Evidence map›Paper›PMID 35273190›Full record

ArticleNature communications2022

Parallel detection and spatial mapping of large nuclear spin clusters.

K S Cujia, K Herb, J Zopes, J M Abendroth, C L Degen

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

4 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  3. Review
  4. 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

5 authors at 2 institutions in 1 country.

K S CujiaDepartment of Physics, ETH Zurich, Otto Stern Weg 1, 8093, Zurich, Switzerland. cujiapek@phys.ethz.ch.ORCID http://orcid.org/0000-0003-3446-4551
K HerbDepartment of Physics, ETH Zurich, Otto Stern Weg 1, 8093, Zurich, Switzerland. kh@rashbw.de.
J ZopesDepartment of Physics, ETH Zurich, Otto Stern Weg 1, 8093, Zurich, Switzerland. jonathanzopes@gmail.com.
J M AbendrothDepartment of Physics, ETH Zurich, Otto Stern Weg 1, 8093, Zurich, Switzerland. jabendroth@phys.ethz.ch.
C L DegenDepartment of Physics, ETH Zurich, Otto Stern Weg 1, 8093, Zurich, Switzerland. degenc@ethz.ch.ORCID http://orcid.org/0000-0003-2432-4301
ETH Zurich · CHSynopsys (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nuclear magnetic resonance imaging (MRI) at the atomic scale offers exciting prospects for determining the structure and function of individual molecules and proteins. Quantum defects in diamond have recently emerged as a promising platform towards reaching this goal, and allowed for the detection and localization of single nuclear spins under ambient conditions. Here, we present an efficient strategy for extending imaging to large nuclear spin clusters, fulfilling an important requirement towards a single-molecule MRI technique. Our method combines the concepts of weak quantum measurements, phase encoding and simulated annealing to detect three-dimensional positions from many nuclei in parallel. Detection is spatially selective, allowing us to probe nuclei at a chosen target radius while avoiding interference from strongly-coupled proximal nuclei. We demonstrate our strategy by imaging clusters containing more than 20 carbon-13 nuclear spins within a radius of 2.4 nm from single, near-surface nitrogen-vacancy centers at room temperature. The radius extrapolates to 5-6 nm for

Indexed as

DiamondMagnetic Resonance ImagingNanotechnologyNitrogenProteinsDiamondNitrogenProteins

Identifiers

PMID35273190
PMCPMC8913684
OpenAlexW3139127817

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.