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Record W3013250122 · doi:10.3390/mi11040339

Development of Pressure-Responsive PolyPropylene and Biochar-Based Materials

2020· article· en· W3013250122 on OpenAlexaff
Amir Noori, Mattia Bartoli, Alberto Frache, Erik Piatti, Mauro Giorcelli, Alberto Tagliaferro

Bibliographic record

VenueMicromachines · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBiocharPolypropyleneDifferential scanning calorimetryComposite materialThermal conductivityMaterials scienceElectrical resistivity and conductivityConductivityThermalChemistryPyrolysisOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

In this research paper, we reported the synthesis of biochar-based composites using biochar derived from exhausted tea leaves and polypropylene. The resulting materials were deeply characterized investigating mechanical (dynamic mechanical thermal analysis), thermal (thermogravimetrical analysis and differential scanning calorimetry), morphological (field emission scanning microscopy) and electrical properties vs. temperature. Furthermore, electrical conductivity was studied for a wide range of pressures showing an irreversible plastic deformation. An increment of one order of magnitude in the conductivity was observed in the case of 40 wt% biochar loading, reaching a value of 0.2 S/m. The material produced exhibited the properties of an irreversible pressure sensor.

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 score0.023
Threshold uncertainty score0.496

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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

Citations35
Published2020
Admission routes1
Has abstractyes

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