Evidence map›Paper›PMID 37504128›Full record

ReviewBiosensors2023

From Self-Assembly of Colloidal Crystals toward Ordered Porous Layer Interferometry.

Yi-Zhen Wan, Weiping Qian

Abstract readReview
In one paragraph

Review in Biosensors, 2023. 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
–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

1 citing paper in PubMed.

  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.

Yi-Zhen WanState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID 0000-0003-3498-7387
Weiping QianState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID 0000-0002-4719-555X

Funding

the Jiangsu Provincial Key Research and Development Program BE2021355the Leading Talent Program for Gusu Innovation and Entrepreneurship in Suzhou ZXL2022635
6 · The paper itself

Abstract

Interferometry-based, reflectometric, label-free biosensors have made significant progress in the analysis of molecular interactions after years of development. The design of interference substrates is a key research topic for these biosensors, and many studies have focused on porous films prepared by top-down methods such as porous silicon and anodic aluminum oxide. Lately, more research has been conducted on ordered porous layer interferometry (OPLI), which uses ordered porous colloidal crystal films as interference substrates. These films are made using self-assembly techniques, which is the bottom-up approach. They also offer several advantages for biosensing applications, such as budget cost, adjustable porosity, and high structural consistency. This review will briefly explain the fundamental components of self-assembled materials and thoroughly discuss various self-assembly techniques in depth. We will also summarize the latest studies that used the OPLI technique for label-free biosensing applications and divide them into several aspects for further discussion. Then, we will comprehensively evaluate the strengths and weaknesses of self-assembly techniques and discuss possible future research directions. Finally, we will outlook the upcoming challenges and opportunities for label-free biosensing using the OPLI technique.

Indexed as

Biosensing TechniquesInterferometryAluminum OxidePorositySiliconAluminum OxideSiliconbiosensorscolloidal crystalslabel-freeoptical interferometryordered porous layer interferometryreflectometryself-assembly

Identifiers

PMID37504128
PMCPMC10377590

What OpenQuestion holds

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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.