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Record W4245325378 · doi:10.26434/chemrxiv.11571225.v2

Liquid-Phase Exfoliation of Biochars in Green Solvents and Correlation with Solvent Parameters

2020· preprint· en· W4245325378 on OpenAlexafffund
Juliana L. Vidal, Stephanie Gallant, Douglas Richards, Stephanie MacQuarrie, Francesca M. Kerton

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsCape Breton UniversityMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandCape Breton University
KeywordsBiocharExfoliation jointSolventMaterials scienceChemical engineeringBiorefiningSawdustSoftwoodSonicationBiomass (ecology)HardwoodHydrogen peroxideYield (engineering)Organic chemistryPyrolysisChemistryComposite materialNanotechnologyBiorefineryGraphene

Abstract

fetched live from OpenAlex

Liquid-phase exfoliation (LPE) is a process frequently used to yield small sheets of layered materials. These materials are prepared via direct or indirect sonication in an ideal solvent, and the sheets produced often present remarkable chemical and physical properties. Unfortunately, the preferred solvents for exfoliation processes are frequently toxic and possess several health risks. In this work, we show the use of LPE in green solvents to access nanostructures of biochar. Biochar is a material produced after thermochemical treatment of biomass residues and it is an important tool for the sequestration of greenhouse gases. Herein, hardwood and softwood biomass residues (e.g. sludge, bark, and sawdust) are used to prepare pristine and oxidized biochars which are then exfoliated in a range of solvents. Stable dispersions containing up to 75% by weight of exfoliated biochar could be obtained. A range of solvents were screened for LPE of biochars to identify ‘green' options that could afford highly concentrated dispersions. The properties of the biochar before and after exfoliation were evaluated using Raman spectroscopy and Transmission Electron Microscopy. Correlations between effective LPE of biochar in a solvent and different solvent parameters were established. For example, LPE of oxidized biochars is more efficient in hydrogen-bond accepting solvents due to the increased concentration of carboxylic acid and alcohol functional groups within this material, when compared with pristine biochars.

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 score0.073
Threshold uncertainty score0.794

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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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