Issue 46, 2022

Machine learning-driven advanced development of carbon-based luminescent nanomaterials

Abstract

Carbon-based luminescent nanomaterials (CLNMs) have been progressively developed and exhibit excellent performance in broad applications. However, the unclear formation mechanism and obtained complex structure of CLNMs, induced a lack of deep understanding of the synthesis–structure–properties–performance relationship, hindering further development in practical application. The recent development of CLNMs in various applications primarily relied on traditional “trial-and-error” costly experiments or complex computational works with insufficient models. Fortunately, machine learning (ML) has emerged as a promising tool for identifying data relationships to accelerate the research and development of CLNMs without explicit programming. The type of datasets, specific problem and the available computing costs are usually the decisive factor for choosing the appropriate ML algorithm. Herein, various pioneering works on ML utilization to boost the development of CLNMs are reviewed. Progressive and remarkable works, the challenges, and various innovative ML techniques that addressed the bottleneck issue in several applications, including guided synthesis, sensors, and biosensors for biomedical diagnostics, are analyzed and discussed. In addition, this review provides an overview of ML workflow and the common ML algorithm used. Finally, the prospect of developing machine learning for the advanced development of CLNMs in various applications is proposed.

Graphical abstract: Machine learning-driven advanced development of carbon-based luminescent nanomaterials

Article information

Article type
Review Article
Submitted
08 септ. 2022
Accepted
23 окт. 2022
First published
24 окт. 2022

J. Mater. Chem. C, 2022,10, 17431-17450

Machine learning-driven advanced development of carbon-based luminescent nanomaterials

D. A. M. Muyassiroh, F. A. Permatasari and F. Iskandar, J. Mater. Chem. C, 2022, 10, 17431 DOI: 10.1039/D2TC03789K

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements