Characterization of doping atoms (Ta, Nb) in advanced PEM fuel cell supports and catalysts as well as of the surface-solvent interaction of laser-generated Pt nanoparticles : A XAFS study
Bibliographic record
Abstract
PEM fuel cells are an interesting energy source for mobile applications. Efforts to improve their weaknesses cost, performance, and durability include improving the catalysts’ supports. Encouraging approaches are C-TiO2 hybrid and core-shell supports whereas titanium dioxide is commonly doped to increase its electric conductivity. In this thesis I studied the impact of Ta-/Nb- co-doping on a TiO2 nano-support, C-TiO2 hybrid/core-shell supports made by adding two kinds of carbon black and a Pt-TiO2 catalyst-support unit. XANES results show that both dopants replaced Ti atoms and are statistically distributed in their respective TiO2 host structure (which is either predominantly rutile or anatase). There is also evidence of interaction between these dopants and both the carbon supports as well as Pt catalyst, most likely via bridging oxygen atoms. EXAFS analysis reveals that Nb incorporation did distort the TiO2 host structure of at least one sample to a greater extent compared to Ta incorporation. The core-shell support displays the highest degree of disorder and smallest particle size, which is most likely correlated. Reduction/oxidation experiments show that Pt atoms in PtPd nano-catalysts supported on C-TiO2 hybrid nano-supports have a low affinity for oxidation and are easily reduced. Pulsed laser ablation in liquid (PLAL) has proven its usefulness as a nanoparticle (NP) synthesis method alternative to traditional chemical reduction methods. Additive-free Pt NPs were synthesized by PLAL and their interaction characterized in situ with H2O, a sodium phosphate buffer and sodium citrate as well as a TiO2 support. XANES results indicate that the respective NP-solvent interaction varies in strength. The ions added ex situ diffuse through the particles’ electric double layer and interact electrostatically with the Stern plane. Consequently, these ions weaken the interaction of the functional OH-groups which are bound to the partially oxidized platinum surfaces and cause their partial reduction. Comparing XANES/EXAFS spectra of laser-generated with wet-chemically synthesized Pt NPs indicate different types of Pt-O bonds: a Pt(IV)O2-type in case of wet-chemical and a Pt(II)O-type in case of laser-generated NPs. A comparison of unsupported laser-generated platinum NPs in H2O with TiO2-supported ones shows no whiteline intensity differences and also an identical number of Pt-O bonds in both cases. This suggests that in the deposition process at least part of the double layer coating stays intact and that the ligand-free particle properties are preserved.
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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".