Georgia Tech: Better nanomeasurements with new stats analysis

July 2, 2009: Researchers at Georgia Tech have developed a new statistical analysis technique that could lead to more precise and reliable measurements of nanomaterials and nanostructures.

The technique, “sequential profile adjustment by regression” (SPAR), identifies and removes system bias, noise, and equipment-based artifacts, which at the nanoscale may be only slightly weaker than true signals of interest. It also can help reduce the amount of experimental data required to make conclusions, and help distinguish true nanoscale phenomena from experimental error.

“At the nanoscale, small errors are amplified,” explained Zhong Lin Wang from Georgia Tech’s School of Materials Science and Engineering. “This new technique applies statistical theory to identify and analyze the data received from nanomechanics so we can be more confident of how reliable it is.”

Specifically, the research focused on a data set measuring the deformation of zinc oxide nanobelts, to determine the material’s elastic modulus. Theoretically, applying force to a nanobelt with the tip of an atomic force microscope should produce consistent linear deformation — but their experimental data showed that sometimes less force appeared to create more deformation and the deformation curve was not symmetrical, and simple data-correction techniques didn’t solve the mystery. “The measurements they had done simply didn’t match what was expected with the theoretical model,” noted Georgia Tech prof. C.F. Jeff Wu. The new modeling technique “uses the data itself to filter out the mismatch step-by-step using the regression technique,” he said.

Georgia Tech researchers illustrate how their new technique improves measurement of nanostructure properties, in this case a graph of elastic modulus of nanobelts. (Georgia Tech Photo: Gary Meek)

In addition to correcting the errors, the technique’s precision could make it easier to produce reliable experimental data on nanostructure properties. “With half of the experimental efforts, you can get about the same standard deviation as following the earlier method without the corrections,” Wu stated. The technique also targets industrial manufacturing environments — i.e., commercialization — “because industrial users cannot afford to make a detailed study of every production run […] the significant experimental errors can be filtered out automatically,” Wu noted.

Future work will target the statistical technique to analysis of the properties of nanowires, whose structure will require a separate model using the same SPAR techniques to correct data errors, Wu noted. The technique also will be applied to past research to possibly generate new findings. “What may have seemed like noise could actually be an important signal,” Wang said. “This technique provides a truly new tool for data mining and analysis in nanotechnology.”

The research, sponsored by the NSF, was published June 25 by the journal Proceedings of the National Academy of Sciences.

SEM images showing a zinc oxide nanobelt on a trenched substrate. An atomic force microscope tip was scanned along the length of the nanobelt with a constant force applied. A series of such scans with the application of different forces produced a bending profile of the nanobelt. These bending measurements were then evaluated using the new SPAR technique to provide information on the nanostructure’s elastic modulus. (Images courtesy of Zhong Lin Wang/Georgia Tech)


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