Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields

量子产额 荧光 量子点 检出限 材料科学 水热合成 产量(工程) 碳纤维 线性范围 计算机科学 热液循环 纳米技术 化学 化学工程 色谱法 物理 工程类 复合数 复合材料 量子力学 冶金
作者
Yu Han,Bijun Tang,Liang Wang,Hong Bao,Yuhao Lu,Cuntai Guan,Liang Zhang,Mengying Le,Zheng Liu,Minghong Wu
出处
期刊:ACS Nano [American Chemical Society]
卷期号:14 (11): 14761-14768 被引量:184
标识
DOI:10.1021/acsnano.0c01899
摘要

Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe3+ ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe3+ ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
orixero应助qp采纳,获得10
1秒前
醍醐不醒完成签到 ,获得积分10
2秒前
gaberella完成签到,获得积分10
3秒前
沐晴发布了新的文献求助150
4秒前
LANER完成签到 ,获得积分10
4秒前
5秒前
xmk完成签到 ,获得积分10
6秒前
cindywu完成签到,获得积分10
6秒前
ccc发布了新的文献求助10
6秒前
8秒前
9秒前
10秒前
10秒前
Star1983发布了新的文献求助10
14秒前
14秒前
坚定馒头发布了新的文献求助10
14秒前
项绝义发布了新的文献求助200
15秒前
Supreme发布了新的文献求助10
17秒前
18秒前
18秒前
20秒前
20秒前
20秒前
22秒前
青柠发布了新的文献求助10
22秒前
nannan发布了新的文献求助10
23秒前
24秒前
24秒前
24秒前
TKTK发布了新的文献求助30
24秒前
Stroeve发布了新的文献求助20
25秒前
29秒前
30秒前
31秒前
33秒前
lelelele发布了新的文献求助10
33秒前
34秒前
ZZZ发布了新的文献求助20
34秒前
爱科研发布了新的文献求助50
34秒前
Ava应助机灵的胡萝卜采纳,获得10
34秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035