Effect of Electrode Potential on the Surface Chemical Composition of Au-Pd Nanoparticles
Bibliographic record
Abstract
Palladium (Pd) nanoparticles have been investigated for the hydrogen evolution reaction, the hydrogen oxidation reaction, the oxygen reduction reaction, and the methanol and ethanol oxidation reaction. The high methanol/ethanol tolerance of Pd is noteworthy. A powerful approach to further improving the catalytic properties of Pd is through alloying with suitable co-metals. In the case of the ethanol oxidation reaction in alkaline media, Pd alloyed with Au has demonstrated excellent activity exceeding even that of highly active Pt catalysts (1). A complete miscibility, small lattice mismatch, and contrasting properties of Pd and Au make Au-Pd a unique model system. Marked changes in the reactivity of Au-Pd catalysts have been reported when conditioning and operating conditions are altered, which should be related to physico-chemical changes in the nanoparticles. Indeed, dealloying/dissolution and segregation of metals at the surface of nanoparticles (NPs) takes place during conditioning and operation, which have pronounced effect on the activity and stability of NPs. The migration of metals from the bulk to the nanoparticles’ surface may take place during pre-catalytic conditioning and during operation, and can be triggered by the presence of oxidation and reducing agents (2), changes in temperature, and potential cycling (3). Thus, it is critical to investigate the effect of electrode potential on metals dissolution and segregation occurring in alloyed nanoparticles. In the present work, Au-Pd NPs prepared by pulsed laser ablation in liquids were used. Pulsed laser ablation is a simple method to synthesize alloy NPs with high purity out of different material systems and with different compositions. Typically, alloy-metal targets or pressed micro-powder mixture targets are ablated or fragmentated in liquid, respectively. Notably, the technique does not require any surfactant additives to achieve NPs stability in the electrolyte, thus allowing for NPs to remain free of associated contamination from the capping agents. We will show how one can fine-tuned the Au and Pd surface fraction of bimetallic Au-Pd nanoparticles using electrochemistry. The method involves electrochemical cycling in alkaline medium at a suitable upper vertex potential, resulting in a controllable diffusion of Pd atoms from the core to the shell. This preferential outward diffusion of Pd atoms occurs because of the electrochemical adsorption of oxygen atoms at the surface of NPs, which is driven by the application of a potential. This potential has to be positive enough for oxygen atoms to adsorb at the surface of the NPs, and therefore is dependent on the nature of the surface atoms. On AuPd NPs that are enriched with Au atoms, a potential of +1.5 V vs. RHE must be applied to form ca. 1 ML of adsorbed oxygen at the surface of gold and initiate the diffusion of Pd atoms from the core to the NP surface. This approach marks a new avenue in the development of compositionally-controlled electrocatalysts and can be tailored to suit different applications with a simple switch of voltage or a controlled pretreatment. References [1] J. B. Xu, et al , Int. J. Hydrogen Energy 35 (2010) 6490-6500. [2] H.L. Xin, et al , Nano Lett., 14 (2014) 3203-3207. [3] E. Pizzutilo, et al , A CS Catal. 2017, 7, 9, 5699–5705.
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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 | 0.001 | 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 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".