Evidence map›Paper›PMID 36147941›Full record

ArticleRoyal Society open science2022

Towards breath sensors that are self-powered by design.

Lucy Fitzgerald, Luis Lopez Ruiz, Joe Zhu, John Lach, Daniel Quinn

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Artificial Intelligence-Driven Soft Bioelectronics for Self-Powered Respiration Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. 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

5 authors.

Lucy FitzgeraldMechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0002-6263-8152
Luis Lopez RuizElectrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0002-0800-2815
Joe ZhuMechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.
John LachElectrical and Computer Engineering, George Washington University, Washington, DC, USA.
Daniel QuinnMechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0001-5880-3728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Piezoelectric materials are widely used to generate electric charge from mechanical deformation or vice versa. These strategies are increasingly common in implantable medical devices, where sensing must be done on small scales. In the case of a flow rate sensor, a sensor's energy harvesting rate could be mapped to that flow rate, making it 'self-powered by design (SPD)'. Prior fluids-based SPD work has focused on turbulence-driven resonance and has been largely empirical. Here, we explore the possibility of sub-resonant SPD flow sensing in a human airway. We present a physical model of piezoelectric sensing/harvesting in the airway, which we validated with a benchtop experiment. Our work offers a model-based roadmap for implantable SPD sensing solutions. We also use the model to theorize a new form of SPD sensing that can detect broadband flow information.

Indexed as

body sensorsenergy harvestingflow sensingimplantablespiezoelectricityself-powered

Identifiers

PMID36147941
PMCPMC9490333

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.