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Record W2990088022 · doi:10.1177/0021998319888734

Influence of pyrolytic thermal history on olive pruning biochar and related epoxy composites mechanical properties

2019· article· en· W2990088022 on OpenAlexaff
Mattia Bartoli, Muhammad Abid Nasir, Pravin Jagdale, Elisa Passaglia, Roberto Spiniello, Carlo Rosso, Mauro Giorcelli, Massimo Rovere, Alberto Tagliaferro

Bibliographic record

VenueJournal of Composite Materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicFlame retardant materials and properties
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBiocharPyrolytic carbonMaterials scienceEpoxyPyrolysisComposite materialPruningThermosetting polymerFiller (materials)Carbon fibersIncinerationPulp and paper industryComposite numberWaste managementBotany

Abstract

fetched live from OpenAlex

Olive pruning is waste from olive cultivation and is generally disposed of through incineration. Olive pruning can, however, be salvaged by pyrolysis, which also produces an interesting carbon-based material known as biochar. Biochar has been proved as a suitable filler which improves the mechanical properties of epoxy composites. Despite this, literature has few studied focused on the relationship between biochar thermal history and the properties it induces in related biochar containing composites. In this work, we report a morphological analysis of biochar produced at different pyrolytic high treatment temperatures (400℃, 600℃, 800℃, and 1000℃) using different heating rates (5℃/min, 15℃/min, and 50℃/min). We investigate the effect of different biochar morphology on the biochar epoxy-related composites, proving the tuneability of the mechanical properties of composites according to the thermal history of the biochar employed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.197
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations47
Published2019
Admission routes1
Has abstractyes

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