Microfluidic synthesis of platinum nanoparticles supported on reduced graphene oxide, titanium dioxide, and carbon for <scp>PEM</scp> fuel cells
Bibliographic record
Abstract
Abstract Platinum (Pt) nanoparticles on various supports, such as commercial carbon powder, titanium dioxide (TiO 2 ), and reduced graphene oxide (rGO), were synthesized using a continuous flow microfluidic system. First, the support materials were separately synthesized using the sonochemical technique followed by the loading of Pt. The Pt nanoparticles on different supports were characterized using Brunauer–Emmett–Teller (BET) for surface area and porosity analysis; X‐ray diffraction (XRD) for structural confirmation; Fourier transform infrared (FTIR) spectroscopy and X‐ray photoelectron spectroscopy (XPS) for surface composition; and transmission electron microscopy (TEM) for morphological investigation. Further, polymer electrolyte membrane (PEM) fuel cells were fabricated using the Pt nanoparticles on different supports, and cyclic voltammetry (CV), linear sweep voltammetry (LSV), and power density were performed. Among the three electrocatalysts, Pt/rGO showed the highest electrochemical performance. Pt/rGO showed higher specific activity (119 mA/cm 2 ), mass activity (238 mA/mg), current density (1274 mA/cm 2 ), and power density (497 mW/cm 2 ). We also determined the mass activity and specific activity of the electrocatalysts based on the electrochemical data. This work shows the potential of the microfluidic system to continuously synthesize the technologically important nanomaterials and their application for energy conversion devices.
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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) | 0.000 | 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".