Evidence map›Paper›PMID 37177395›Full record

ReviewSensors (Basel, Switzerland)2023

Modular Design in Triboelectric Sensors: A Review on the Clinical Applications for Real-Time Diagnosis.

Zequan Zhao, Yin Lu, Yajun Mi, Qiliang Zhu, Jiajing Meng, Xueqing Wang, Xia Cao, Ning Wang

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Zequan ZhaoCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Yin LuCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Yajun MiCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Qiliang ZhuCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Jiajing MengCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Xueqing WangCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Xia CaoBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China.
Ning WangCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0001-9772-7270
University of Science and Technology Beijing · CNChinese Academy of Sciences · CN

Funding

National Natural Science Foundation of China NSFC No. 51873020the University Basic Scientific Research Business Fee (No. FRF-MP-20-38).
6 · The paper itself

Abstract

Triboelectric nanogenerators (TENGs) have garnered considerable interest as a promising technology for energy harvesting and stimulus sensing. While TENGs facilitate the generation of electricity from micro-motions, the modular design of TENG-based modular sensing systems (TMSs) also offers significant potential for powering biosensors and other medical devices, thus reducing dependence on external power sources and enabling biological processes to be monitored in real time. Moreover, TENGs can be customised and personalized to address individual patient needs while ensuring biocompatibility and safety, ultimately enhancing the efficiency and security of diagnosis and treatment. In this review, we concentrate on recent advancements in the modular design of TMSs for clinical applications with an emphasis on their potential for personalised real-time diagnosis. We also examine the design and fabrication of TMSs, their sensitivity and specificity, and their capabilities of detecting biomarkers for disease diagnosis and monitoring. Furthermore, we investigate the application of TENGs to energy harvesting and real-time monitoring in wearable and implantable medical devices, underscore the promising prospects of personalised and modular TMSs in advancing real-time diagnosis for clinical applications, and offer insights into the future direction of this burgeoning field.

Indexed as

ElectricityElectric Power SuppliesHumansMotionTechnologymodular designreal-time diagnosisself-powered systemtriboelectric sensor

Identifiers

PMID37177395
PMCPMC10181202
OpenAlexW4366828715

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.