Evidence map›Paper›PMID 33260524›Full record

ArticleMicromachines2020

A Silicon Optical Bench-Based Forward-View Two-Axis Scanner for Microendoscopy Applications.

Dong Zheng, Dingkang Wang, Y K Yoon, Huikai Xie

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Dong ZhengDepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
Dingkang WangDepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
Y K YoonDepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
Huikai XieSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100000, China.ORCID 0000-0002-8393-0021
University of Florida · USBeijing Institute of Technology · CN

Funding

NSF MIST Center Feng - NIH-imaging - P0071443
6 · The paper itself

Abstract

Optical microendoscopy enabled by a microelectromechanical system (MEMS) scanning mirror offers great potential for in vivo diagnosis of early cancer inside the human body. However, an additional beam folding mirror is needed for a MEMS mirror to perform forward-view scanning, which drastically increases the diameter of the resultant MEMS endoscopic probe. This paper presents a new monolithic two-axis forward-view optical scanner that is composed of an electrothermally driven MEMS mirror and a beam folding mirror both vertically standing and integrated on a silicon substrate. The mirror plates of the two mirrors are parallel to each other with a small distance of 0.6 mm. The laser beam can be incident first on the MEMS mirror and then on the beam folding mirror, both at 45°. The MEMS scanner has been successfully fabricated. The measured optical scan angles of the MEMS mirror were 10.3° for the x axis and 10.2° for the y axis operated under only 3 V. The measured tip-tilt resonant frequencies of the MEMS mirror were 1590 Hz and 1850 Hz, respectively. With this compact MEMS design, a forward-view scanning endoscopic probe with an outer diameter as small as 2.5 mm can be made, which will enable such imaging probes to enter the subsegmental bronchi of an adult patient.

Indexed as

forward-view scanninglung cell carcinomaMEMS mirroroptical coherence tomographysquamous dysplasia (SD)

Identifiers

PMID33260524
PMCPMC7761163
OpenAlexW3110493259

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.