Evidence map›Paper›PMID 33103674›Full record

ArticleLab on a chip2020

Deterministic droplet coding

Peiran Zhang, Wei Wang, Hai Fu, Joseph Rich, Xingyu Su, Hunter Bachman, Jianping Xia, Jinxin Zhang, Shuaiguo Zhao, Jia Zhou and 1 more

Open access · greenAbstract read
In one paragraph

Article in Lab on a chip, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 31% 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

6 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Review
  3. Innovative Advances in Droplet Microfluidics.Research (Washington, D.C.) · 2025
    Review
  4. Article
  5. Article
  6. 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

11 authors at 2 institutions in 3 countries.

Peiran ZhangDepartment of Mechanical Engineering and Material Science, Duke University, Durham, NC 27708, USA. tony.huang@duke.edu.
Wei Wang
Hai Fu
Joseph Rich
Xingyu Su
Hunter Bachman
Jianping Xia
Jinxin Zhang
Shuaiguo Zhao
Jia Zhou
Tony Jun Huang
Duke University · USFudan University · CN

Funding

Extracellular vesicles and their ncRNA cargo as markers of trophoblast injuryR01HD086325 · NICHD · MAGEE-WOMEN'S RES INST AND FOUNDATION · PI SADOVSKY, YOEL · 2015 to 2019
$3.6M
Integrated Acoustofluidic Plasmonic Molecular Diagnostic System for Detecting MicroRNA BiomarkersR01GM135486 · NIGMS · DUKE UNIVERSITY · PI VO-DINH, TUAN · 2020 to 2023
$1.8M
Enabling Efficient, Fast, Biocompatible Exosome Separation via AcoustofluidicsR01GM132603 · NIGMS · DUKE UNIVERSITY · PI HUANG, TONY JUN · 2019 to 2022
$1.7M
Determining treatment sensitivity in B cell lymphoma by novel microfluidics-based NK cell immunogenicity platformR33CA223908 · NCI · NORTHEASTERN UNIVERSITY · PI EVENS, ANDREW M, HUANG, TONY JUN · 2018 to 2020
$1.2M
An integrated acoustofluidic droplet-sorting platform for classification of lymphocyte activity and functional phenotyping at single cell levelR01GM127714 · NIGMS · NORTHEASTERN UNIVERSITY · PI KONRY, TANIA TALI · 2018 to 2021
$1.2M
Acoustofluidic Separation (AFS), Purification and Raman Spectral Fingerprinting of Single EVs: From Cell of Origin to Target Cell and BiofluidsUG3TR002978 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AN, JI YEONG, GALL, KENNETH A · 2019 to 2020
$823k
NCATS NIH HHS UG3 TR002978NCI NIH HHS R33 CA223908NICHD NIH HHS R01 HD086325NIGMS NIH HHS R01 GM127714NIGMS NIH HHS R01 GM132603NIGMS NIH HHS R01 GM135486
6 · The paper itself

Abstract

Droplet microfluidics has become an indispensable tool for biomedical research and lab-on-a-chip applications owing to its unprecedented throughput, precision, and cost-effectiveness. Although droplets can be generated and screened in a high-throughput manner, the inability to label the inordinate amounts of droplets hinders identifying the individual droplets after generation. Herein, we demonstrate an acoustofluidic platform that enables on-demand, real-time dispensing, and deterministic coding of droplets based on their volumes. By dynamically splitting the aqueous flow using an oil jet triggered by focused traveling surface acoustic waves, a sequence of droplets with deterministic volumes can be continuously dispensed at a throughput of 100 Hz. These sequences encode barcoding information through the combination of various droplet lengths. As a proof-of-concept, we encoded droplet sequences into end-to-end packages (e.g., a series of 50 droplets), which consisted of an address barcode with binary volumetric combinations and a sample package with consistent volumes for hosting analytes. This acoustofluidics-based, deterministic droplet coding technique enables the tagging of droplets with high capacity and high error-tolerance, and can potentially benefit various applications involving single cell phenotyping and multiplexed screening.

Indexed as

Lab-On-A-Chip DevicesMicrofluidicsWaterWater

Identifiers

PMID33103674
PMCPMC7688411
OpenAlexW3094263859

What OpenQuestion holds

Textmetadata
LicenceTDM
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.