ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Scalable Lignin Monomer Production Via Machine Learning-Guided Reductive Catalytic Fractionation of Lignocellulose.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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.
- Hydrophobic Eutectogels Reinforced by ZnNano-micro letters · 2026Article
- Reimagining Lignin Valorization: Synthetic Biology-Enabled Sustainable Aromatic Carbon Biomanufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Precision Lignocellulosic Biorefinery: Process Regulation From Corn Stover to Products.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Synergistic pretreatment: hybrid strategies for maximum lignocellulose valorization.Biodegradation · 2026Review
- Integrating artificial intelligence and conventional approaches in sugarcane bagasse biorefineries: a review towards a circular bioeconomy.Frontiers in bioengineering and biotechnology · 2026Review
- Scalable Lignin Monomer Production Via Machine Learning-Guided Reductive Catalytic Fractionation of Lignocellulose.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Efficient valorization of lignocellulosic biomass into high-value lignin monomers is a cornerstone of sustainable biorefineries, yet the complexity of optimizing reductive catalytic fractionation limits industrial scalability. This study presents a machine learning (ML)-driven framework that harnesses 3,451 experimental data points from 54 peer-reviewed studies to model and optimize lignin monomer production. Among four advanced ML models developed, eXtreme Gradient Boosting Regression is found to achieve the highest predictive accuracy (R = 0.80-0.86) with low prediction errors (root mean square error: 3.99-8.31; mean absolute error: 2.85-6.90) for monomer production. Feature importance analysis reveals that operational parameters account for the largest influence (40-57%), followed by substrate content (25-43%) and catalyst-solvent properties (14-21%). The error between experimental and ML-predicted total monomer yields ranges from 2% to 2.6%, demonstrating robust performance of the model. Scaling this approach has the potential to process 140 million tons of aspen biomass annually, can reduce 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.