Characterization and Spatial Mapping of Crystal Lattice Strain in Nanoporous Gold
Bibliographic record
Abstract
There is a growing interest in nanoporous gold (NPG) as a promising material for next generation biosensors, actuators, and catalysts. Formed by scalable top-down methods, the dealloying of AgAu precursors yields a mechanically stable, noble metal nanostructure with active mass-specific surface areas on the order of several m2/g. This property is readily exploited for the abundance of binding sites in many useful applications. Throughout the porous layer, the predominance of surface atoms relative to the master alloy, coupled with the local curvature at the solid-pore interface, changes the strain state of the material to a large extent. The strain alters the macroscopic deformation of the material and is impacted by the adsorption and desorption of reactants. Understanding and harnessing these strain effects are pivotal in advancing the design and optimization of NPG-based functional materials for enhanced efficiency and sensitivity in diverse applications. However, despite the significant progress in understanding surface-induced strains in NPG, the variations in strain distribution from the surface to the dealloying front remain relatively unexplored. Investigating these differences is crucial for comprehension of the material's mechanical behavior, as it can provide insights into the dynamic evolution of strain during dealloying processes. Moreover, incorporation of small amounts of Pt to the master alloy, yielding NPG-Pt, fundamentally alters the structure evolution during dealloying and high temperature coarsening. The Pt-rich surfaces that result from these processes introduce a potential additional layer of complexity to the strain dynamics within the material, though this effect has not yet been explored. This work aims at unravelling the intricacies of strain distribution in NPG, especially in the context of NPG-Pt, using advanced electron microscopy. Among the techniques used for nanoscale strain resolution, four-dimensional scanning transmission electron microscopy (4D-STEM) stands out for its ability to quantify and spatially resolve lattice strain at varying magnifications. 4D-STEM was used to map strain at multiple length scales; spanning the entire nanoporous layer and at the level of individual NPG/NPG-Pt ligaments. Given the sensitivity of the method to optical distortions in the image data, another emphasis of this work was the optimization of microscope parameters and data processing to yield reliable 4D-STEM datasets. Resulting maps of lattice deformation reveal how the strain evolves in parallel with the microstructure during prolonged dealloying times. Additionally, high-magnification mapping of coarsened ligaments shows the retention of strain despite significantly reduced surface area. These findings may be used to carefully engineer strain in nanoporous metals by controlling the dealloying process, and harness bulk strain for stress-driven sensors and actuators.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".