Evidence map›Paper›PMID 40503913›Full record

ArticleNano letters2025

Slow Water in Engineered Nanochannels Revealed by Color-Center-Enabled Sensing.

Daniela Pagliero, Rohma Khan, Kapila Elkaduwe, Ankit Bhardwaj, Kang Xu, Abraham Wolcott, Gustavo E López, Boya Radha, Nicolás Giovambattista, Carlos A Meriles

Abstract read
In one paragraph

Article in Nano letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

10 authors.

Daniela PaglieroDepartment of Physics, CUNY─The City College of New York, New York, New York 10031, United States.
Rohma KhanDepartment of Physics, CUNY─The City College of New York, New York, New York 10031, United States.
Kapila ElkaduweCUNY-Graduate Center, New York, New York 10016, United States.ORCID 0000-0001-6845-7435
Ankit BhardwajDepartment of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, U.K.
Kang XuDepartment of Physics, CUNY─The City College of New York, New York, New York 10031, United States.ORCID 0009-0008-0548-5293
Abraham WolcottDepartment of Physics, CUNY─The City College of New York, New York, New York 10031, United States.ORCID 0000-0002-4025-3040
Gustavo E LópezCUNY-Graduate Center, New York, New York 10016, United States.ORCID 0000-0003-1660-547X
Boya RadhaDepartment of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, U.K.ORCID 0000-0003-1345-7029
Nicolás GiovambattistaCUNY-Graduate Center, New York, New York 10016, United States.ORCID 0000-0003-1149-0693
Carlos A MerilesDepartment of Physics, CUNY─The City College of New York, New York, New York 10031, United States.ORCID 0000-0003-2197-1474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanoscale confinement of liquids can result in enhanced viscosity, local fluidic order, or collective motion. Studying these effects, however, is notoriously difficult, mainly due to the lack of experimental methods with the required sensitivity and spatial or time resolution. Here we leverage shallow nitrogen-vacancy (NV) centers in diamond to probe the dynamics of room-temperature water molecules entrapped within ∼5 nm-tall channels formed between the diamond crystal and a suspended hexagonal boron nitride (hBN) flake. NV-enabled nuclear magnetic resonance measurements of confined water protons reveal a much reduced H

Indexed as

confined waterH2O self-diffusionnanochannelsNV centersquantum sensing

Identifiers

PMID40503913
PMCPMC12203581

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.