<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data</dc:title><dc:creator>Hamburger, Robert; Rumble, Christopher; Young, Elizabeth R.</dc:creator><dc:corporate_author/><dc:editor/><dc:description>Transient absorption (TA) spectroscopy is a powerful time-resolved spectroscopic
method used to track the evolution of excited-state processes through changes in
the system's absorption spectrum. Early implementations of TA were confined to
specialized laboratories, but the evolution of commercial turn-key systems has made
the technique increasingly available to research groups across the world. Modern TA
systems are capable of producing large datasets with high energetic and temporal
resolution that are rich in photophysical information. However, processing, fitting, and
interpreting TA spectra can be challenging due to the large number of excited-state
features and instrumental artifacts. Many factors must be carefully considered when
collecting, processing, and fitting TA data in order to reduce uncertainty over which
model or set of fitting parameters best describes the data. The goal of data preparation
and fitting is to reduce as many of these extraneous factors while preserving the data
for analysis. In this method, beginners are provided with a protocol for processing and
preparing TA data as well as a brief introduction to selected fitting procedures and
models, specifically single wavelength fitting and global lifetime analysis. Commentary
on a number of commonly encountered data preparation challenges and methods of
addressing them is provided, followed by a discussion of the challenges and limitations
of these simple fitting methods.</dc:description><dc:publisher>Journal of Visualized Experiments</dc:publisher><dc:date>2024-02-16</dc:date><dc:nsf_par_id>10500130</dc:nsf_par_id><dc:journal_name>Journal of Visualized Experiments</dc:journal_name><dc:journal_volume/><dc:journal_issue>204</dc:journal_issue><dc:page_range_or_elocation>1-27</dc:page_range_or_elocation><dc:issn>1940-087X</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.3791/65519</dc:doi><dcq:identifierAwardId>2313290</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>