Comparison of the Moisture Adsorption Properties of Starch Particles and Flax Fiber Coatings for Energy Wheel Applications
Bibliographic record
Abstract
Abstract The adsorption–desorption behavior of flax fibers (FFs) is reported in this paper. FFs are a potential desiccant material for air-to-air energy wheels, which transfer heat and moisture in building heating, ventilation, and air conditioning (HVAC) systems. The raw FFs sample was subjected to physical modification, followed by complementary material characterization to understand the relationship between its structure and its moisture uptake performance. The surface and textural properties of the modified FFs were determined by gas adsorption (N2, H2O) and gravimetric liquid water swelling studies and further supported by spectroscopic (infrared and scanning electron microscopy) results. A FF-coated small-scale energy exchanger was used to determine the moisture transfer (or latent effectiveness; εl) using single-step and cyclic testing. The FF-coated exchanger had εl values of ∼10 and 40% greater compared to similar exchangers coated with starch particles (SPs) and silica gel (SG) reported in a previous study. The enhanced surface and textural properties, along with the complex compositional structure of FFs and its greater propensity to swell in water, account for the improved performance over SPs. Thus, FFs offer an alternative low-cost, environment-friendly, and sustainable biodesiccant for air-to-air energy wheel applications in buildings. The current study contributes to an improved understanding of the structure–function relationship of biodesiccants for such energy wheel applications.
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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".