Evidence map›Paper›PMID 35214391›Full record

ArticleSensors (Basel, Switzerland)2022

Piezoresistive Conductive Microfluidic Membranes for Low-Cost On-Chip Pressure and Flow Sensing.

Md Nazibul Islam, Steven M Doria, Xiaotong Fu, Zachary R Gagnon

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Md Nazibul IslamArtie McFerrin Department of Chemical Engineering, Texas A & M University, College Station, TX 77843, USA.
Steven M DoriaArtie McFerrin Department of Chemical Engineering, Texas A & M University, College Station, TX 77843, USA.
Xiaotong FuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Zachary R GagnonArtie McFerrin Department of Chemical Engineering, Texas A & M University, College Station, TX 77843, USA.

Funding

NASA 80NSSC19K1401National Science Foundation CBET 1351253Shared Services Center NASA 80NSSC18K1401
6 · The paper itself

Abstract

Over the last two decades, the field of microfluidics has received significant attention from both academia and industry. Each year, researchers report thousands of new prototype devices for use in a broad range of environmental, pharmaceutical, and biomedical engineering applications. While lab-on-a-chip fabrication costs have continued to decrease, the hardware required for monitoring fluid flows within the microfluidic devices themselves remains expensive and often cost-prohibitive for researchers interested in starting a microfluidics project. As microfluidic devices become capable of handling complex fluidic systems, low-cost, precise, and real-time pressure and flow rate measurement capabilities have become increasingly important. While many labs use commercial platforms and sensors, these solutions can often cost thousands of dollars and can be too bulky for on-chip use. Here we present a new inexpensive and easy-to-use piezoresistive pressure and flow sensor that can be easily integrated into existing on-chip microfluidic channels. The sensor consists of PDMS-carbon black conductive membranes and uses an impedance analyzer to measure impedance changes due to fluid pressure. The sensor costs several orders of magnitude less than existing commercial platforms and can monitor local fluid pressures and calculate flow rates based on the pressure gradient.

Indexed as

Lab-On-A-Chip DevicesMicrofluidicsimpedance spectroscopymicrofluidicspressure sensor

Identifiers

PMID35214391
PMCPMC8879421

What OpenQuestion holds

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LicenceCC BY
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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.