Evidence map›Paper›PMID 32468723›Full record

ArticleChemSusChem2020

Nontargeted Analysis Strategy for the Identification of Phenolic Compounds in Complex Technical Lignin Samples.

Jens Prothmann, Kena Li, Christian Hulteberg, Peter Spégel, Margareta Sandahl, Charlotta Turner

Open access · hybridAbstract read
In one paragraph

Article in ChemSusChem, 2020. 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
1.3field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 20 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Jens ProthmannLund University, Department of Chemistry, Centre for Analysis and Synthesis, P.O. Box 124, 22100, Lund, Sweden.
Kena LiLund University, Department of Chemical Engineering, P.O. Box 124, 21100, Lund, Sweden.
Christian HultebergLund University, Department of Chemical Engineering, P.O. Box 124, 21100, Lund, Sweden.
Peter SpégelLund University, Department of Chemistry, Centre for Analysis and Synthesis, P.O. Box 124, 22100, Lund, Sweden.
Margareta SandahlLund University, Department of Chemistry, Centre for Analysis and Synthesis, P.O. Box 124, 22100, Lund, Sweden.
Charlotta TurnerLund University, Department of Chemistry, Centre for Analysis and Synthesis, P.O. Box 124, 22100, Lund, Sweden.ORCID http://orcid.org/0000-0001-9466-1149
Lund University · SE

Funding

Stiftelsen för Strategisk Forskning RBP 14-0052
6 · The paper itself

Abstract

Lignin is the second most abundant biopolymer in nature and a promising renewable resource for aromatic chemicals. For the understanding of different lignin isolation and conversion processes, the identification of phenolic compounds is of importance. However, given the vast number of possible chemical transformations, the prediction of produced phenolic structures is challenging and a nontargeted analysis method is therefore needed. In this study, a nontargeted analysis method has been developed for the identification of phenolic compounds by using an ultrahigh-performance supercritical fluid chromatography-high-resolution multiple stage tandem mass spectrometry method, combined with a Kendrick mass defect-based classification model. The method is applied to a Lignoboost Kraft lignin (LKL), a sodium lignosulfonate lignin (SLS), and a depolymerized Kraft lignin (DKL) sample. In total, 260 tentative phenolic compounds are identified in the LKL sample, 50 in the SLS sample, and 77 in the DKL sample.

Indexed as

analytical methodsbiomasschromatographymass spectrometrysupercritical fluids

Identifiers

PMID32468723
PMCPMC7540015
OpenAlexW3030548151

What OpenQuestion holds

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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.