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Record W2750191811 · doi:10.1021/acsami.7b08907

Highly Flexible, Multipixelated Thermosensitive Smart Windows Made of Tough Hydrogels

2017· article· en· W2750191811 on OpenAlexafffund
Thanh‐Giang La, Xinda Li, Amit Kumar, Yiyang Fu, Shu Yang, Hyun‐Joong Chung

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMisericordia Community HospitalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpacityMaterials scienceTransmittanceOptoelectronicsInfraredSelf-healing hydrogelsInfrared heaterBlack-body radiationContrast ratioComposite materialRadiationOpticsPolymer chemistry

Abstract

fetched live from OpenAlex

In a cold night, a clear window that will become opaque while retaining the indoor heat is highly desirable for both privacy and energy efficiency. A thermally responsive material that controls both the transmittance of solar radiance (predominantly in the visible and near-infrared wavelengths) and blackbody radiation (mainly in the mid-infrared) can realize such windows with minimal energy consumption. Here, we report a smart coating made from polyampholyte hydrogel (PAH) that transforms from a transparency state to opacity to visible radiation and strengthens opacity to mid-infrared when lowering the temperature as a result of phase separation between the water-rich and polymer-rich phases. To match a typical temperature fluctuation during the day, we fine-tune the phase transition temperature between 25 and 55 °C by introducing a small amount of relatively hydrophobic monomers (0.1 to 0.5 wt % to PAH). To further demonstrate an actively controlled, highly flexible, and high-contrast smart window, we build in an array of electric heaters made of printed elastomeric composite. The multipixelated window offers rapid switching, ∼70 s per cycle, whereas the device can withstand high strain (up to 80%) during operations.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations110
Published2017
Admission routes2
Has abstractyes

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