Field electron emission enhancement of graphenated MWCNTs emitters following their decoration with Au nanoparticles by a pulsed laser ablation process
Bibliographic record
Abstract
A plasma-enhanced chemical vapor deposition (PECVD) process was adapted to alter the growth of multiwall carbon nanotubes (MWCNTs) so that graphene sheets grow out of their tips. Gold nanoparticle (Au-NP) decoration of graphenated MWCNTs (g-MWCNTs) was obtained by subsequent decoration by a pulsed laser deposition (PLD) process. By varying the number of laser ablation pulses (N(Lp)) in the PLD process, we were able to control the size of the gold nanoparticles and the surface coverage of the decorated g-MWCNTs. The presence of Au-NPs, preferentially located at the tip of the g-MWCNTs emitters, is shown to significantly improve the field electron emission (FEE) properties of the global g-MWCNT/Au-NP nanohybrid films. Indeed, the electric field needed to extract a current density of 0.1 μA cm(-)(2) from the g-MWCNT/Au-NP films was decreased from 2.68 V μm(-1) to a value as low as 0.96 V μm(-1). On the other hand, UV photoelectron spectroscopy (UPS) characterization revealed a decrease in the global work function of the Au-decorated g-MWCNT nanohybrids compared to that of bare g-MWCNT emitters. Surprisingly, the work function of g-MWCNT was found to decrease from 4.9 to 4.7 eV with the addition of Au-NPs-a value lower than the work function of both materials worth 5.2 and 4.9 eV for gold and g-MWCNT, respectively. Our results show that the N(Lp) dependence of the FEE characteristics of the g-MWCNT/Au-NP emitters correlates well with their work function changes. Fowler-Nordheim-theory-based calculations suggest that the significant FEE enhancement of the emitters is also caused by the Au-NPs acting as nanoscale electric field enhancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".