ArticleACS nano2025
Interpretable Machine Learning for Evaluating Nanogenerators' Structural Design.
Article in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- A biomimetic, ultralow-power edge-AI-empowered and self-sustaining gait analysis system.Science advances · 2026Article
- Charge-Capacitance Channel Decomposition Reveals Fabrication-Tolerant Design Windows for Disk Triboelectric Nanogenerators.Materials (Basel, Switzerland) · 2026Article
- A dual-track, physics-informed framework for predicting TENG open circuit voltage and short circuit current.iScience · 2026Article
- An Electromechanical Power Law in Sustainable Thermally Drawn Triboelectric Nanocomposite Fibers for Sensing in Continuum Robots.ACS applied materials & interfaces · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The limited battery life in modern mobile, wearable, and implantable electronics critically constrains their operational longevity and continuous use. Consequently, as a self-powered technology, triboelectric nanogenerators (TENGs) have emerged as a promising solution to this. Traditional approaches for evaluating TENG structural design typically require manual, repetitive, time-consuming, and high-cost finite element modeling or experiments. To overcome this bottleneck, we developed a fully automated platform that leverages machine learning (ML) techniques. Our framework contains an artificial neuron network-based surrogate model that can provide accurate and reliable performance predictions for any structural parameters and a TreeSHAP interpretable ML model that can generate precise global and local insights for TENG structural parameters. Our platform shows broad adaptability to multiple TENG structures. In summary, our platform is an integrated platform that utilizes interpretable ML techniques to solve the complex multidimensional TENG structural evaluation problem, marking a significant advancement in TENG design and supporting sustainable energy solutions in mobile electronics.
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Registered trials
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.