Evidence map›Paper›PMID 36557463›Full record

ArticleMicromachines2022

A Polymeric Piezoelectric Tactile Sensor Fabricated by 3D Printing and Laser Micromachining for Hardness Differentiation during Palpation.

Chang Ge, Edmond Cretu

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 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
0.4field-weighted citation impact, top 45% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

2 authors at 1 institution in 1 country.

Chang GeDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Edmond CretuDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
University of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tactile sensors are important bionic microelectromechanical systems that are used to implement an artificial sense of touch for medical electronics. Compared with the natural sense of touch, this artificial sense of touch provides more quantitative information, augmenting the objective aspects of several medical operations, such as palpation-based diagnosis. Tactile sensors can be effectively used for hardness differentiation during the palpation process. Since palpation requires direct physical contact with patients, medical safety concerns are alleviated if the sensors used can be made disposable. In this respect, the low-cost, rapid fabrication of tactile sensors based on polymers is a possible alternative. The present work uses the 3D printing of elastic resins and the laser micromachining of piezoelectric polymeric films to make a low-cost tactile sensor for hardness differentiation through palpation. The fabricated tactile sensor has a sensitivity of 1.52 V/mm to mechanical deformation at the vertical direction, a sensitivity of 11.72 mV/HA in sensing material hardness with a pressing depth of 500 µm for palpation, and a validated capability to detect rigid objects buried in a soft tissue phantom. Its performance is comparable with existing piezoelectric tactile sensors for similar applications. In addition, the tactile sensor has the additional advantage of providing a simpler microfabrication process.

Indexed as

bionic MEMSelastomer 3D printinglaser micromachiningpalpationpiezoelectric tactile sensortissue hardness differentiation

Identifiers

PMID36557463
PMCPMC9782577
OpenAlexW4311852590

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.