Evidence map›Paper›PMID 40944753›Full record

ReviewBioresources and bioprocessing2025

Harnessing carbon potential of lignocellulosic biomass: advances in pretreatments, applications, and the transformative role of machine learning in biorefineries.

Lakshana G Nair, Pradeep Verma

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
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  4. Article
  5. Review
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lakshana G NairBioprocess and Bioenergy Laboratory (BPBEL), Department of Microbiology, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, 305817, India.
Pradeep VermaBioprocess and Bioenergy Laboratory (BPBEL), Department of Microbiology, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, 305817, India. pradeepverma@curaj.ac.in.ORCID http://orcid.org/0000-0003-2266-9437

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID40944753
PMCPMC12433431

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.