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Record W3093308315 · doi:10.1126/sciadv.abc8605

Autonomous atmospheric water seeping MOF matrix

2020· article· en· W3093308315 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Toronto
FundersMinistry of Education - Singapore
KeywordsSorptionMatrix (chemical analysis)Chemical engineeringEnvironmental scienceMaterials scienceChemistryComposite materialAdsorptionOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The atmosphere contains an abundance of fresh water, but this resource has yet to be harvested efficiently. To date, passive atmospheric water sorbents have required a desorption step that relies on steady solar irradiation. Since the availability and intensity of solar radiation vary, these limit on-demand desorption and hence the amount of harvestable water. Here, we report a polymer-metal-organic framework that provides simultaneous and uninterrupted sorption and release of atmospheric water. The adaptable nature of the hydro-active polymer, and its hybridization with a metal-organic framework, enables enhanced sorption kinetics, water uptake, and spontaneous water oozing. We demonstrate continuous water delivery for 1440 hours, producing 6 g of fresh water per gram of sorbent at 90% relative humidity (RH) per day without active condensation. This leads to a total liquid delivery efficiency of 95% and an autonomous liquid delivery efficiency of 71%, the record among reported atmospheric water harvesters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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.

Opus teacher head0.011
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it