ReviewBioresources and bioprocessing2025
Harnessing carbon potential of lignocellulosic biomass: advances in pretreatments, applications, and the transformative role of machine learning in biorefineries.
Review in Bioresources and bioprocessing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- High-cell density two-stage self-cycling fermentation system for the enhanced production of ethanol from steam-exploded poplar hydrolysate.Bioresources and bioprocessing · 2026Article
- Multi-scale characterization and mechanistic comparison of acid-alkali pretreatment during cell wall deconstruction of Achnatherum splendens.Bioresources and bioprocessing · 2026Article
- Nitrogen deprivation differentially alters lipid accumulation and pyrolysis-derived volatile profiles in three microalgal species.Bioresources and bioprocessing · 2026Article
- Integrated KOH-SDS-microwave pretreatment for enhanced delignification of agro-waste biomass for second-generation biorefinery applications.Biotechnology letters · 2026Article
- Recent advances in biomass deconstruction, microbial conversion, artificial intelligence, and carbon capture for sustainable bioenergy.Bioresources and bioprocessing · 2026Review
- Green Synthesis of N-Doped Carbon Quantum Dots from Chitin Nanohydrogels for Highly Sensitive FeGels (Basel, Switzerland) · 2026Article
- Integrating artificial intelligence and conventional approaches in sugarcane bagasse biorefineries: a review towards a circular bioeconomy.Frontiers in bioengineering and biotechnology · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The over-exploitation of resources has depleted non-renewable energy reserves, impacting daily life. Additionally, the excessive lignocellulosic biomass (LCB) waste from agriculture and forestry is a pressing challenge. LCB is a rich carbon source that can produce renewable biofuels and help mitigate waste concerns. LCB biorefineries are essential to the circular economy, offering eco-friendly and cost-effective solutions due to low feedstock prices. LCB, an abundant source of carbon, can be employed not only to generate renewable biofuels and other valuable products but also to mitigate waste disposal problems. LCB biorefineries are at the forefront of the circular economy, providing environmentally friendly and economically viable solutions due to the lower cost of LCB feedstocks. To enhance the efficiency of biorefineries, it is essential to overcome the recalcitrance of LCB through pretreatment, which improves the feedstock characteristics. Furthermore, exploring new methodologies and generating products beyond traditional biofuel conversions has revealed a wide range of useful products with applicability across numerous sectors. This review focuses on various trends in LCB pretreatment, highlighting current advancements in the biorefinery sector and exploring the search for innovative products and applications. This includes 3D printing, activated carbon as a biosorbent, and innovations in biocomposites and bio-adhesives aimed at sustainability. In addition, the use of LCB components in biomedical applications, such as antimicrobial/antiviral compounds, hydrogels, and the potential of cello-oligosaccharides, is explored. Lastly, the integration of machine learning in biorefineries further optimizes pretreatment and processing technologies.
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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.