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Optical Materials for Sensing and Bioimaging: Advances and Challenges
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Optical Materials for Sensing and Bioimaging: Advances and Challenges

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Biomaterials".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 12097

Special Issue Editor

Dr. Run Zhang

E-Mail Website
Guest Editor
Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane 4072, Australia
Interests: analytical chemistry; biosensing and bioimaging; photochemistry; nanotechnology; nano-bio interface chemistry; nanobiosensors; theranostic nanomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Development of activatable sensors for recognition and detection of environmentally and biologically important species have attracted considerable interests in recent years. Among various sensing techniques, optical sensing using fluorescent or luminescent sensors for the detection of trace amount of analytes is receiving increasing attention because of its simplicity, high sensitivity, and versatility in obtaining diverse information in-situ. For biological samples, the application of these responsive sensors to real-time detection of biomolecules in live cells and organisms is also accelerating through support from rapid developments in optical microscopy. As a result, the analytes can be clearly seen with the naked eye.

The rapid progress in the sensing and bioimaging research fields have significantly benefited from the development of responsive fluorescence/luminescence materials, such as organic dyes, organometallics, lanthanide chelates, luminescent nanocrystals, etc. The performance of these sensors with regard to sensitivity, stability, and reliability when applied to the detection of specific analytes is also dominated by the fluorescence/luminescence materials mentioned above. Here, we propose a special issue to highlight “Optical Materials for Sensing and Bioimaging: Advances and Challenges”. It is anticipated that this special issue will provide a forum for mutual communication among researchers in the fields of chemistry, materials science, spectroscopy, environmental research and biology.

Specifically, the scope includes:

Chemistry: organic chemistry, analytical chemistry, inorganic chemistry

Material Science: organic dyes, organometallics, lanthanide chelates, luminescent nanocrystals, etc.

Spectroscopy: fluorescence, luminescence, time-resolved luminescence, electrochemical luminescence, bioluminescence, etc

Applications in Environment and biology: sensing and bioimaging

Dr. Run Zhang
Guest Editor

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Keywords

  • Nanomaterials
  • Biomaterials
  • (Bio)sensors
  • Bioimaging
  • Biophotonics
  • Fluorescence/Luminescence/Phosphorescence/Bioluminescence/Chemiluminescence/Electrochemiluminescence
  • Probes
  • Inorganic/organic compounds
  • Analytical method

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Published Papers (3 papers)

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10 pages, 6020 KiB  
Article
A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method
by Dingfei Jin, Yue Yang, Tao Ge and Daole Wu
Materials 2019, 12(8), 1227; https://doi.org/10.3390/ma12081227 - 15 Apr 2019
Cited by 5 | Viewed by 2932
Abstract
In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the [...] Read more.
In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions. Full article
(This article belongs to the Special Issue Optical Materials for Sensing and Bioimaging: Advances and Challenges)
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10 pages, 3917 KiB  
Article
A Label-Free Fluorescent DNA Machine for Sensitive Cyclic Amplification Detection of ATP
by Jingjing Zhang, Jialun Han, Shehong Feng, Chaoqun Niu, Chen Liu, Jie Du and Yong Chen
Materials 2018, 11(12), 2408; https://doi.org/10.3390/ma11122408 - 29 Nov 2018
Cited by 4 | Viewed by 3751
Abstract
In this study, a target recycled amplification, background signal suppression, label-free fluorescent, enzyme-free deoxyribonucleic acid (DNA) machine was developed for the detection of adenosine triphosphate (ATP) in human urine. ATP and DNA fuel strands (FS) were found to trigger the operation of the [...] Read more.
In this study, a target recycled amplification, background signal suppression, label-free fluorescent, enzyme-free deoxyribonucleic acid (DNA) machine was developed for the detection of adenosine triphosphate (ATP) in human urine. ATP and DNA fuel strands (FS) were found to trigger the operation of the DNA machine and lead to the cyclic multiplexing of ATP and the release of single stranded (SS) DNA. Double-stranded DNA (dsDNA) was formed on graphene oxide (GO) from the combination of SS DNA and complementary strands (CS′). These double strands then detached from the surface of the GO and in the process interacted with PicoGreen dye resulting in amplifying fluorescence intensity. The results revealed that the detection range of the DNA machine is from 100 to 600 nM (R2 = 0.99108) with a limit of detection (LOD) of 127.9 pM. A DNA machine circuit and AND-NOT-AND-OR logic gates were successfully constructed, and the strategy was used to detect ATP in human urine. With the advantage of target recycling amplification and GO suppressing background signal without fluorescent label and enzyme, this developed strategy has great potential for sensitive detection of different proteins and small molecules. Full article
(This article belongs to the Special Issue Optical Materials for Sensing and Bioimaging: Advances and Challenges)
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12 pages, 2679 KiB  
Article
Carbon Black-Carbon Nanotube Co-Doped Polyimide Sensors for Simultaneous Determination of Ascorbic Acid, Uric Acid, and Dopamine
by Yue Wang, Tian Yang, Yasushi Hasebe, Zhiqiang Zhang and Dongping Tao
Materials 2018, 11(9), 1691; https://doi.org/10.3390/ma11091691 - 12 Sep 2018
Cited by 19 | Viewed by 4700
Abstract
Carbon black (CB) and carbon nanotube (CNT) co-doped polyimide (PI) modified glassy carbon electrode (CB-CNT/PI/GCE) was first prepared for the simultaneous determination of ascorbic acid (AA), dopamine (DA), and uric acid (UA). The CB-CNT/PI/GCE exhibited persistent electrochemical behavior and excellent catalytic activities. Cyclic [...] Read more.
Carbon black (CB) and carbon nanotube (CNT) co-doped polyimide (PI) modified glassy carbon electrode (CB-CNT/PI/GCE) was first prepared for the simultaneous determination of ascorbic acid (AA), dopamine (DA), and uric acid (UA). The CB-CNT/PI/GCE exhibited persistent electrochemical behavior and excellent catalytic activities. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for the simultaneous detection of AA, DA, and UA in their ternary mixture. The peak separations between AA and DA, and DA and UA, are up to 166 mV and 148 mV, respectively. The CB-CNT/PI/GCE exhibited high sensitivity to DA and UA, with the detection limit of 1.9 µM and 3 µM, respectively. In addition, the CB-CNT/PI/GCE showed sufficient selectivity and long-term stability, and was applicable to detect AA, DA, and UA in human urine sample. Full article
(This article belongs to the Special Issue Optical Materials for Sensing and Bioimaging: Advances and Challenges)
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