Dual-mode sensor for intelligent solution monitoring: Enhancing sensitivity and recognition accuracy through capacitive and triboelectric sensing

摩擦电效应 电容感应 材料科学 电容 灵敏度(控制系统) 双模 光电子学 电子皮肤 电介质 电极 电气工程 电子工程 工程类 化学 物理化学 复合材料
作者
Jian Yu,Jiafeng Tang,Long Wang,Yanjie Guo,Wenyao Ma,Lei Yang,Shiyin Chen
出处
期刊:Nano Energy [Elsevier]
卷期号:118: 109009-109009 被引量:6
标识
DOI:10.1016/j.nanoen.2023.109009
摘要

Monitoring solution parameters is of utmost importance in various industries and daily applications. However, the challenge lies in using a single sensor to effectively monitor different parameters in the solution. In this study, a dual-mode sensor is proposed, capable of monitoring multiple solution parameters combined with deep learning method. The fabrication process of the dual-mode sensor is simple, involving a substrate, interdigital electrodes, and a dielectric layer. The sensitivity of the dual-mode sensor is improved by increasing the dielectric constant of the dielectric layer and optimizing the design of the interdigital electrodes. Under the capacitive sensing mode, the sensor effectively identifies solution type by detecting capacitance changes due to the conductivity of the mixed solution. Under the triboelectric sensing mode, the sensor exhibits high sensitivity to solution concentration through the coupling of the capacitive enhancement effect and the triboelectric effect. An electric switch is incorporated into the design to control the signal acquisition of the dual-mode sensor. By combining the deep learning method with the dual-mode sensor, high recognition accuracies have been achieved for both solution type and concentration, with average accuracies exceeding 95%. Furthermore, the dual-mode sensor is not limited to monitoring liquid droplets; it can also be used for monitoring the types of liquids in bottles. In addition, an intelligent system is developed to visualize the intelligent monitoring process. This work not only contributes to a better understanding of the underlying mechanisms of planar capacitive sensors (PCS) and free-standing triboelectric nanogenerators (FS-TENG), but also presents a promising method for intelligent solution monitoring.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
不配.应助BLDYT采纳,获得20
1秒前
hhf完成签到,获得积分10
4秒前
Xiaoxiannv完成签到,获得积分10
7秒前
10秒前
一杯美事完成签到,获得积分10
10秒前
TLB完成签到,获得积分10
11秒前
辛勤的灵薇完成签到,获得积分10
13秒前
一杯美事发布了新的文献求助10
14秒前
Hello应助搞怪烨伟采纳,获得10
14秒前
丰盛的煎饼应助樊川采纳,获得20
18秒前
sllytn发布了新的文献求助50
19秒前
22秒前
ZKJ完成签到,获得积分10
22秒前
灰灰喵完成签到 ,获得积分10
23秒前
24秒前
王小乐完成签到 ,获得积分10
24秒前
25秒前
星光完成签到 ,获得积分10
25秒前
善学以致用应助Aprilapple采纳,获得10
25秒前
tutulunzi完成签到,获得积分0
27秒前
自觉小夏发布了新的文献求助10
27秒前
ZKJ发布了新的文献求助10
29秒前
yzlsci完成签到,获得积分0
29秒前
寻桃阿玉完成签到 ,获得积分10
31秒前
Orange应助橙花采纳,获得10
31秒前
紧张的如南完成签到,获得积分10
33秒前
36秒前
从心从心完成签到,获得积分10
37秒前
38秒前
吴糖完成签到,获得积分10
39秒前
maolizi完成签到,获得积分10
47秒前
50秒前
51秒前
嘉芮完成签到,获得积分10
52秒前
徐叽钰完成签到,获得积分10
53秒前
54秒前
zyy发布了新的文献求助50
57秒前
57秒前
chcui发布了新的文献求助10
58秒前
heavens发布了新的文献求助10
59秒前
高分求助中
The Oxford Handbook of Social Cognition (Second Edition, 2024) 1050
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Handbook of Qualitative Cross-Cultural Research Methods 600
Chen Hansheng: China’s Last Romantic Revolutionary 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3140266
求助须知:如何正确求助?哪些是违规求助? 2791039
关于积分的说明 7797809
捐赠科研通 2447561
什么是DOI,文献DOI怎么找? 1301942
科研通“疑难数据库(出版商)”最低求助积分说明 626345
版权声明 601194