Formation Pathways of Porous Alloy Nanoparticles through Selective Chemical and Electrochemical Etching
Bibliographic record
Abstract
Porous alloy nanomaterials are important for applications in catalysis, sensing, and actuation. Chemical and electrochemical etching are two methods to form porous nanostructures by dealloying bimetallic nanoparticles (NPs). However, it is not clear how the NPs evolve during these etching processes. Insight into the morphological and compositional transformations of the NPs during the etching is critical to understanding the nanoscale details of the dealloying process. Here, using in situ liquid phase transmission electron microscopy, the structural evolution of individual AuAg alloy NPs is tracked during both chemical and electrochemical etching of their Ag component. The observations show that the electrochemical etching produces NPs with more uniform pore sizes than the chemical etching and enables tuning the NPs porosity by modulating the electrochemical potential. The results show that at the initial stages of both etching methods, Au-rich passivation layer forms on the surface of the NPs, which is critical in preserving the NP's porous shell as pores form underneath this layer during the etching. These findings describing the selective etching and dealloying of AuAg NPs provide a critical insight needed to control the morphology and composition of porous multimetallic NPs, and paves the way for synthesizing nanomaterials with tailored chemical and physical properties for various applications.
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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) | 0.000 | 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 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".