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Record W2073726401 · doi:10.1002/pc.23502

Facile preparation and characterization of sodium alginate/graphite conductive composite hydrogel

2015· article· en· W2073726401 on OpenAlexaff
Bing Qu, Junrong Li, Huining Xiao, Beihai He, Liying Qian

Bibliographic record

VenuePolymer Composites · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of New Brunswick
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsGraphiteMaterials scienceThermogravimetric analysisComposite numberSelf-healing hydrogelsComposite materialScanning electron microscopeThermal stabilityRaman spectroscopyChemical engineeringPolymer chemistry

Abstract

fetched live from OpenAlex

Conductive composite hydrogels based on sodium alginate (SA) and graphite were fabricated by a facile method via dispersing homogeneously conductive graphite into SA hydrogel matrix. The hydrogel was formed by in situ release of Ca 2+ from Ca–EDTA, thus eliminating the multistep reactions and tedious purification compared to the previous work. Raman spectra, scanning electron microscopy (SEM), X‐ray diffraction (XRD), and thermogravimetric analysis (TGA) were used to characterize the structure, crystalline nature, and thermostability of SA/graphite composite hydrogels. The SA/graphite composite hydrogels exhibited the improved network and layer‐type structure. The thermal stability of the hydrogel decreased slightly after the graphite was incorporated into the SA hydrogel matrix regardless of the content of graphite. The enhanced mechanical strength of SA/graphite composite hydrogel was achieved via increasing the f value (i.e., [Ca 2+ ]/[COO ‐ in alginate]) and lowering graphite content. The conductivity of the composite hydrogels could be varied in a broad range, reaching up to 10 −3 S/cm, mainly depending on the content of graphite and the f value. POLYM. COMPOS., 37:3050–3056, 2016. © 2015 Society of Plastics Engineers

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 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 score0.007
Threshold uncertainty score0.562

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2015
Admission routes1
Has abstractyes

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