Starch Particles, Energy Harvesting, and the “Goldilocks Effect”
Bibliographic record
Abstract
Abstract This study reports on the unique water vapor adsorption properties of biomass-derived starch particles (SPs). SPs offer an alternative desiccant for air-to-air energy exchangers in heating, ventilation, and air conditioning systems because of their remarkable adsorption–desorption performance. SP15 has a particle diameter (dp) of 15 μm with a surface area (SA) of 2.89 m2/g and a pore width (Pw) of 80 Å. Microporous starch particles (SP15) were compared with high amylose starch (HAS15; SA = 0.56 m2/g, dp = 15 μm, Pw = 46 Å) and silica gel (SG13; SA = 478 m2/g, dp = 13 μm, Pw = 62 Å). Transient water vapor tests were performed using a customized small-scale energy exchanger coated with SP15, HAS15, and SG13. The water swelling (%) for SP15 was ca. 2 orders of magnitude greater with markedly higher (ca. three- and six-fold) water vapor uptake compared to HAS15 and SG13, respectively. At similar desiccant coating levels on the energy exchanger, the latent effectiveness of the SP15 system was much improved (4–31%) over the HAS15 and SG13 systems at controlled operating conditions. SP15 is a unique desiccant material with high affinity for water vapor and superior adsorption properties where ca. 98% regeneration was achieved under mild conditions. Therefore, SPs display unique adsorption–desorption properties, herein referred to as the “Goldilocks effect”. This contribution reports on the utility of SPs as promising desiccant coatings in air-to-air energy exchangers for ventilation systems or as advanced materials for potential water/energy harvesting 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".