Synthesis and study of palladium-containing nanomaterials for hydrogen technologies
Bibliographic record
Abstract
We are currently facing a climate change and global warming effect due to the emission of greenhouse gases from our existing energy sources. A hydrogen-based economy is one solution to uphold our standard of living while lowering our carbon emissions. Palladium has the potential to play a major role in many aspects of the hydrogen-based economy, from purifying hydrogen to harnessing the energy via fuel cells and storing hydrogen as PdH x . In my M.Sc. study, Pd-based nanomaterials have been synthesized and examined for their applications in various hydrogen technologies. Surface properties of the synthesized Pd-based nanomaterials were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectrometry (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and N 2 gas adsorption/desorption. Electrochemical analysis of the fabricated materials was performed using cyclic voltammetry (CV), linear sweep voltammetry (LSV), and chronoamperometry (CA). Other characterization methods were also applied such as inductively-coupled plasma atomic emission spectroscopy (ICP-AES), density functional theory (OFT) calculations, and hydrogen gas adsorption/absorption. The adsorption of the catalytically poisoning species, carbon monoxide, was also examined on Pd, PdPt, and PdAu nanostructures. The relative quantities of CO molecules adsorbed to surface of the catalysts decrease in the order of: PdPt > Pd > PdAu. It was found that the possible adsorption sites of CO can be tuned by alloying Pd with metals to which CO has different binding strengths. The work done in this thesis shows that by alloying Pd with other metals, both geometric and electronic properties are changed drastically. This has a major influence on the applications of Pd for hydrogen technologies.
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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 | 0.001 | 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 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".