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Record W2906973122 · doi:10.15625/0868-3166/28/4/12683

Characterization of NH\(_3\) Sensing Properties of P3HT+rGO+CNT Composite Films Made by Spin-coating

2018· article· en· W2906973122 on OpenAlexfundno aff
Lam Minh Long, Nguyễn Năng Định, Tran Quang Trung

Bibliographic record

VenueCommunications in Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
FundersConcordia UniversityUniversity of Engineering and Technology, Lahore
KeywordsMaterials scienceGrapheneSpin coatingCarbon nanotubeOxideCoatingComposite numberBand gapSurface roughnessCharacterization (materials science)AmmoniaChemical engineeringThin filmAtomic force microscopyNanotechnologyComposite materialOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Thin films of poly(3-hexylthiophene) (P3HT) incorporated with reduced graphene oxide (rGO) and multi-walled carbon nanotubes (CNTs) were prepared by spin-coating technique. Atomic force microscope (AFM) surface morphology, UV-Vis spectra and NH3 gas sensing of the films were studied. Results showed that the P3HT embedded with a content of 20 wt.% of rGO and 10 % of CNTs (abbreviated to P3GC) resulted in the formation of nanostructured composites, exhibiting 1.50 nm-roughness surface and a semiconducting material with a bandgap of 1.92eV. These structure and composition of the P3GC film are appropriate for making film sensors whose resistance changes as a function of gas concentration. Monitoring ammonia gas by the sensors showed that the responding time of the sensing reached a value as fast as 30 s, the response at ammonia gas content of 10 ppm attained a value as large as 0.8% and the relative sensitivity was of 0.05 %/ppm.

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 score0.006
Threshold uncertainty score0.369

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 science0.0010.000
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 head0.059
GPT teacher head0.302
Teacher spread0.242 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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