Evidence map›Paper›PMID 35095947›Full record

ArticleFrontiers in plant science2021

Solid-State Nuclear Magnetic Resonance as a Tool to Probe the Impact of Mechanical Preprocessing on the Structure and Arrangement of Plant Cell Wall Polymers.

Coyla R Munson, Yu Gao, Jenny C Mortimer, Dylan T Murray

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.0field-weighted citation impact, top 28% of its field
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

9 citing papers in PubMed, 15 citations in OpenAlex.

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  5. Solid-state NMR at natural isotopic abundance for bioenergy applications.Biotechnology for biofuels and bioproducts · 2025
    Review
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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

4 authors at 2 institutions in 2 countries.

Coyla R MunsonDepartment of Chemistry, University of California, Davis, Davis, CA, United States.
Yu GaoJoint BioEnergy Institute, Emeryville, CA, United States.
Jenny C MortimerJoint BioEnergy Institute, Emeryville, CA, United States.
Dylan T MurrayDepartment of Chemistry, University of California, Davis, Davis, CA, United States.
Joint BioEnergy Institute · USUniversity of California, Davis · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficient separation of the plant cell wall polymers during lignocellulose processing has been historically challenging due to insolubility of the polymers and their propensity for recalcitrant reassembly. Methods, such as "lignin first" extraction techniques, have advanced efficient biomass use, but the molecular mechanisms for recalcitrance remain enigmatic. Here, we discuss how solid-state Nuclear Magnetic Resonance (NMR) approaches report on the 3D organization of cellulose, xylan, and lignin in the plant cell wall. Recent results illustrate that the organization of these polymers varies across biomass sources and sample preparation methods, with even minimal physical processing causing significant effects. These structural differences contribute to variable extraction efficiencies for bioproducts after downstream processing. We propose that solid-state NMR methods can be applied to follow biomass processing, providing an understanding of the polymer rearrangements that can lead to poor yields for the desired bioproducts. The utility of the technique is illustrated for mechanical processing using lab-scale vibratory ball milling of

Indexed as

ball millingcellulosehemicelluloseligninrecalcitrance (saccharification), preprocessingsolid-state nuclear magnetic resonanceSorghum bicolor

Identifiers

PMID35095947
PMCPMC8790750
OpenAlexW4206449971

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.