Evidence map›Paper›PMID 34837190›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Micromachined Optical Fiber Sensors for Biomedical Applications.

Chen Zhu, Rex E Gerald, Jie Huang

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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
1.6field-weighted citation impact, top 15% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Chen ZhuDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Rex E GeraldDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Jie HuangDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA. jieh@mst.edu.
Missouri University of Science and Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Optical fibers revolutionized the rate of information reception and transmission in telecommunications. The revolution has now extended to the field of physicochemical sensing. Optical fiber sensors (OFSs) have found a multitude of applications, spanning from structural health monitoring to biomedical and clinical measurements due to their unique physical and functional advantages, such as small dimensions, light weight, immunity to electromagnetic interference, high sensitivity and resolution, multiplexing, and remote operation. OFSs generally rely on the detection of measurand-induced changes in the optical properties of the light propagating in the fiber, where the OFS essentially functions as the conduit and physical link between the probing light waves and the physicochemical parameters under investigation. Several advanced micromachining techniques have been developed to optimize the structure of OFSs, thus improving their sensing performance. These techniques include fusion splicing, tapering, polishing, and more complicated femtosecond laser micromachining methods. This chapter discusses and reviews the most recent developments in micromachined OFSs specifically for biomedical applications. Step-by-step procedures for several optical fiber micromachining techniques are detailed.

Indexed as

Optical FibersLasersMicrotechnologyBiomechanicalBiomedicalBiosensorsHealthcareMicromachiningOptical fiber sensors

Identifiers

PMID34837190
OpenAlexW3215824156

What OpenQuestion holds

Textmetadata
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.