Evidence map›Paper›PMID 36252006›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Wettability-based ultrasensitive detection of amphiphiles through directed concentration at disordered regions in self-assembled monolayers.

Yuxing Yao, Robert K A Bennett, Yang Xu, Adil M Rather, Shucong Li, Tung Chun Cheung, Alisha Bhanji, Michael J Kreder, Dan Daniel, Solomon Adera and 2 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Sliding droplets as the chemical version for identifying the number and type of needles in a haystack.Proceedings of the National Academy of Sciences of the United States of America · 2022
    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

12 authors.

Yuxing YaoDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
Robert K A BennettJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Yang XuWilliam G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210.
Adil M RatherWilliam G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210.
Shucong LiDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
Tung Chun CheungJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Alisha BhanjiJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Michael J KrederJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Dan DanielJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-5859-170X
Solomon AderaJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Joanna AizenbergDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-2343-8705
Xiaoguang WangWilliam G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various forms of ecological monitoring and disease diagnosis rely upon the detection of amphiphiles, including lipids, lipopolysaccharides, and lipoproteins, at ultralow concentrations in small droplets. Although assays based on droplets' wettability provide promising options in some cases, their reliance on the measurements of surface and bulk properties of whole droplets (e.g., contact angles, surface tensions) makes it difficult to monitor trace amounts of these amphiphiles within small-volume samples. Here, we report a design principle in which self-assembled monolayer-functionalized microstructured surfaces coated with silicone oil create locally disordered regions within a droplet's contact lines to effectively concentrate amphiphiles within the areas that dominate the droplet static friction. Remarkably, such surfaces enable the ultrasensitive, naked-eye detection of amphiphiles through changes in the droplets' sliding angles, even when the concentration is four to five orders of magnitude below their critical micelle concentration. We develop a thermodynamic model to explain the partitioning of amphiphiles at the contact line by their cooperative association within the disordered, loosely packed regions of the self-assembled monolayer. Based on this local analyte concentrating effect, we showcase laboratory-on-a-chip surfaces with positionally dependent pinning forces capable of both detecting industrially and biologically relevant amphiphiles (e.g., bacterial endotoxins), as well as sorting aqueous droplets into discrete groups based on their amphiphile concentrations. Furthermore, we demonstrate that the sliding behavior of amphiphile-laden aqueous droplets provides insight into the amphiphile's effective length, thereby allowing these surfaces to discriminate between analytes with highly disparate molecular sizes.

Indexed as

MicellesSilicone OilsLipopolysaccharidesSurface TensionWaterWettabilityLipopolysaccharidesMicellesSilicone OilsWateramphiphileslubricated surfacesself-assemblysensorswettability

Identifiers

PMID36252006
PMCPMC9618133

What OpenQuestion holds

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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.