ArticleFrontiers in robotics and AI2021
Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications.
Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Fibrous contextual embodiments formed by task-invariant continuous blow spinning in dynamic environments.Frontiers in robotics and AI · 2026Article
- Recent Applications, Challenges, and Future Prospects of Microbial Fuel Cells: A Review.Global challenges (Hoboken, NJ) · 2025Review
- Research Progress in Electroactive Polymers for Soft Robotics and Artificial Muscle Applications.Polymers · 2025Review
- Bio-inspired designs: leveraging biological brilliance in mechanical engineering-an overview.3 Biotech · 2024Review
- Machine Learning in Bioelectrocatalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Using AI and BES/MFC to decrease the prediction time of BODEnvironmental monitoring and assessment · 2023Article
- Customized Multichannel Measurement System for Microbial Fuel Cell Characterization.Bioengineering (Basel, Switzerland) · 2023Article
- Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence.Frontiers in neurorobotics · 2022Review
- Editorial: Machine Learning Techniques for Soft Robots.Frontiers in robotics and AI · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.
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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.