Evidence map›Paper›PMID 35448279›Full record

ArticleBiosensors2022

Quantitative Biosensing Based on a Liquid Crystal Marginally Aligned by the PVA/DMOAP Composite for Optical Signal Amplification.

Tsung-Keng Chang, Mon-Juan Lee, Wei Lee

Abstract read
In one paragraph

Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Tsung-Keng ChangCollege of Photonics, National Yang Ming Chiao Tung University, Tainan 711010, Taiwan.
Mon-Juan LeeDepartment of Bioscience Technology, Chang Jung Christian University, Tainan 711301, Taiwan.ORCID 0000-0003-0420-6244
Wei LeeCollege of Photonics, National Yang Ming Chiao Tung University, Tainan 711010, Taiwan.ORCID 0000-0002-3515-1170

Funding

Ministry of Science and Technology 110-2320-B-309-001
6 · The paper itself

Abstract

The working principle for a liquid crystal (LC)-based biosensor relies on the disturbance in the orderly aligned LC molecules induced by analytes at the LC-aqueous or LC-solid interface to produce optical signals that can be typically observed under a polarizing optical microscope (POM). Our previous studies demonstrate that such optical response can be enhanced by imposing a weak electric field on LCs so that they are readily tilted from the homeotropic alignment in response to lower concentrations of analytes at the LC-glass interface. In this study, an alternative approach toward signal amplification is proposed by taking advantage of the marginally tilted alignment configuration without applying an electric field. The surface of glass substrates was modified with a binary aligning agent of poly(vinyl alcohol) (PVA) and dimethyloctadecyl[3-(trimethoxysilyl)propyl] ammonium chloride (DMOAP), in which the amount of PVA was fine-tuned so that the interfacing LC molecules were slightly tilted but remained virtually homeotropically aligned to yield no light leakage under the POM in the absence of an analyte. Two nematic LCs, E7 and 5CB, were each sandwiched between two parallel glass substrates coated with the PVA/DMOAP composite for the detection of bovine serum albumin (BSA), a model protein, and cortisol, a small-molecule steroid hormone. Through image analysis of the optical appearance of E7 observed under the POM, a limit of detection (LOD) of 2.5 × 10

Indexed as

Biosensing TechniquesLiquid CrystalsHydrocortisoneLimit of DetectionSerum Albumin, BovineHydrocortisoneSerum Albumin, Bovinebovine serum albumincortisolliquid crystaloptical biosensorpoly(vinyl alcohol)tilted alignment

Identifiers

PMID35448279
PMCPMC9026489

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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.