@ARTICLE{Xu2023-ch,
  title    = "Correlating local chemical and structural order using Geographic
              Information Systems-based spatial statistics",
  author   = "Xu, Michael and Kumar, Abinash and LeBeau, James M",
  journal  = "Ultramicroscopy",
  volume   =  243,
  pages    =  113642,
  abstract = "Analysis of nanoscale short-range chemical and/or structural order
              via (scanning) transmission electron microscopy (S/TEM) imaging is
              fundamentally limited by projection of the three dimensional
              sample, which averages informational along the beam direction.
              Extracting statistically significant spatial correlations between
              the structure and chemistry determined from these two-dimensional
              datasets thus remains challenging. Here, we apply methods commonly
              used in Geographic Information Systems (GIS) to determine the
              spatial correlation between measures of local chemistry and
              structure from atomic-resolution STEM imaging of a compositionally
              complex relaxor, Pb(Mg1/3Nb2/3)O3 (PMN). The approach is used to
              determine the type of ordering present and to quantify the spatial
              variation of chemical order, oxygen octahedral distortions, and
              oxygen octahedral tilts. The extent of autocorrelation and
              inter-feature correlation among these short-range ordered regions
              are then evaluated through a spatial covariance analysis, showing
              correlation as a function of distance. The results demonstrate
              that integrating GIS tools for analyzing microscopy datasets can
              serve to unravel subtle relationships among chemical and
              structural features in complex materials that can be hidden when
              ignoring their spatial distributions.",
  month    =  jan,
  year     =  2023,
  keywords = "Geographic Information Systems; Scanning transmission electron
              microscopy; Short-range order;LeBeau Group",
  doi      = "10.1016/j.ultramic.2022.113642",
  pmid     =  36403389
}

{"_id":"gwZAbQsTE8JNidAiP","bibbaseid":"xu-kumar-lebeau-correlatinglocalchemicalandstructuralorderusinggeographicinformationsystemsbasedspatialstatistics-2023","author_short":["Xu, M.","Kumar, A.","LeBeau, J. M"],"bibdata":{"bibtype":"article","type":"article","title":"Correlating local chemical and structural order using Geographic Information Systems-based spatial statistics","author":[{"propositions":[],"lastnames":["Xu"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Kumar"],"firstnames":["Abinash"],"suffixes":[]},{"propositions":[],"lastnames":["LeBeau"],"firstnames":["James","M"],"suffixes":[]}],"journal":"Ultramicroscopy","volume":"243","pages":"113642","abstract":"Analysis of nanoscale short-range chemical and/or structural order via (scanning) transmission electron microscopy (S/TEM) imaging is fundamentally limited by projection of the three dimensional sample, which averages informational along the beam direction. Extracting statistically significant spatial correlations between the structure and chemistry determined from these two-dimensional datasets thus remains challenging. Here, we apply methods commonly used in Geographic Information Systems (GIS) to determine the spatial correlation between measures of local chemistry and structure from atomic-resolution STEM imaging of a compositionally complex relaxor, Pb(Mg1/3Nb2/3)O3 (PMN). The approach is used to determine the type of ordering present and to quantify the spatial variation of chemical order, oxygen octahedral distortions, and oxygen octahedral tilts. The extent of autocorrelation and inter-feature correlation among these short-range ordered regions are then evaluated through a spatial covariance analysis, showing correlation as a function of distance. The results demonstrate that integrating GIS tools for analyzing microscopy datasets can serve to unravel subtle relationships among chemical and structural features in complex materials that can be hidden when ignoring their spatial distributions.","month":"January","year":"2023","keywords":"Geographic Information Systems; Scanning transmission electron microscopy; Short-range order;LeBeau Group","doi":"10.1016/j.ultramic.2022.113642","pmid":"36403389","bibtex":"@ARTICLE{Xu2023-ch,\n title = \"Correlating local chemical and structural order using Geographic\n Information Systems-based spatial statistics\",\n author = \"Xu, Michael and Kumar, Abinash and LeBeau, James M\",\n journal = \"Ultramicroscopy\",\n volume = 243,\n pages = 113642,\n abstract = \"Analysis of nanoscale short-range chemical and/or structural order\n via (scanning) transmission electron microscopy (S/TEM) imaging is\n fundamentally limited by projection of the three dimensional\n sample, which averages informational along the beam direction.\n Extracting statistically significant spatial correlations between\n the structure and chemistry determined from these two-dimensional\n datasets thus remains challenging. Here, we apply methods commonly\n used in Geographic Information Systems (GIS) to determine the\n spatial correlation between measures of local chemistry and\n structure from atomic-resolution STEM imaging of a compositionally\n complex relaxor, Pb(Mg1/3Nb2/3)O3 (PMN). The approach is used to\n determine the type of ordering present and to quantify the spatial\n variation of chemical order, oxygen octahedral distortions, and\n oxygen octahedral tilts. The extent of autocorrelation and\n inter-feature correlation among these short-range ordered regions\n are then evaluated through a spatial covariance analysis, showing\n correlation as a function of distance. The results demonstrate\n that integrating GIS tools for analyzing microscopy datasets can\n serve to unravel subtle relationships among chemical and\n structural features in complex materials that can be hidden when\n ignoring their spatial distributions.\",\n month = jan,\n year = 2023,\n keywords = \"Geographic Information Systems; Scanning transmission electron\n microscopy; Short-range order;LeBeau Group\",\n doi = \"10.1016/j.ultramic.2022.113642\",\n pmid = 36403389\n}\n\n","author_short":["Xu, M.","Kumar, A.","LeBeau, J. M"],"key":"Xu2023-ch","id":"Xu2023-ch","bibbaseid":"xu-kumar-lebeau-correlatinglocalchemicalandstructuralorderusinggeographicinformationsystemsbasedspatialstatistics-2023","role":"author","urls":{},"keyword":["Geographic Information Systems; Scanning transmission electron microscopy; Short-range order;LeBeau Group"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://paperpile.com/eb/hvQdZzcQAp","dataSources":["T6bwdcdAx2jmtGv5a"],"keywords":["geographic information systems; scanning transmission electron microscopy; short-range order;lebeau group"],"search_terms":["correlating","local","chemical","structural","order","using","geographic","information","systems","based","spatial","statistics","xu","kumar","lebeau"],"title":"Correlating local chemical and structural order using Geographic Information Systems-based spatial statistics","year":2023}