Evidence map›Paper›PMID 35808351›Full record

ArticleSensors (Basel, Switzerland)2022

Design of a Robust Tool for Deploying Large-Area Stretchable Sensor Networks from Microscale to Macroscale.

Elliot Ransom, Xiyuan Chen, Fu-Kuo Chang

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Elliot RansomDepartment of Aeronautics and Astronautics, Stanford University, Durand Building, 496 Lomita Mall, Stanford, CA 94305, USA.
Xiyuan ChenDepartment of Mechanical Engineering, Stanford University, Building 530, 440 Escondido Mall, Stanford, CA 94305, USA.ORCID 0000-0002-0948-2611
Fu-Kuo ChangDepartment of Aeronautics and Astronautics, Stanford University, Durand Building, 496 Lomita Mall, Stanford, CA 94305, USA.

Funding

Airbus (United States) 127302Kaneka (Japan) N/ANational Science Foundation 1528145NextFlex FA8650-15-2-5401RUAG (Switzerland) 127290United States Air Force Office of Scientific Research 124069United States Army Research Laboratory 130478
6 · The paper itself

Abstract

An investigation was conducted to develop an effective automated tool to deploy micro-fabricated stretchable networks of distributed sensors onto the surface of large structures at macroscale to create "smart" structures with embedded distributed sensor networks. Integrating a large network of distributed sensors with structures has been a major challenge in the design of so-called smart structures or devices for cyber-physical applications where a large amount of usage data from structures or devices can be generated for artificial intelligence applications. Indeed, many "island-and-serpentine"-type distributed sensor networks, while promising, remain difficult to deploy. This study aims to enable such networks to be deployed in a safe, automated, and efficient way. To this end, a scissor-hinge controlled system was proposed as the basis for a deployment mechanism for such stretchable sensor networks (SSNs). A model based on a kinematic scissor-hinge mechanism was developed to simulate and design the proposed system to automatically stretch a micro-scaled square network with uniformly distributed sensor nodes. A prototype of an automatic scissor-hinge stretchable tool was constructed during the study with an array of four scissor-hinge mechanisms, each belt-driven by a single stepper motor. Two micro-fabricated SSNs from a 100 mm wafer were fabricated at the Stanford Nanofabrication Facility for this deployment study. The networks were designed to be able to cover an area 100 times their manufacturing size (from a 100 mm diameter wafer to a 1 m2 active area) once stretched. It was demonstrated that the proposed deployment tool could place sensor nodes in prescribed locations efficiently within a drastically shorter time than in current labor-intensive manual deployment methods.

Indexed as

Artificial IntelligenceWearable Electronic Devicessensor networksmart structurestretchable electronics deploymentstructural health monitoring

Identifiers

PMID35808351
PMCPMC9269494

What OpenQuestion holds

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Registered trials

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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.