Moisture Content and Mechanical Properties of Bio-Waste Pellets for Fuel and/or Water Remediation Applications
Bibliographic record
Abstract
The current research is focused on the mutual comparison (mechanical properties, response to humidity) of agro-waste composite materials. The purpose of this work is directed at the valorization of agro-waste biomass products and to investigate their mechanical stability for transport or other applications (in dry and wet states). Three different types of agro-waste (oat hull (Oh), torrefied wheat straw (S), and spent coffee grounds (SCG)) were blended with kaolinite (K) and chitosan (CHT) at variable weight ratios to yield ternary composites. Mechanical properties were represented by measuring hardness (in compression mode) and elastic modulus (under tension mode). Young’s (elastic) modulus was measured both for dried and hydrated samples. The pelletized materials were prepared in two forms: crosslinked (CL) with epichlorohydrin and non-crosslinked (NCL). The hardness of the Oh pellets was poor (75 N) and decreased by four times with greater agro-waste content, while crosslinking affected the hardness only slightly. S pellets had the highest level of hardness at 40% agro-waste content (160 N), with a concomitant decrease to 120 N upon crosslinking. SCG pellets had the least change in hardness for both CL and NCL specimens (105–120 N). The trends of Young’s modulus were similar to hardness. Hydration caused the elastic modulus to decrease ca. 100-fold. In general, S and SCG composites exhibit the greatest hardness and Young’s modulus compared to Oh composites (CL or NCL) in their dry state.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".