Evidence map›Paper›PMID 35981885›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Engineering Self-Powered Electrochemical Sensors Using Analyzed Liquid Sample as the Sole Energy Source.

Sunil Kumar Sailapu, Carlo Menon

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

2 authors.

Sunil Kumar SailapuBiomedical and Mobile Health Technology (BMHT) lab, Department of Health Sciences and Technology, ETH Zürich, Zürich, 8008, Switzerland.ORCID 0000-0002-0923-0879
Carlo MenonBiomedical and Mobile Health Technology (BMHT) lab, Department of Health Sciences and Technology, ETH Zürich, Zürich, 8008, Switzerland.ORCID 0000-0002-2309-9977

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many healthcare and environmental monitoring devices use electrochemical techniques to detect and quantify analytes. With sensors progressively becoming smaller-particularly in point-of-care (POC) devices and wearable platforms-it creates the opportunity to operate them using less energy than their predecessors. In fact, they may require so little power that can be extracted from the analyzed fluids themselves, for example, blood or sweat in case of physiological sensors and sources like river water in the case of environmental monitoring. Self-powered electrochemical sensors (SPES) can generate a response by utilizing the available chemical species in the analyzed liquid sample. Though SPESs generate relatively low power, capable devices can be engineered by combining suitable reactions, miniaturized cell designs, and effective sensing approaches for deciphering analyte information. This review details various such sensing and engineering approaches adopted in different categories of SPES systems that solely use the power available in liquid sample for their operation. Specifically, the categories discussed in this review cover enzyme-based systems, battery-based systems, and ion-selective electrode-based systems. The review details the benefits and drawbacks with these approaches, as well as prospects of and challenges to accomplishing them.

Indexed as

Biosensing TechniquesWearable Electronic DevicesElectrochemical TechniquesSweatWaterWaterbatteriesbiofuel cellselectrochemical systemsenzymesionsion-selective electrodesself-powered sensors

Identifiers

PMID35981885
PMCPMC9561779

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.