Graphene-PEDOT-Platinum Tertiary Composite Material Based Catalyst for Hydrogen Evolution Reaction
Bibliographic record
Abstract
We fabricated a Laser Scribed Graphene-PEDOT-Platinum (LSG-PEDOT-Pt) tertiary composite material, using a three-step synthesis process, to investigate its catalytic hydrogen evolution reaction (HER) performance: 1. Graphene oxide film was photo-thermally reduced via a CO2 laser into laser-scribed graphene (LSG). 2. A layer of PEDOT polymer was grown on top of the LSG film using vapor assisted polymerization methods to yield a LSG-PEDOT composite film. 3. Platinum nanoparticles were deposited on the LSG-PEDOT composite via pulse-potentiometry. The vapor-assisted polymerized PEDOT decorates the surface of the LSG, forming nanoribbon/wire like structures. The pulse potentiommetry enables platinum to grow vertically on top of the LSG-PEDOT substrate, forming arrays of nanowires. The composite material shows better HER performances than metal platinum in 0.5M H2SO4 solution, with a very low Tafel slope of 29mV/decade, 15mV (vs. RHE) onset overpotential, and achieves 10mA/cm2 current density at a low overpotential of 35mV (vs. RHE). Even though LSG-PEDOT is an intrinsically poor HER catalyst, its high surface area, high conductivity and low charge transfer resistance make LSG-PEDOT a good substrate for HER catalysis material, as demonstrated by the performances of LSG-PEDOT-Pt. LSG-PEDOT composite material could be a promising base material for developing non-noble metal based catalysts. Figure 1
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".