Evidence map›Paper›PMID 41486237›Full record

ReviewNano-micro letters2026

Harnessing the Power from Ambient Moisture with Hygroscopic Materials.

Daozhi Shen, Fangzhou Li, Yanjie Su, Limin Zhu

Abstract readReview
In one paragraph

Review in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

4 authors.

Daozhi ShenSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China. dzshen@sjtu.edu.cn.
Fangzhou LiSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
Yanjie SuDepartment of Micro/Nano Electronics, School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China. yanjiesu@sjtu.edu.cn.
Limin ZhuSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China. zhulm@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Moisture electricity generation (MEG) has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy, which holds great promise for renewable energy and constructing self-powered electronics. In this review, we begin by outlining the fundamental mechanisms-ion diffusion, electric double layer formation, and streaming potential-that govern charge transport for MEG in moist environments. A comprehensive survey of material innovations follows, highlighting breakthroughs in carbon-based materials, conductive polymers, hydrogels, and bio-inspired systems that enhance MEG performance, scalability, and biocompatibility. We then explore a range of device architectures, from planar and layered systems to flexible, miniaturized, and textile-integrated designs, engineered for both energy conversion and sensor integration. Key challenges are analyzed, along with strategies for overcoming them. We conclude with a forward-looking perspective on future directions, including hybrid energy systems, AI-assisted material design, and real-world deployment. This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.

Indexed as

HydroelectricityHygroscopic materialMaterials designMoisture electricity generationNanogenerators

Identifiers

PMID41486237
PMCPMC12765796

What OpenQuestion holds

Textmetadata
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.