Evidence map›Paper›PMID 38315583›Full record

ArticleACS nano2024

Structure Discovery in Atomic Force Microscopy Imaging of Ice.

Fabio Priante, Niko Oinonen, Ye Tian, Dong Guan, Chen Xu, Shuning Cai, Peter Liljeroth, Ying Jiang, Adam S Foster

Open access · hybridAbstract read
In one paragraph

Article in ACS nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.0field-weighted citation impact, top 10% of its field
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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Automating Model Building for SPM Images of Biomolecules Using MISO.Journal of chemical information and modeling · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. 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

9 authors at 3 institutions in 3 countries.

Fabio PrianteDepartment of Applied Physics, Aalto University, Helsinki FI-00076, Finland.ORCID 0000-0001-7052-8570
Niko OinonenDepartment of Applied Physics, Aalto University, Helsinki FI-00076, Finland.
Ye TianInternational Center for Quantum Materials, Peking University, Beijing 100871, China.
Dong GuanInternational Center for Quantum Materials, Peking University, Beijing 100871, China.
Chen XuDepartment of Applied Physics, Aalto University, Helsinki FI-00076, Finland.ORCID 0000-0003-2730-1025
Shuning CaiDepartment of Applied Physics, Aalto University, Helsinki FI-00076, Finland.
Peter LiljerothDepartment of Applied Physics, Aalto University, Helsinki FI-00076, Finland.ORCID 0000-0003-1253-8097
Ying JiangInternational Center for Quantum Materials, Peking University, Beijing 100871, China.ORCID 0000-0002-6887-5503
Adam S FosterDepartment of Applied Physics, Aalto University, Helsinki FI-00076, Finland.ORCID 0000-0001-5371-5905
Aalto University · FIPeking University · CNKanazawa University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The interaction of water with surfaces is crucially important in a wide range of natural and technological settings. In particular, at low temperatures, unveiling the atomistic structure of adsorbed water clusters would provide valuable data for understanding the ice nucleation process. Using high-resolution atomic force microscopy (AFM) and scanning tunneling microscopy, several studies have demonstrated the presence of water pentamers, hexamers, and heptamers (and of their combinations) on a variety of metallic surfaces, as well as the initial stages of 2D ice growth on an insulating surface. However, in all of these cases, the observed structures were completely flat, providing a relatively straightforward path to interpretation. Here, we present high-resolution AFM measurements of several water clusters on Au(111) and Cu(111), whose understanding presents significant challenges due to both their highly 3D configuration and their large size. For each of them, we use a combination of machine learning, atomistic modeling with neural network potentials, and statistical sampling to propose an underlying atomic structure, finally comparing its AFM simulated images to the experimental ones. These results provide insights into the early phases of ice formation, which is a ubiquitous phenomenon ranging from biology to astrophysics.

Indexed as

atomic force microscopyice nanoclustersmachine learningneural network potentialstip functionalization

Identifiers

PMID38315583
PMCPMC10883028
OpenAlexW4391542723

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.