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Record W3102923411 · doi:10.1016/j.dib.2020.106512

Data on making uniform lignin building blocks via in-situ real-time monitoring of hydroxyethyl modification

2020· article· en· W3102923411 on OpenAlexafffund
Liyang Liu, Kim Bessler, Si-Wei Chen, Mijung Cho, Qi Hua, Scott Renneckar

Bibliographic record

VenueData in Brief · 2020
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
FundersCanada Research Chairs
KeywordsLigninChemistryFourier transform infrared spectroscopyCarboxylic acidEtherAlkylGel permeation chromatographyPhenolChemical modificationOrganic chemistryArylPolymer chemistryChemical engineeringPolymer

Abstract

fetched live from OpenAlex

In this work, a lab-designed apparatus was developed to collect and record the CO2 amount during the hydroxyethyl modification of lignin. We presented the CO2 volume amount and the production rate under different reaction conditions (80 – 120 °C and 2 – 6 hrs). Nuclear magnetic resonance spectroscopy was performed to analyze the chemical structure of the hydroxyethyl lignin corresponding with different amounts of CO2 that evolved during the reaction. The aliphatic hydroxyl, aromatic hydroxyl, and carboxylic acid groups were analyzed and tabulated. The acetylated hydroxyethyl lignin samples were characterized by 13C NMR to obtain the aliphatic hydroxyl (primary and secondary), phenol (ortho substituted and ortho-free), hydroxyethyl, methoxy, and aromatic hydrogen groups semi-quantitatively. Fourier-transform infrared (FTIR) spectroscopy was adopted to analyze the surface functional groups including alkyl aryl ether bond, carboxylic acid groups, and aromatic hydroxyl groups. Gel permeation chromatography combined with a multi-angle light scattering detector and differential refractive index detector were used to obtain the molar mass of lignin before and after the modification.

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 score0.130
Threshold uncertainty score0.704

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 science0.0010.000
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 head0.050
GPT teacher head0.288
Teacher spread0.238 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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