<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Analytical Science | Surface Analysis section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/analytical-science/sections/surface-analysis</link>
        <description>RSS Feed for Surface Analysis section in the Frontiers in Analytical Science journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-15T16:23:21.687+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frans.2025.1512520</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frans.2025.1512520</link>
        <title><![CDATA[The variability in hydrocarbon ions (CnH−) of polymers detected by ToF-SIMS: principal component analysis on carbon density and cross-linking degree]]></title>
        <pubdate>2025-02-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Heng-Yong Nie</author>
        <description><![CDATA[Negative hydrocarbon ions, CnH− (n = 1–10), are ubiquitous in time-of-flight secondary ion mass spectrometry, but their utility may have been overlooked. Recently, however, it has been demonstrated that the ion intensity ratio between C6H− and C4H−, denoted as ρ, can differentiate the chemical structures of polymers such as polyethylene, polypropylene, polyisoprene and polystyrene, as well as depth profile the cross-linking degree of poly (methyl methacrylate). It was found that ρ increases with the carbon density of polymers. Principal component analysis (PCA), a dimensionality reduction technique, can reveal hidden data structures through exploring the relationships among the CnH− intensities for the four polymers. Assisted by the biplot approach, PCA is key to uncovering hidden data structures, from which characteristic ions may be identifiable and their relationships classifiable. The four polymers were classified by their carbon densities, which dictate the variability of CnH− intensities and are captured by the first principal component (PC1). It also became clear that PC1 is correlated with ρ. This data-driven analytical approach is imperative when differentiating chemicals with similar structures, especially when diagnostic ions are lacking. We demonstrate the usefulness of this approach by examining poly (methyl methacrylate) with different degrees of cross-linking.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frans.2024.1509438</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frans.2024.1509438</link>
        <title><![CDATA[Perspective on the development of XPS and the pioneers who made it possible]]></title>
        <pubdate>2025-01-22T00:00:00Z</pubdate>
        <category>Review</category>
        <author>D. R. Baer</author><author>P. M. A. Sherwood</author>
        <description><![CDATA[As of 2024, the use of X-photoelectron spectroscopy (XPS), initially called Electron Spectroscopy for Chemical Analysis (ESCA), has grown to become the most widely used surface analysis method. In this paper we offer a perspective of the early development of XPS and describe some of the advances and pioneers who made them that provided the foundation for it to grow into the technique we know today. Included is information about the early development of photoelectron spectroscopy, the seminal work of Kai Siegbahn, influential conferences that helped spread excitement and provide a fundamental understanding of the method, early development of commercial instruments, and identification of the need for systematic metrology. Because hundreds of researchers have contributed to advancing the method, we note that this is our perspective, with likely a different emphasis than others may have chosen. To limit the scope somewhat, we have chosen to focus on authors whose contributions started before 1980.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frans.2023.1234943</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frans.2023.1234943</link>
        <title><![CDATA[Challenges in surface analysis]]></title>
        <pubdate>2023-06-29T00:00:00Z</pubdate>
        <category>Specialty Grand Challenge</category>
        <author>John T. Grant</author>
        <description></description>
      </item>
      </channel>
    </rss>