Characterization of recycled styrene butadiene rubber ground tire rubber:<scp>C</scp>ombining X‐ray fluorescence, differential scanning calorimetry, and dynamical thermal analysis for quality control
Bibliographic record
Abstract
ABSTRACT Appraisal of the main rubber characterization techniques for styrene butadiene rubber (SBR) was performed on standard SBR samples as well as recycled ground tire rubber (GTR) from an industrial tire recycling facility, containing a blend of SBR and natural rubber. The aim of the work was to provide additional information relevant to quality control in the field of rubber recycling. Benchmark characterization of industrial samples by inductively coupled plasma optical emission spectrometry, atomic absorption spectrometry, solid‐state proton nuclear magnetic resonance, and elemental (CHNS) analysis are reported. X‐ray fluorescence spectrometry is shown to be rapid and quantitative for determining the zinc content in an industrial context. Thermogravimetric analysis, already used to determine carbon black and inorganic material content in rubbers and GTR, is recommended for determination of monomer weight ratios of SBR sources not containing other rubbers, but not for GTR. Differential scanning calorimetry (DSC) measurements of the glass‐transition show that changes in monomer ratio affect glass‐transition temperature values, and therefore, DSC can be used to detect changes in rubber composition from batch to batch. These results show that DSC and X‐ray fluorescence spectroscopy characterization techniques can be used for GTR and may lead to more thorough and rapid quality control procedures of these complex samples. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 42692.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".