemistry (discipline)

identifier

http://hdl.handle.net/10339/96944 (uri)

language

en (iso)

publisher

Wake Forest University

title

Bridging the Gap Between Chemical Analysis and Applied Statistics to Expand the Applications of Atomic Spectrometric Techniques

type

Dissertation