Cuvette‐Based Electrogenerated Chemiluminescence Detection System for the Assessment of Polymerizable Ruthenium Luminophores

Danis, Andrew S.; Odette, William L; Perry, Samuel C.; Canesi, Sylvain; Sleiman, Hanadi F.; Mauzeroll, Janine

doi:10.1002/celc.201600879

Cited by 12 publications

(19 citation statements)

References 26 publications

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“…This linearity, which is characteristic of an outer‐sphere mediated electron transfer, supports the rationale that the Ru(MON)’s mechanism for generating ECL will mirror Ru(bpy) 3 2+ . Assuming the Ru(MON) employs the same ECL mechanism as Ru(bpy) 3 2+ , the observed difference in kinetics should have minimal effect on the Ru(MON)’s ECL performance under the co‐reactant method being employed in this system . Co‐reactant ECL facilitates photon emission through a linear potential sweep and should not be hampered by slower kinetics in the reverse reaction reducing the oxidized Ru(III) to Ru(II) at the electrode…”

Section: Resultsmentioning

confidence: 99%

“…Assuming the Ru(MON) employs the same ECL mechanism as Ru(bpy) 3 2 + , the observed difference in kinetics should have minimal effect on the Ru(MON)'s ECL performance under the co-reactant method being employed in this system. [16] Coreactant ECL facilitates photon emission through a linear potential sweep and should not be hampered by slower kinetics Figure 1. Cyclic voltammetry of monomers using a potential waveform containing vertex potentials of 0 V, 1.3 V, À 1.3 V, and 0 V at a scan rate of 100 mV/s.…”

Section: Monomer Characterizationmentioning

confidence: 99%

“…Block copolymers were originally hypothesized to be capable of generating ECL due to the Ru(MON)s individually being ECLactive. [16] To explore this hypothesis, different types of the block copolymers, diblock (Ru-PEG) and triblock (C4-Ru-PEG), were evaluated for their ECL performance as both unassembled polymers in acetonitrile and as self-assembled nanospheres in water ( Figure 6). These measurements not only confirmed the polymers' ECL activity but indicated that the polymer composition was not closely tied to the intensity of ECL emission.…”

Section: Ecl Performance Of Self-assembled Polymersmentioning

confidence: 99%

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Bottom‐Up Characterization and Self‐Assembly of Electrogenerated Chemiluminescence Active Ruthenium Nanospheres

et al. 2019

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An approach to electrogenerated chemiluminescence (ECL) signal enhancement, utilizing the highly tailorable ring‐opening metathesis polymerization (ROMP) to generate novel ECL active nanoscale structures is explored. The strategy involves integrating into a single polymer multiple copies of an analogue of the established ECL luminophore, tris(2,2’‐bipyridine)ruthenium(II) (Ru(bpy)32+). Through the addition of hydrophobic and hydrophilic functional groups to the polymer architecture, these ECL active block copolymers are engineered to self‐assemble when exposed to aqueous environments. Self‐assembled ruthenium nanospheres generated through this methodology were found to demonstrate drastic signal amplification properties compared to their non‐assembled polymer counterparts. Characterization methodologies conducted throughout the bottom‐up synthesis of the nanospheres, utilizing the ECL reference of Ru(bpy)32+, facilitated investigating the origins of the nanosphere's signal enhancement.

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Section: Resultsmentioning

confidence: 99%

Section: Monomer Characterizationmentioning

confidence: 99%

Section: Ecl Performance Of Self-assembled Polymersmentioning

confidence: 99%

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Bottom‐Up Characterization and Self‐Assembly of Electrogenerated Chemiluminescence Active Ruthenium Nanospheres

et al. 2019

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“…Meanwhile, ECL sl exhibited lower values at higher concentration of Ru(bpy) 2+ 3 (Figure 5c). Previous studies [7,25] discussed the importance of having systems capable of performing ECL and electrochemical measurements in sync to develop models that investigate the mechanism of the Ru(bpy) 2+ 3 /TPrA system. The consistent downward trend of experimental measurements of C maxp , C mind , and ECL sl with the concentration of the luminophore made it possible for these measurements to be interpolated to generate a large dataset.…”

Section: Chronoamperometric Data For Data-driven Modelingmentioning

confidence: 99%

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Rivera

Swerdlow

Summerscales

et al. 2020

Sensors

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Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru ( bpy ) 3 2 + luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru ( bpy ) 3 2 + /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru ( bpy ) 3 2 + concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru ( bpy ) 3 2 + using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

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“…The control of emission position is particularly of significance for ECLbased imaging analysis. Finally, ECL analysis can be accomplished by recording either electrical or optical signals or both, allowing the investigations of ECL mechanisms via electrochemical methods and vice versa [30]. For example, spooling ECL spectroscopy is capable of acquiring ECL spectra at different time intervals in the course of ECL processes, gathering information on emission wavelength and intensity, as well as their dependence on the applied potential simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Imaging Analysis Based on Electrogenerated Chemiluminescence

et al. 2017

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Electrochemiluminescence (ECL), also called electrogenerated chemiluminescence, is luminescence that is produced by chemical reactions triggered by electrochemical method. ECL imaging is a novel imaging technique, which can simultaneously provide two kinds of signals of both electrochemistry and optical image. Over the past two decades, ECL imaging has been not only a powerful tool for investigating the fundamental scientific questions such as ECL mechanisms and reaction kinetics, but also a versatile analytical technique for detection of a wide range of analytes including small molecules, DNA, proteins, and cells. In the first part of this review, we briefly describe the reaction mechanisms of ECL generation. Then the review focuses on the research progress on the ECL imaging approach. It is basically introduced from the following five aspects: (i) visualization of electroactive sites on surfaces, (ii) imaging analysis at the single-bead and single-cell levels, (iii) array bioanalysis, (iv) multi-color ECL imaging, and (v) paper chip based on ECL imaging. Finally, some perspectives and future directions in this active research area are presented.

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Cuvette‐Based Electrogenerated Chemiluminescence Detection System for the Assessment of Polymerizable Ruthenium Luminophores

Cited by 12 publications

References 26 publications

Bottom‐Up Characterization and Self‐Assembly of Electrogenerated Chemiluminescence Active Ruthenium Nanospheres

Bottom‐Up Characterization and Self‐Assembly of Electrogenerated Chemiluminescence Active Ruthenium Nanospheres

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Imaging Analysis Based on Electrogenerated Chemiluminescence

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