Evidence map›Paper›PMID 30505354›Full record

ArticleAnalytical methods : advancing methods and applications2018

Advancement of analytical modes in a multichannel, microfluidic droplet-based sample chopper employing phase-locked detection.

Jean T Negou, Juan Hu, Xiangpeng Li, Christopher J Easley

Abstract read
In one paragraph

Article in Analytical methods : advancing methods and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Dynamic sampling fromLab on a chip · 2024
    Article
  2. Article
  3. Article
  4. Review
  5. Active Flow Control and Dynamic Analysis in Droplet Microfluidics.Annual review of analytical chemistry (Palo Alto, Calif.) · 2021
    Review
  6. Article
  7. Article
  8. 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

4 authors.

Jean T NegouDepartment of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849, USA.
Juan HuDepartment of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849, USA.
Xiangpeng LiDepartment of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849, USA.
Christopher J EasleyDepartment of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849, USA.

Funding

Unmasking mechanisms of lipolytic dynamics in adipose tissue using high-resolution microfluidic samplingR01DK093810 · NIDDK · AUBURN UNIVERSITY AT AUBURN · PI EASLEY, CHRISTOPHER J · 2012 to 2023
$3.8M
NIDDK NIH HHS R01 DK093810
6 · The paper itself

Abstract

In this work, we expand upon our recently developed droplet-based sample chopping concepts by introducing a multiplexed fluidic micro-chopper device (μChopper). Six aqueous input channels were integrated with a single oil input, and each of these seven channels was controlled by a pneumatic valve for automated sampling through software control. This improved design, while maintaining high precision in valve-based droplet generation at bandwidths of 0.03 to 0.05 Hz, enabled a variety of analytical modes to be employed on-chip compared to previous devices limited to sample/reference alternations. The device was analytically validated for real-time, continuous calibration with a single sample and five standards; multiplexed analysis during calibration using a mixed mode; and standard addition through spiking of six sample droplets with varying amounts of standard. Finally, the standard addition mode was applied to protein quantification in human serum samples using on-chip, homogeneous fluorescence immunoassays. Ultimately, with only ~1.2 μL of total analyzed solution volume- representing 100-fold and 75-fold reductions in reagent and serum volumes, respectively-we were able to generate full, six-point standard addition curves in only 1.5 min, and results correlated well with those from standard plate-reader equipment. This work thus exploited microfluidic valves for both their automation and droplet phase-locking capabilities, resulting in a micro-analytical tool capable of complex analytical interrogation modes on sub-microliter sample volumes while also leveraging drastic noise rejection via lock-in detection. The multichannel μChopper device should prove particularly useful in analyzing precious biological samples or for dynamic analyses at small volume scales.

Identifiers

PMID30505354
PMCPMC6258173

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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.