Evidence map›Paper›PMID 36969966›Full record

ArticleMicrosystems & nanoengineering2023

Multimodal MEMS vibration energy harvester with cascaded flexible and silicon beams for ultralow frequency response.

Haizhao Feng, Ling Bu, Zhangshanhao Li, Sixing Xu, Bingmeng Hu, Minghao Xu, Siyao Jiang, Xiaohong Wang

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Article in Microsystems & nanoengineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

4 citing papers in PubMed.

  1. Article
  2. Article
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  4. Freeze-Cast Porous Textured BaTiOACS applied energy materials · 2025
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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.

Haizhao Feng *School of Integrated Circuit, Tsinghua University, 100084 Beijing, China.ORCID 0000-0002-0750-2578
Ling BuSchool of Information Engineering, China University of Geosciences, 100083 Beijing, China.ORCID 0000-0001-6149-4540
Zhangshanhao LiSchool of Integrated Circuit, Tsinghua University, 100084 Beijing, China.
Sixing XuCollege of Semiconductors (College of Integrated circuits), Hunan University, 430001 Changsha, China.
Bingmeng HuSchool of Integrated Circuit, Tsinghua University, 100084 Beijing, China.ORCID 0000-0002-0641-3805
Minghao XuSchool of Integrated Circuit, Tsinghua University, 100084 Beijing, China.
Siyao JiangSchool of Integrated Circuit, Tsinghua University, 100084 Beijing, China.
Xiaohong WangSchool of Integrated Circuit, Tsinghua University, 100084 Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scavenged energy from ambient vibrations has become a promising energy supply for autonomous microsystems. However, restricted by device size, most MEMS vibration energy harvesters have much higher resonant frequencies than environmental vibrations, which reduces scavenged power and limits practical applicability. Herein, we propose a MEMS multimodal vibration energy harvester with specifically cascaded flexible PDMS and "zigzag" silicon beams to simultaneously lower the resonant frequency to the ultralow-frequency level and broaden the bandwidth. A two-stage architecture is designed, in which the primary subsystem consists of suspended PDMS beams characterized by a low Young's modulus, and the secondary system consists of zigzag silicon beams. We also propose a PDMS lift-off process to fabricate the suspended flexible beams and the compatible microfabrication method shows high yield and good repeatability. The fabricated MEMS energy harvester can operate at ultralow resonant frequencies of 3 and 23 Hz, with an NPD index of 1.73 μW/cm

Indexed as

Electrical and electronic engineeringNanoscale devicesStructural properties

Identifiers

PMID36969966
PMCPMC10033895

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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.