Evidence map›Paper›PMID 25665933›Full record

ArticleNature communications2015

Improving image contrast and material discrimination with nonlinear response in bimodal atomic force microscopy.

Daniel Forchheimer, Robert Forchheimer, David B Haviland

Abstract read
In one paragraph

Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  3. Incongruous Harmonics of Vibrating Solid-Solid Interface.Small (Weinheim an der Bergstrasse, Germany) · 2025
    Article
  4. Review
  5. Tailored Microcantilever Optimization for Multifrequency Force Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daniel ForchheimerDepartment of Applied Physics, Section for Nanostructure Physics, Royal Institute of Technology (KTH), SE-106 91 Stockholm, Sweden.
Robert ForchheimerDivision of Information Coding, Department of Electrical Engineering, Linköping University, SE- 581 83 Linköping, Sweden.
David B HavilandDepartment of Applied Physics, Section for Nanostructure Physics, Royal Institute of Technology (KTH), SE-106 91 Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atomic force microscopy has recently been extented to bimodal operation, where increased image contrast is achieved through excitation and measurement of two cantilever eigenmodes. This enhanced material contrast is advantageous in analysis of complex heterogeneous materials with phase separation on the micro or nanometre scale. Here we show that much greater image contrast results from analysis of nonlinear response to the bimodal drive, at harmonics and mixing frequencies. The amplitude and phase of up to 17 frequencies are simultaneously measured in a single scan. Using a machine-learning algorithm we demonstrate almost threefold improvement in the ability to separate material components of a polymer blend when including this nonlinear response. Beyond the statistical analysis performed here, analysis of nonlinear response could be used to obtain quantitative material properties at high speeds and with enhanced resolution.

Identifiers

PMID25665933
PMCPMC4346977

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.