Pith. sign in

REVIEW 1 cited by

Determination of Calibration Parameters of Cantilevers of Arbitrary Shape by Finite Elements Analysis

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2010.12451 v5 pith:UF43I2WB submitted 2020-10-23 physics.app-ph

Determination of Calibration Parameters of Cantilevers of Arbitrary Shape by Finite Elements Analysis

classification physics.app-ph
keywords cantilevercantileverscorrectionfactorsgeometriesmethodanalysisarbitrary
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The use of atomic force microscopy on nanomechanical measurements requires accurate calibration of the cantilever's spring constant ($k_c$) and the optical lever sensitivity ($OLS$). The thermal method, based on the cantilever's thermal fluctuations in fluid, allows estimating $k_c$ in a fast, non-invasive mode. However, differences in the cantilever geometry and mounting angle require the knowledge of three correction factors to get a good estimation of $k_c$: the contribution of the oscillation mode to the total amplitude, the shape difference between the free and the end-loaded configurations, and the tilt of the cantilever respect to the measured surface. While the correction factors for traditional rectangular and V-shaped cantilevers geometries have been reported, they must be determined for cantilevers with non-traditional geometries and large tips. Here, we develop a method based on finite element analysis to estimate the correction factors of cantilevers with arbitrary geometry and tip dimensions. The method relies on the numerical computation of the effective cantilever mass. The use of the correction factor for rectangular geometries on our model cantilever (PFQNM-LC) will lead to values underestimated by 16%. In contrast, experiments using pre-calibrated cantilevers revealed a maximum uncertainty below 5% in the estimation of the $OLS$, verifying our approach.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Comparative Analysis of Liquid Neural Networks and LSTM for Sequential Pattern Recognition: Robustness, Efficiency, and Clinical Utility

    cs.LG 2026-05 unverdicted novelty 2.0

    Benchmarking study reports that Closed-form Continuous-time Liquid Neural Networks outperform LSTMs in parameter efficiency and robustness to temporal dropout on neuromorphic, drawing, handwriting, and sepsis predicti...