Combined helical-blade-strengthened co-flow focusing and high-throughput screening for the synthesis of highly homogeneous nanoliposomes

分散性 材料科学 纳米技术 吞吐量 粒径 粒子(生态学) 微流控 微通道 纳米医学 自动化 计算机科学 纳米颗粒 机械工程 工艺工程 工程类 化学工程 电信 海洋学 地质学 高分子化学 无线
作者
Haoji Wang,Zhengyi Lan,Ruizhi Tian,Liang Xiao,Fuhao Jia,Ming Ma,H Chen
出处
期刊:Nano Today [Elsevier BV]
卷期号:56: 102301-102301 被引量:3
标识
DOI:10.1016/j.nantod.2024.102301
摘要

Nanoliposomes have been widely employed as promising drug delivery vehicles for the treatment of various diseases. However, the large-scale synthesis of drug-loaded nanoliposomes manifesting a highly uniform particle size is impeded by several unmet challenges. Herein a novel helical-blade-strengthened co-flow focusing (HBSCF) device was developed by installing multiple parallel helical blades in a commonly used co-flow focusing microfluidic device. This transformation in the microchannel structure may accelerate the mixing of aqueous and lipid streams in a radial direction, thereby affording the production of nanoliposomes with a significantly lower polydispersity index (PDI) value in terms of particle size. Moreover, a high-throughput experimental platform was developed by employing HBSCF device alongside its integration with various automation modules, which afforded 672 distinct experimental schemes for the synthesis and size characterization of drug-loaded nanoliposomes within 40 h. Afterwards, based on the above obtained large data set of nanoliposomes, a typical machine learning (ML) model pertaining to particle size was established to predict candidate synthesis schemes for the desired average particle size. Therefore, by narrowing the screening ranges through ML, the final synthesis scheme capable of producing liposomes with the desired particle size along with minimum PDI value can be precisely and rapidly obtained using automated experiments based on the same platform. Taken together, an effective integration of the HBSCF synthesis along with an automated high-throughput experimental platform may have broad implications for the industrialization and clinical application of nanomedicine.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
吱吱吱完成签到 ,获得积分10
1秒前
1秒前
1秒前
乂氼完成签到,获得积分10
1秒前
张祖伦发布了新的文献求助10
2秒前
可爱的函函应助就这样采纳,获得10
2秒前
科研通AI2S应助lee采纳,获得10
2秒前
温金凤发布了新的文献求助10
3秒前
小鱼发布了新的文献求助10
3秒前
3秒前
3秒前
一颗桃子完成签到,获得积分20
4秒前
wry发布了新的文献求助10
4秒前
吴未发布了新的文献求助10
4秒前
长安完成签到,获得积分10
4秒前
4秒前
脑洞疼应助Lang777采纳,获得10
4秒前
hhhh发布了新的文献求助10
5秒前
善良思天发布了新的文献求助10
6秒前
6秒前
一个zzq发布了新的文献求助10
6秒前
6秒前
YuGe发布了新的文献求助30
6秒前
在水一方应助小笼包采纳,获得10
6秒前
凶狠的葶发布了新的文献求助10
8秒前
ifly发布了新的文献求助10
9秒前
11秒前
蓝豆子发布了新的文献求助10
11秒前
SciGPT应助长安采纳,获得10
11秒前
神勇中道发布了新的文献求助10
11秒前
12秒前
CipherSage应助不爱学习的cc采纳,获得30
13秒前
张祖伦完成签到 ,获得积分10
14秒前
补药学习完成签到,获得积分10
16秒前
FJH发布了新的文献求助10
16秒前
领导范儿应助小白采纳,获得10
16秒前
Hello应助lee采纳,获得10
16秒前
yang发布了新的文献求助10
18秒前
18秒前
18秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Mapping the Stars: Celebrity, Metonymy, and the Networked Politics of Identity 400
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
The phrasal lexicon 200
Solving Nonlinear Equations with Newton's Method 200
Reference Guide for Dynamic Models of HVAC Equipment 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3836107
求助须知:如何正确求助?哪些是违规求助? 3378496
关于积分的说明 10504553
捐赠科研通 3098068
什么是DOI,文献DOI怎么找? 1706249
邀请新用户注册赠送积分活动 820923
科研通“疑难数据库(出版商)”最低求助积分说明 772312