Optimization of agricultural waste derived biochar through physical activation methods for use as reinforcement fillers in rubber composites
Bibliographic record
Abstract
Biochar has a variety of applications such as contaminant removal, soil amendment, and general carbon storage.Some recent research has investigated its application as a reinforcing filler for rubber composites.The properties of biochar are influenced by factors such as feedstock composition, pyrolysis conditions, and pre-and post-treatments.The relationship between biochar properties and performance as a reinforcing filler in rubber composites is not yet well understood.This thesis aims to determine the effect of feedstock composition and steam activation on the composition of biochar, and its efficacy as a reinforcing filler.The first study aimed to determine effective activation methods for the improvement of reinforcement in corn stover biochars.Three steam activation methods, two gaseous and one slurry-based, were performed.The biochars were assessed for their physicochemical properties and then incorporated into styrene-butadiene composites.These composites were then analyzed for their cure performance and mechanical properties in comparison to N772 carbon black-filled composites.The unactivated biochar and gaseous steam-activated biochars demonstrated poor performance as reinforcement fillers.Slurry activation improved reinforcement performance in the rubber composites, comparable to N772.Its improved performance is attributed to increases in mesoporosity, and a correlation between mesoporosity and reinforcement performance is presented.The second study investigated different feedstocks chosen for suspected valuable properties for reinforcement performance.Two nut shell feedstocks (hazelnut and walnut) and two grain husk feedstocks (oat and rice) were used to produce biochar.These biochars were also subjected to a slurry-based steam activation.The biochar samples were fully characterized, then used in styrene-butadiene rubber composites.The mechanical properties of the resulting composites were analyzed and compared to the N772 filled composite.Nut shell biochars provided very good reinforcement properties with and without activation and met or exceeded the performance of carbon black filled composites, despite having poorer dispersion.High silica content was not found to be beneficial to reinforcement performance.Activation increased mesoporosity and carbon content in all biochars and resulted in various mechanical improvements in formulated Chapter 2: Current and future research into biowaste candidates for rubber composite additives Abstract Rubber composites are highly engineered materials composed of a multitude of additives in addition to natural and/or synthetic rubber.These additives range from reinforcing fillers, to processing aids, to protectants, to cross-linking systems.Current commercial additives are generally sourced from petroleum sources, including carbon black and various petrochemicals.As research into more sustainable production practices has increased considerably in recent years, identifying and developing effective bio-sourced alternatives to these components of rubber composites warrants investigation.Waste biomass is an attractive raw material source as it is readily abundant and cost-effective.Current research has shown that waste biomass can be used to produce compounds that have similar physical and chemical properties to many rubber additives.Some have demonstrated efficacy in polymer formulations, while others have developed these components for entirely different applications.This review overviews additive types in rubber composites, their function, the current commercial fillers used, discusses current novel biowaste alternatives, and develops hypothetical candidates for future investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".