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Record W4206970580 · doi:10.1021/acs.nanolett.1c04216

Kinetics of Sorption in Hygroscopic Hydrogels

2022· article· en· W4206970580 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNano Letters · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaSingapore-MIT Alliance for Research and Technology CentreAbdul Latif Jameel Water and Food Systems Lab, Massachusetts Institute of TechnologyMassachusetts Institute of Technology
KeywordsSelf-healing hydrogelsKineticsSorptionChemical engineeringMaterials sciencePolymerNanoporousWater vaporPorosityPorous mediumWater transportNanotechnologyChemistryAdsorptionPolymer chemistryComposite materialOrganic chemistryEnvironmental scienceEnvironmental engineeringWater flow

Abstract

fetched live from OpenAlex

Hygroscopic hydrogels hold significant promise for high-performance atmospheric water harvesting, passive cooling, and thermal management. However, a mechanistic understanding of the sorption kinetics of hygroscopic hydrogels remains elusive, impeding an optimized design and broad adoption. Here, we develop a generalized two-concentration model (TCM) to describe the sorption kinetics of hygroscopic hydrogels, where vapor transport in hydrogel micropores and liquid transport in polymer nanopores are coupled through the sorption at the interface. We show that the liquid transport due to the chemical potential gradient in the hydrogel plays an important role in the fast kinetics. The high water uptake is attributed to the expansion of hydrogel during liquid transport. Moreover, we identify key design parameters governing the kinetics, including the initial porosity, hydrogel thickness, and shear modulus. This work provides a generic framework of sorption kinetics, which bridges the knowledge gap between the fundamental transport and practical design of hygroscopic hydrogels.

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 categoriesnone
Consensus categoriesnone
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 score0.055
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.019
GPT teacher head0.263
Teacher spread0.244 · 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