ArticleChemPlusChem2025
Machine Learning-Guided Design of Biomass-Based Porous Carbon for Aqueous Symmetric Supercapacitors.
Article in ChemPlusChem, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- A Perspective on Multiscale Electrodiffusion and Model Selection for Ion Transport in Energy-Storage Nanomaterials.Nanomaterials (Basel, Switzerland) · 2026Article
- Synthetic Porous Carbons for High-Energy, High-Power Supercapacitors.Chemical reviews · 2026Article
- Toward General Design of Mn-Based Layered Oxide Cathodes for Sodium-Ion Batteries: From Thermodynamic Principles to Entropy Engineering.Molecules (Basel, Switzerland) · 2026Review
- Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities.Nanomaterials (Basel, Switzerland) · 2025Review
- Pseudocapacitive Behavior of Blade-Coated MoNanomaterials (Basel, Switzerland) · 2025Article
- Machine Learning-Guided Design of Biomass-Based Porous Carbon for Aqueous Symmetric Supercapacitors.ChemPlusChem · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Biomass-derived porous carbon electrodes have attracted significant attention for high-performance supercapacitor applications due to their sustainability, cost-effectiveness, and tunable porosity. To accelerate the design and evaluation of these materials, it is essential to develop accurate and efficient strategies for optimizing their physicochemical and electrochemical properties. Herein, a machine learning (ML) approach is employed to analyze experimental data from previously reported sources, enabling the prediction of specific capacitance (F g
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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.