Fitting the AFM force–distance curves the correct way

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00177016%3A_____%2F24%3AN0000137" target="_blank" >RIV/00177016:_____/24:N0000137 - isvavai.cz</a>

Výsledek na webu

<a href="https://iopscience.iop.org/article/10.1088/1361-6501/ad8b60" target="_blank" >https://iopscience.iop.org/article/10.1088/1361-6501/ad8b60</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1361-6501/ad8b60" target="_blank" >10.1088/1361-6501/ad8b60</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fitting the AFM force–distance curves the correct way

Popis výsledku v původním jazyce

Data fitting is an indispensable tool in modern metrology. However, as the models become more and more complex the most popular method, ordinary least squares regression, reaches its limit. As the relative uncertainty in the independent variable increases, we can no longer speak about an exactly known independent variable and an uncertain dependent variable. The increasing complexity of the measurement process may give rise to correlationsFurthermore correlations between data may become non negligible: typical sources are e.g. the use of reference samples or crosstalk between sensors. These problems can be treated with generalized least squares. A new algorithm–Optimum Estimate of Function Parameters by Iterated Linearization (OEFPIL) – has been recently suggested which can handle both a wide class of functions as well as general covariance matrices. We illustrate its application in the analysis of force distance curves in AFM which are used to evaluate the mechanical properties of samples such as the Young's modulus and adhesion. In this work we apply the new algorithm and compare the results to other methods. The uncertainties obtained by OEFPIL are in good agreement with uncertainties obtained by the Monte Carlo method but can be obtained in a more straightforward way.

Název v anglickém jazyce

Fitting the AFM force–distance curves the correct way

Popis výsledku anglicky

Data fitting is an indispensable tool in modern metrology. However, as the models become more and more complex the most popular method, ordinary least squares regression, reaches its limit. As the relative uncertainty in the independent variable increases, we can no longer speak about an exactly known independent variable and an uncertain dependent variable. The increasing complexity of the measurement process may give rise to correlationsFurthermore correlations between data may become non negligible: typical sources are e.g. the use of reference samples or crosstalk between sensors. These problems can be treated with generalized least squares. A new algorithm–Optimum Estimate of Function Parameters by Iterated Linearization (OEFPIL) – has been recently suggested which can handle both a wide class of functions as well as general covariance matrices. We illustrate its application in the analysis of force distance curves in AFM which are used to evaluate the mechanical properties of samples such as the Young's modulus and adhesion. In this work we apply the new algorithm and compare the results to other methods. The uncertainties obtained by OEFPIL are in good agreement with uncertainties obtained by the Monte Carlo method but can be obtained in a more straightforward way.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Measurement Science and Technology

ISSN

0957-0233

e-ISSN

1361-6501

Svazek periodika

36

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

—

Kód UT WoS článku

001353756500001

EID výsledku v databázi Scopus

2-s2.0-85219424350