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Record W4389509025 · doi:10.1002/adfm.202370294

Ultra‐Flyweight Cryogels of MXene/Graphene Oxide for Electromagnetic Interference Shielding (Adv. Funct. Mater. 50/2023)

2023· article· en· W4389509025 on OpenAlexaff
Ahmadreza Ghaffarkhah, Seyyed Alireza Hashemi, Sara Rostami, Majed Amini, Farhad Ahmadijokani, Ali Pournaghshband Isfahani, Sameer Mhatre, Orlando J. Rojas, Milad Kamkar, Stefan Wuttke, Masoud Soroush, Mohammad Arjmand

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of WaterlooUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceGrapheneOxideElectromagnetic shieldingPorosityNanoparticleNanotechnologyElectromagnetic interferenceComposite material

Abstract

fetched live from OpenAlex

Ultra-Flyweight Cryogels In article number 2304748, Stefan Wuttke, Masoud Soroush, Mohammad Arjmand, and co-workers devise a method to craft ultra-lightweight cryogels from Ti3C2Tx/graphene oxide by employing interfacial jamming of nanoparticle surfactants. This approach stabilizes aqueous suspensions of nanoparticles in a nonpolar medium, generating filamentous liquids as templates for cryogel production. These cryogels possess multi-scale porosity, optimizing their performance in electromagnetic interference shielding.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.251
Teacher spread0.231 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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