Evidence map›Paper›PMID 33399450›Full record

ArticleACS nano2021

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography.

Benjamin Midtvedt, Erik Olsén, Fredrik Eklund, Fredrik Höök, Caroline Beck Adiels, Giovanni Volpe, Daniel Midtvedt

Abstract read
In one paragraph

Article in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. AI-nanotech synergies: recent advances in sustainable bio-manufacturing.World journal of microbiology & biotechnology · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022
    Review
  12. Article
  13. The Role ofFrontiers in digital health · 2022
    Review
  14. Review
  15. Review
  16. A Deep Learning Perspective on Dropwise Condensation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Article
  17. An Image Recognition Framework for Oral Cancer Cells.Journal of healthcare engineering · 2021
    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

7 authors.

Benjamin MidtvedtDepartment of Physics, University of Gothenburg, SE-412 96 Gothenburg, Sweden.
Erik OlsénDepartment of Physics, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.ORCID 0000-0002-4002-0917
Fredrik EklundDepartment of Physics, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.ORCID 0000-0003-0582-3404
Fredrik HöökDepartment of Physics, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.ORCID 0000-0003-1994-5015
Caroline Beck AdielsDepartment of Physics, University of Gothenburg, SE-412 96 Gothenburg, Sweden.
Giovanni VolpeDepartment of Physics, University of Gothenburg, SE-412 96 Gothenburg, Sweden.ORCID 0000-0001-5057-1846
Daniel MidtvedtDepartment of Physics, University of Gothenburg, SE-412 96 Gothenburg, Sweden.ORCID 0000-0003-4132-4629

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size

Indexed as

aggregation kineticsdeep learningholographyoptical microscopyparticle characterization

Identifiers

PMID33399450
PMCPMC7905872

What OpenQuestion holds

Textmetadata
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.