{"title":"The application of machine learning in 3D/4D printed stimuli-responsive hydrogels","authors":"Onome Ejeromedoghene , Moses Kumi , Ephraim Akor , Zexin Zhang","doi":"10.1016/j.cis.2024.103360","DOIUrl":null,"url":null,"abstract":"<div><div>The integration of machine learning (ML) in materials fabrication has seen significant advancements in recent scientific innovations, particularly in the realm of 3D/4D printing. ML algorithms are crucial in optimizing the selection, design, functionalization, and high-throughput manufacturing of materials. Meanwhile, 3D/4D printing with responsive material components has increased the vast design flexibility for printed hydrogel composite materials with stimuli responsiveness. This review focuses on the significant developments in using ML in 3D/4D printing to create hydrogel composites that respond to stimuli. It discusses the molecular designs, theoretical calculations, and simulations underpinning these materials and explores the prospects of such technologies and materials. This innovative technological advancement will offer new design and fabrication opportunities in biosensors, mechatronics, flexible electronics, wearable devices, and intelligent biomedical devices. It also provides advantages such as rapid prototyping, cost-effectiveness, and minimal material wastage.</div></div>","PeriodicalId":239,"journal":{"name":"Advances in Colloid and Interface Science","volume":"336 ","pages":"Article 103360"},"PeriodicalIF":15.9000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Colloid and Interface Science","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001868624002835","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of machine learning (ML) in materials fabrication has seen significant advancements in recent scientific innovations, particularly in the realm of 3D/4D printing. ML algorithms are crucial in optimizing the selection, design, functionalization, and high-throughput manufacturing of materials. Meanwhile, 3D/4D printing with responsive material components has increased the vast design flexibility for printed hydrogel composite materials with stimuli responsiveness. This review focuses on the significant developments in using ML in 3D/4D printing to create hydrogel composites that respond to stimuli. It discusses the molecular designs, theoretical calculations, and simulations underpinning these materials and explores the prospects of such technologies and materials. This innovative technological advancement will offer new design and fabrication opportunities in biosensors, mechatronics, flexible electronics, wearable devices, and intelligent biomedical devices. It also provides advantages such as rapid prototyping, cost-effectiveness, and minimal material wastage.
期刊介绍:
"Advances in Colloid and Interface Science" is an international journal that focuses on experimental and theoretical developments in interfacial and colloidal phenomena. The journal covers a wide range of disciplines including biology, chemistry, physics, and technology.
The journal accepts review articles on any topic within the scope of colloid and interface science. These articles should provide an in-depth analysis of the subject matter, offering a critical review of the current state of the field. The author's informed opinion on the topic should also be included. The manuscript should compare and contrast ideas found in the reviewed literature and address the limitations of these ideas.
Typically, the articles published in this journal are written by recognized experts in the field.