A Guideline to Evaluate Sorbent Performance for Atmospheric Water Harvesting
Bibliographic record
Abstract
Access to safe drinking water is one of the most urgent challenges of our time. According to the United Nations (2023), more than 2 billion people still lack access to safely managed drinking water, and climate change is intensifying this crisis. Atmospheric water harvesting (AWH) technologies, particularly those based on solid sorbents such as activated carbons or metal–organic frameworks, have emerged as promising solutions capable of harvesting water even in arid and low‐humidity environments. However, the absence of standardized testing protocols and performance metrics has led to inconsistent and often noncomparable data across studies. Reported values for water uptake, regeneration energy, and cycling stability are frequently obtained under divergent conditions, limiting the practical evaluation of sorbent materials for real‐world deployment. This article proposes a unified and reproducible methodological framework for characterizing sorbents for AWH. By exploiting gravimetric and volumetric methods, as well as essential water quality metrics, seven key performance indicators are defined: water uptake capacity, humidity sensitivity, sorption/desorption kinetics, reversibility, regeneration conditions, long‐term stability, and the quality of water produced. This approach aims to accelerate the development and certification of AWH technologies by enabling clear, standardized comparisons between materials.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.015 |
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".