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Record W2618365931 · doi:10.1139/facets-2016-0069

Application of phase correction to improve the characterization of photooxidation products of lignin using 7 Tesla Fourier-transform ion cyclotron resonance mass spectrometry

2017· article· en· W2618365931 on OpenAlexvenueno aff
Yulin Qi, Ruoji Luo, Wolfgang Schräder, Dietrich A. Volmer

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
FundersAlfried Krupp von Bohlen und Halbach-StiftungDeutsche Forschungsgemeinschaft
KeywordsFourier transform ion cyclotron resonanceMass spectrometryLigninChemistryAnalytical Chemistry (journal)Characterization (materials science)Fourier transformIon cyclotron resonanceSelected ion monitoringCyclotronIonMaterials scienceOrganic chemistryPhysicsNanotechnologyChromatographyGas chromatography–mass spectrometry

Abstract

fetched live from OpenAlex

Lignin is the second most abundant natural biopolymer and potentially a valuable alternative energy source for conventional fossil fuels. In this study, Fourier-transform ion cyclotron resonance-mass spectrometry (FTICR-MS) in conjunction with phase correction was applied to study photooxidation products of lignin using a 7 Tesla (T) mass spectrometer. The application of 7 T FTICR-MS has often been inadequate for the analysis of complex natural organic matter because of insufficient resolving power as compared with high-field FTICR, which led to incorrect assignments of elemental formulae and discontinuous plots in graphical and statistical analyses. Here, the application of phase correction to the FTICR mass spectra of lignin oxidation products greatly improved the spectral quality, and thus, readily permitted characterization of photooxidation processes of lignin compounds under simulated solar radiation conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.243
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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