Synthesis and electrochemical study of Pd- and Pt- based nanostructured materials
Bibliographic record
Abstract
We are currently facing climate change and global warming effects due to the emission of \ngreenhouse gases from our existing energy sources. Proton exchange membrane fuel cells are \none of the most efficient alternatives for power generation with the potential for greater than \n80% efficiency in combined heat and power systems. Pd- and Pt-based catalysts are deemed to \nhold great potential in many aspects of energy conversion; from the purification and storage of \nhydrogen as palladium metal hydride (PdHx), to harnessing clean energy via fuel cells. In this \nM.Sc. study, Pd and Pt-based nanomaterials have been synthesized and examined to elucidate \ntheir applications in hydrogen storage and for fuel cell catalysis. The surface properties of the \nsynthesized Pd and Pt-based nanomaterials were characterized by scanning electron microscopy \n(SEM), energy dispersive X-ray spectrometry (EDS), X-ray diffraction (XRD), and X-ray \nphotoelectron spectroscopy (XPS). Electrochemical analysis of the fabricated nanomaterials was \nperformed using cyclic voltammetry (CV), linear sweep voltammetry (LSV), \nchronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). \nNovel nanoporous Pd-Ag electrocatalysts were synthesized utilizing a facile hydrothermal \nmethod. The Ag content of the Pd-Ag alloy varied from 0 to 40%. EDS, XPS and inductively \ncoupled plasma (ICP) were used to directly and indirectly characterize the composition of the \nformed Pd-Ag nanostructures. XRD analysis confirmed that the Pd-Ag nanomaterials were \nalloys that contained a face-centered cubic structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".