ArticleScientific reports2026
Computational framework for multi-objective optimization of activated biochar properties using machine learning and evolutionary algorithms.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Process modeling and optimization of biochar production: contemporary and emerging techniques for environmental engineering applications.Biodegradation · 2026Review
- Response Surface Methodology for Biochar Performance Optimization: A Comprehensive Bibliometric Review.Molecules (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Climate neutrality and renewable energy expansion demand multifunctional materials capable of simultaneous carbon sequestration and electrochemical energy storage. Agricultural residue biochar offers dual-function potential, yet conventional pyrolysis does not systematically optimize competing objectives. This study presents a simulation-based computational framework integrating multi-output random forest surrogate modeling with differential evolution algorithms to identify Pareto-optimal process configurations balancing specific surface area, CO
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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.