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Record W2940135700 · doi:10.11575/prism/36337

Carbon Hybrid Materials for Electromagnetic Interference (EMI) Shielding and Charge Storage Applications

2019· dissertation· en· W2940135700 on OpenAlexfundno aff
Seyyedalireza Mirkhani

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typedissertation
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEMIElectromagnetic interferenceElectromagnetic shieldingInterference (communication)Materials scienceElectromagnetic compatibilityCarbon fibersCharge (physics)Electrical engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Polymer nanocomposites having the combination of processability of the polymer matrix and versatility of the properties attainable using a large array of nanosized fillers have become the target of extensive studies for modern electronic applications. In this thesis, our attention was focused on two key advanced electrical applications of polymer nanocomposites namely, EMI (electromagnetic interference) shielding and charge storage. In EMI shielding, our goal was to alleviate the destructive interference of electronic devices caused by their emitted electromagnetic waves which adversely affect device functions as well as the performance of nearby devices. Another important aspect of EMI shielding is to protect humans from electromagnetic pollution which is responsible for many deleterious health effects. Charge storage applications are of great importance in designing embedded capacitors which is of the key challenges for the miniaturization of electronic circuits. In this thesis, we used carbon hybrid nanomaterials to prepare polymer nanocomposites for EMI shielding and charge storage applications. For EMI shielding part, we synthesized multi-wall carbon nanotubes by iron catalyst embedded in alumina substrate at five different synthesis temperatures (550°C-950°C (with 100°C intervals)) to have a clear image of how morphological changes of carbon nanotubes affected the final properties of polymer nanocomposites especially electrical properties and EMI shielding. The nanocomposites made with CNT synthesized at 650°C had significantly lower percolation threshold (around 0.4wt%) and higher electromagnetic interference shielding effectiveness (EMI SE) (20.3dB over the X-band for 3.5 wt% CNT and 1.1 mm thickness) than the other temperatures. High aspect ratio, high carbon yield and less defective structure of CNT synthesized at 650°C are the key ingredients of achieving high EMI shielding. For charge storage applications, we used two different strategies to fabricate high-k polymer nanocomposites. In the first strategy, we introduce MnO2 nanowires into PVDF/CNT polymer nanocomposites to interrupt the formation of a conductive network by CNTs which is responsible for the high loss of such systems. Our best result was achieved for CNT: MnO2NW (3.0:4.5wt%) hybrid nanocomposite with a high dielectric permittivity (50.6) and low dielectric loss (0.7), which are among the best reported values in the literature in the X-band frequency range (8.2-12.4 GHz). Finally, for the first time, we studied the charge storage properties of polymer nanocomposites contain new 2D materials called MXene and PVA as a polymer matrix. By implementing solution casting and vacuum-assisted filtration (VAF) flexible thin-films with exceptional dielectric properties (solution casting at10.0 wt% MXene: ε^'=370.5 ,tanδ=0.11 and VAF at10.0 wt% MXene: ε^'=3165.8 ,tanδ=0.1) were fabricated. The reported dielectric constants in this study are the highest values obtained with low dielectric losses for polymer nanocomposites in X-band frequency.

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), Insufficient payload (model declined to judge)
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 score0.020
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)0.0010.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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