Electrochemical Pd Nanodeposits on a Au Nanoisland Template Supported on Si(100): Formation of Pd−Au Alloy and Interfacial Electronic Structures
Bibliographic record
Abstract
Palladium nanoparticles have uniformly been electrodeposited on a Au nanoisland template (NIT) supported on a Si(100) substrate, which exhibits Au-rich, Pd-rich, and/or polycrystalline mixed structures upon annealing to 700 °C. Glancing-incidence X-ray diffraction (GIXRD) and energy-dispersive X-ray (EDX) elemental analysis of the as-deposited sample both show metallic Pd, while depth-profiling X-ray photoelectron spectroscopy (XPS) further reveals the presence of Pd-Au (and PdxSi) at the interfaces of the Pd nanodeposits on the Au NIT. Upon the sample being annealed to 700 °C, both Pd 3d(3/2) and Au 4f(7/2) XPS peaks are found to shift to lower binding energies, which further confirms Pd-Au alloy formation. The convergence of respective GIXRD features of metallic Au and Pd toward intermediate peak positions supports the formation of alloy and their crystalline nature. Depth-profiling XPS analysis of the annealed sample further shows that the Pd nanoparticles are found to consist of an ultrathin shell of PdO2, and a PdO-rich (i.e., Pd-poor) inner-core, which is consistent with the observed GIXRD patterns of PdO and Pd-Au alloy but indiscernible PdO2. We compare the above results with the experimental results for electrodeposited Pd on a bare Si(100) substrate. Our study provides new insight into the formation of Pd-Au alloy composite on Si by electrochemistry. The easy control of the Pd, Au, and Pd-Au composition in the nanodeposits as illustrated in the present method offers new flexibility for developing hybrid nanocatalysts and other applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".