Incorporation of carbonised water hyacinth for increasing mechanical, thermal, and odour properties of fabrics
Bibliographic record
Abstract
This study aims to carbonise water hyacinth, activate it chemically using ZnCl₂, and apply the resulting material in textile finishing to enhance selected properties of clothing.Water hyacinth, a widely occurring free-floating perennial aquatic weed, is often regarded as invasive; its conversion into a value-added textile finish therefore presents both environmental and functional benefits.The carbonisation process produces concave, oval-shaped carbon nanoparticles measuring approximately 200 nm by 100 nm, which are subsequently applied to fabrics using padding, coating, coat-pad curing, and infrared dyeing techniques.Among these, the coated samples produced through multiple padding cycles exhibit the most promising performance in terms of FTIR characteristics, air permeability, and stiffness.Although the treatment does not significantly improve mechanical properties such as tensile, bursting, or tear strength, it does enhance thermal behaviour, yielding a 13.8% increase in thermal insulation (CLO) and a 14.3% increase in thermal resistance (m²ꞏK/W).Notably, the treated samples also demonstrate 100% odour resistance.Energy Dispersive Spectroscopy confirms the material composition, showing 99.32% carbon and 0.32% zinc distributed uniformly across the examined areas, indicating successful particle deposition.
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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".