Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing

Mousavizadegan, Maryam; Firoozbakhtian, Ali; Hosseini, Morteza; Ju, Huangxian

doi:10.1016/j.trac.2023.117216

Cited by 20 publications

(10 citation statements)

References 170 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Electrogenerated chemiluminescence emerges from the excited state of luminophores as a result of high-energy electron transfer reactions within close proximity to the electrode. − Electrochemiluminescence (ECL) exhibits notable advantages for technological applications, encompassing remarkable sensitivity, an extensive dynamic range, and precise controllability. − Thus, the progress and enhancement of ECL technology significantly rely on the innovation and refinement of ECL emitters.…”

Section: Introductionmentioning

confidence: 99%

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C₃N₄ Fiber-like Nanostructure

Firoozbakhtian,

Salah,

Eid

et al. 2024

Langmuir

Self Cite

View full text Add to dashboard Cite

Graphitic-phase carbon nitride (g-C 3 N 4 ) materials have exhibited increasingly remarkable performance as emerging electrochemiluminescence (ECL) emitters, owing to their unique optical and electronic properties; however, the ECL merits of porous g-C 3 N 4 nanofibers doped with ternary metals are not yet explored. Deciphering the ECL properties of trimetal-doped g-C 3 N 4 nanofibers could provide an exquisite pathway for ultrasensitive sensing and imaging with impressive advantages of minimal background signal, great sensitivity, and durability. Herein, we rationally synthesized g-C 3 N 4 nanofibers doped atomically with Mn, Fe, and Co elements (Mn/Fe/Co/g-C 3 N 4 ) in a one-pot via the protonation in ethanol and annealing process driven by the rolling up mechanism. The ECL performance of g-C 3 N 4 with and without metal dopants was investigated and compared with standard Ru(bpy) 3 2+ in the presence of potassium persulfate (K 2 S 2 O 8 ) as the coreactant. Notably, g-C 3 N 4 nanofibers doped with metal ions exhibited an ECL efficiency of 483% that was 4.83 times higher than that of Ru(bpy) 3 2+ . Mechanistic investigations unveiled that the g-C 3 N 4 nanofibers possess a large surface area and, as a result, exhibit a reduced interfacial impedance within the porous microstructure. These factors contribute to the acceleration of charge transfer rates and the stabilization of charge carriers and excitons, ultimately facilitating the ECL process. This research endeavor may pave the way for a new hot research area and serves as a powerful tool for elucidating fundamental inquiries of ECL on one-dimensional g-C 3 N 4 nanostructures.

show abstract

Section: Introductionmentioning

confidence: 99%

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C₃N₄ Fiber-like Nanostructure

Firoozbakhtian,

Salah,

Eid

et al. 2024

Langmuir

Self Cite

View full text Add to dashboard Cite

show abstract

“…Neural network-based ML models are also successful at predicting multidimensional optical spectra and fluorescence properties of chromophores in complex environments 41 . Different types of ML models were also adept at predicting the fluorescence emission spectra of nanomaterials used as fluorescent or luminescent probes 42 , as well as for predicting emission spectra of DNA-templated silver nanoclusters, for which the training accuracies were found to be greater than 80% 43 .…”

Section: Introductionmentioning

confidence: 99%

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths

Kelich,

Adams,

Jeong

et al. 2024

Preprint

View full text Add to dashboard Cite

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT) based sensors for chemically-specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing datasets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training dataset leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

show abstract

“…Normally, single-point photodetectors like photomultiplier tubes or avalanche photodiodes are used because of their outstanding gain, sensitivity, and linearity. 25 However, the integration of these detectors into portable devices is hindered by their size, high voltage requirements, and high power consumption. Additionally, their compatibility with multiplexed detection designs poses further limitations.…”

Section: ■ Introductionmentioning

confidence: 99%

“…To tackle the challenge mentioned earlier, specialized photodetectors are required for acquiring and analyzing the ECL emission. Normally, single-point photodetectors like photomultiplier tubes or avalanche photodiodes are used because of their outstanding gain, sensitivity, and linearity . However, the integration of these detectors into portable devices is hindered by their size, high voltage requirements, and high power consumption.…”

Section: Introductionmentioning

confidence: 99%

Boosting Electrochemiluminescence Immunoassay Sensitivity via Co–Pt Nanoparticles within a Ti₃C₂ MXene-Modified Single Electrode Electrochemical System on Raspberry Pi

Firoozbakhtian,

Hosseini,

Guan

et al. 2023

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

Point-of-care testing plays a crucial role in diagnostics within resourcepoor areas, necessitating the utilization of portable and user-friendly devices. The adaptation of biosensors for point-of-care applications requires careful considerations, such as miniaturization, cost-effectiveness, and streamlined sample processing. In recent years, the electrochemiluminescence (ECL) immunoassay has gained significant attention due to its visual detection capabilities and ability to facilitate high-throughput analysis. However, the development of a practical and cost-effective ECL device remains a challenging task. This study presents the development of an integrated MXenemodified single-electrode electrochemical system (SEES) for visual and high-throughput ECL immunoassays incorporating a Raspberry Pi system. The SEES was designed by affixing a plastic sticker with multiple perforations onto a single carbon ink screenprinted electrode, which operates based on a resistance-induced potential difference. Leveraging the excellent adsorption and bioaffinity properties of the carbon ink screenprinted electrode, effective immobilization of antibodies was achieved. Furthermore, the incorporation of Co−Pt nanoparticles enhanced the ECL intensity and electron transfer kinetics, enabling the sensitive detection of SARS-CoV-2. The developed system comprised 18 individual reaction cells, allowing for simultaneous analysis while maintaining sample isolation. Impressively, the system achieved a remarkable minimum virus detection limit of 10 −14 g mL −1 , accompanied by a high R 2 value of 0.9798. These findings highlight the promising potential of our developed system for efficient point-of-care testing in resource-limited settings.

show abstract

Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing

Cited by 20 publications

References 170 publications

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C₃N₄ Fiber-like Nanostructure

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C₃N₄ Fiber-like Nanostructure

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths

Boosting Electrochemiluminescence Immunoassay Sensitivity via Co–Pt Nanoparticles within a Ti₃C₂ MXene-Modified Single Electrode Electrochemical System on Raspberry Pi

Contact Info

Product

Resources

About

Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing

Cited by 20 publications

References 170 publications

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C3N4 Fiber-like Nanostructure

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C3N4 Fiber-like Nanostructure

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths

Boosting Electrochemiluminescence Immunoassay Sensitivity via Co–Pt Nanoparticles within a Ti3C2 MXene-Modified Single Electrode Electrochemical System on Raspberry Pi

Contact Info

Product

Resources

About

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C₃N₄ Fiber-like Nanostructure

Unmasking the Electrochemiluminescence Properties of Ternary Mn/Fe/Co Metals Doped Porous g-C₃N₄ Fiber-like Nanostructure

Boosting Electrochemiluminescence Immunoassay Sensitivity via Co–Pt Nanoparticles within a Ti₃C₂ MXene-Modified Single Electrode Electrochemical System on Raspberry Pi