Evidence map›Paper›PMID 39337732›Full record

ArticleMicromachines2024

A True Random Number Generator Design Based on the Triboelectric Nanogenerator with Multiple Entropy Sources.

Shuaicheng Guo, Yuejun Zhang, Ziyu Zhou, Lixun Wang, Zhuo Ruan, Yu Pan

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Shuaicheng GuoFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.ORCID 0009-0004-7085-3540
Yuejun ZhangFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.ORCID 0000-0003-1132-6332
Ziyu ZhouFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.
Lixun WangFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.ORCID 0000-0003-3590-3683
Zhuo RuanFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.
Yu PanFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.

Funding

National Natural Science Foundation of China 62174121, 61871244, 62134002the Fundamental Research Funds for the Provincial Universities of Zhejiang SJLY2020015the Ningbo University Student Research and Innovation Program 2024SRIP1312the Science and Technology Innovation 2025 Major Project of Ningbo City 2022Z203the S&T Plan of Ningbo Science and Technology Department 202002N3134
6 · The paper itself

Abstract

The triboelectric nanogenerator (TENG) has the potential to serve as a high-entropy energy harvester, enabling the self-powered operation of Internet of Things (IoT) devices. True random number generator (TRNG) is a common feature of encryption used in IoT data communication, ensuring the security of transmitted information. The benefits of multiplexing TENG and TRNG in resource-constrained IoT devices are substantial. However, current designs are limited by the usage scenarios and throughput of the TRNG. Specifically, we propose a structurally and environmentally friendly design based on the contact-separation structure, integrating heat fluctuation and charge decay as entropy sources. Furthermore, filtering and differential algorithms are recommended for data processing based on TENG characteristics to enhance randomness. Finally, a TENG-based TRNG is fabricated, and its performance is verified. Test results demonstrate a random number throughput of 25 Mbps with a randomness test pass rate approaching 99%, demonstrating suitability for resource-constrained IoT applications.

Indexed as

differential algorithmmulti-entropy sourcestriboelectric nanogeneratortrue random number generators

Identifiers

PMID39337732
PMCPMC11433682

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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.