Fast Kinetics Biosourced Carbon-Based Sorbents for Atmospheric Water Harvesting
Bibliographic record
Abstract
Sorbent-based atmospheric water harvesting methods are emerging as promising techniques to address current and future water stress challenges. Recent advancements in sorbent design have shifted focus toward achieving both rapid sorption kinetics and high steady-state water uptake. Herein, daily water yields reaching 42, 15, 11, and 6.5 L·kg –1 ·day –1 are reported, respectively, at 95, 60, 30 and 10% relative humidity at 30 °C by employing activated, biosourced carbon-based sorbents. The specific dynamic vapor sorption performances of these biobased nanoporous sponges, Bio-NPS, were discussed as a function of their processing conditions, structures, and chemical compositions. The theoretical model proposed by Do et al. was applied to better understand the sorption mechanisms of water in different porous carbon media. The oxidation of hardwood charcoals using KOH at temperatures below 500 °C produced microporous sorbents rich in oxygen (18 atom %) and hydrophilic functions with a small specific surface. Type V water-sorption isotherms were obtained with no hysteresis. A moderate maximum water uptake (0.35 g·g –1 of sorbent at 95% relative humidity) was attained, with fast water sorption kinetics. At higher processing temperatures, sorbents presented a higher specific surface (2748 m 2 ·g –1 for the sorbent processed at 900 °C) with reduced oxygen amount and hydrophilic functions. A higher maximum water uptake was obtained, reaching 1.3 g·g –1 at 95% relative humidity, but cycles were slower. Through Bio-NPS, a significant step demonstrating effective, sustainable, and robust water production performances across a wide range of conditions has been achieved, alongside low-environmental-impact and sustainable synthesis.
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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 | 0.001 |
| 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".