Self-Powered Triboelectric Nanogenerators in Intelligent Food Packaging: Recent Advances and Applications.

Zhongli Liu, Feijie Wang, Shiqiang Ouyang, Yueming Hu, Jinghan Zhang, Qianru Meng, Yan Ma, Shufeng Ma, Liqiang Wang

Abstract readReview

In one paragraph

Review in Small (Weinheim an der Bergstrasse, Germany), 2026. 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

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

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

9 authors.

Zhongli LiuJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Feijie WangJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Shiqiang OuyangJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Yueming HuJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Jinghan ZhangJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Qianru MengJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Yan MaJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.

Shufeng MaState Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, China.

Liqiang WangJiangsu Provincial Key Laboratory of Food Advanced Manufacturing Equipment Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, China.ORCID 0009-0009-8064-2276

Funding

Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology FMZ202304Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX23_2433Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX25_2648

6 · The paper itself

Abstract

As demands for food quality, safety, traceability, and sustainability increase, traditional packaging systems face challenges in power supply, information acquisition, and dynamic quality management. Triboelectric nanogenerators (TENGs), integrating energy harvesting and signal transduction in a self-powered platform, offer a promising approach for intelligent food packaging. This review summarizes recent advances in TENG-based intelligent food packaging, focusing on working mechanisms, material selection, structural optimization, and applications. Recent advances in charge regulation, interface engineering, and device architecture are discussed in relation to output performance, environmental robustness, and multimodal sensing. Representative applications integrating TENGs with pressure, humidity, vibration, and gas sensors for self-powered monitoring during food transportation, storage, and distribution are summarized. The potential of integrating TENGs with machine learning for data analysis, quality prediction, and intelligent decision-making is also discussed. Current challenges, including limited output, environmental adaptability, mechanical durability, and scalability, are analyzed, and future research directions are proposed. This review provides guidance for the development and industrialization of TENG-based intelligent food packaging and highlights opportunities for future innovation.

Indexed as

Electric Power SuppliesFood PackagingNanotechnologyMachine Learningintelligent food packagingmachine learningself‐powered sensing systemstriboelectric nanogenerators

Identifiers

PMID42417288

DOI10.1002/smll.74497

PMCPMC13495864

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.