Mittal, Prateek;
Nampoothiri, Krishnadas Narayanan;
Jha, Abhishek;
Bansal, Shubhi;
(2024)
Convergence of machine learning with microfluidics and metamaterials to build smart materials.
International Journal on Interactive Design and Manufacturing (IJIDeM)
10.1007/s12008-023-01707-9.
(In press).
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Abstract
Recent advances in machine learning have revolutionized numerous research domains by extracting the hidden features and properties of complex systems, which are not otherwise possible using conventional ways. One such development can be seen in designing smart materials, which intersects the ability of microfluidics and metamaterials with machine learning to achieve unprecedented abilities. Microfluidics involves generating and manipulating fluids in the form of liquid streams or droplets from microliter to femtoliter regimes. However, analysis of such fluid flows is always tiresome and challenging due to the complexity involved in the integration and detection of various chemical or biological processes. On the other hand, acoustic metamaterials manipulate acoustic waves to achieve unparalleled properties, which is not possible using natural materials. Nonetheless, the design of such metamaterials relies on the expertise of specialists or on analytical models that require an enormous number of expensive function evaluations, making this method extremely complex and time-consuming. These complexities and exorbitant function evaluations of both fluidic and metamaterial systems embark on the need for the support of computational tools that can identify, process, and quantify the large amounts of intricacy, thus machine learning techniques. This review discusses the shortcomings of microfluidics and acoustic metamaterials, which are overcome by neoteric machine learning approaches for building smart materials. The following review ends by providing the importance and future perspective of integrating machine learning and optimization approaches with microfluidic-based acoustic metamaterials to build smart and efficient intelligent next-generation materials.
| Type: | Article |
|---|---|
| Title: | Convergence of machine learning with microfluidics and metamaterials to build smart materials |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s12008-023-01707-9 |
| Publisher version: | https://doi.org/10.1007/s12008-023-01707-9 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Machine learning, Acoustic metamaterials, Microfluidics, Intelligent systems, Smart materials |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10186053 |
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