ArticleNature communications2023
Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed, 153 citations in OpenAlex.
- Benchmarking Large Language Models for Polymer Property Predictions.Macromolecular rapid communications · 2026Article
- Local Variations in Current Density and Selectivity in COACS energy letters · 2026Article
- A Machine Learning-Driven Pore-Scale Network Model Coupling Reaction Kinetics and Interparticle Transport for Catalytic Process Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- A self-breathing electrode enabled by interface regulation and gradient wettability engineering for industrial HNature communications · 2026Article
- Scaling deep learning for material imaging with a pseudo 3D model for domain transfer.Nature communications · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning.Nature communications · 2025Article
- AI image enhancement for failure analysis in 3D quantum information technology.Scientific reports · 2025Article
- Characterisation of hydraulic properties of commercial gas diffusion layers: Toray, SGL, MGL, woven carbon cloth.Scientific reports · 2024Article
- Simulating Two-Phase Seepage in Undisturbed Soil Based on Lattice Boltzmann Method and X-ray Computed Tomography Images.Sensors (Basel, Switzerland) · 2024Article
- Inter-site structural heterogeneity induction of single atom Fe catalysts for robust oxygen reduction.Nature communications · 2024Article
- Simultaneous multimaterial operando tomography of electrochemical devices.Science advances · 2023Article
- Atomic metal coordinated to nitrogen-doped carbon electrocatalysts for proton exchange membrane fuel cells: a perspective on progress, pitfalls and prospectives.Journal of materials chemistry. A · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (<1 mm
Identifiers
What OpenQuestion holds
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.