Biomedical Applications of Chitin

Chandran, Greeshma U.; R, Jyothi P; Sambhudevan, Sreedha

doi:10.1007/978-981-16-6603-2_25-1

Cited by 1 publication

(1 citation statement)

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“…A very recent report from Das et al details the potential of ML-assisted Au-gC 3 N 4 -ZnO-based TENGs in full-body motion detection using RF, NN, and SVM algorithms, with motion detection accuracy of up to 100%. [197] Wearable bioelectronics have the potential to be revolutionized with the aid of ML, as demonstrated by Kwon et al [198] Their work details the development of printed nanomembrane hybrid electronics (NHEs) based on functionalized conductive graphene. Data obtained from hand gestures and muscle flexions made by a subject when wearing the NHE were used to train KNN and CNN algorithms to create a human-machine interface, which enabled the user to externally control drones, RC cars, and PowerPoint presentations using various hand movements.…”

Section: Electronics Optics and Human-machine Interfacesmentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Section: Electronics Optics and Human-machine Interfacesmentioning

confidence: 99%