A sustainable bio‐adsorbent for thermal energy storage for space heating applications
Bibliographic record
Abstract
Abstract Thermal energy storage is an emerging technology that allows the storage of heat when it is available, which can be used later. One of the available technologies for thermal energy storage is the adsorption of moisture from air by adsorbents. Several adsorbents have been studied in the literature for this application, but there is a need for a sustainable adsorbent that can be eco‐friendly, cost effective, and available for scale‐up for commercialization of the technology. The current paper focused on the synthesis of a flax shives‐based composite (equal weight percent of flax shives and salt hydrates) prepared by the impregnation method and its application in thermal energy storage. The composite showed durability, stability, and reasonable energy storage density with a very low cost per unit of energy. The structural characterization of the hybrid was performed by scanning electron microscopy (SEM)/energy‐dispersive X‐ray spectroscopy (EDX). The thermal energy storage density, as well as the charging/discharging characteristics were measured using a laboratory‐scale thermal energy storage apparatus. The flax/CaCl 2 /LiCl hybrid showed reasonable energy storage density at 74 kWh/m 3 for 50% inlet relative humidity after regeneration at 120°C. Although the energy storage density was not high, the flax/CaCl 2 composite was found to be the most cost‐effective material, as it showed the lowest cost per energy stored at 0.98 CAD/kWh at 50% relative humidity (RH) after regeneration at 120°C.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".