Bibliographic record
Abstract
The high resolution characterization of nanoporous gold (NPG), a material with promising functional capabilities, facilitates better understanding of its structure-property relationships. An improved characterization of NPG by atom probe tomography (APT) was recently reported, using Cu electroplating to fill the pores [1]. Atomic scale analysis of nanoligaments showed the expected core-shell structure in high detail (Figure 1), confirming and extending previous findings using other characterization methods [2]. Further to those findings, finer, more complex nanoporous materials, formed by dealloying of AgAuPt alloys, have been studied by APT as well as STEM tomography, demonstrating an ever clearer 3D picture of the structure. As the extent of aberrations found in the data is directly related to the relative evaporation fields of the metals in the sample [3], further improvement of this technique prompts the search for alternative metallic fillers. Figure 1- (a) Atom map of a single nanoligament in NPG formed by dealloying of Ag77Au23, (b) Chemical profiling across a single ligament. References [1] Ayman A. El-Zoka., Doug D. Perovic, Roger C. Newman, and Brian Langelier. "High Resolution Studies of Dealloyed Layers." In Meeting Abstracts, The Electrochemical Society, vol. 12, pp. 1259-1259, 2016. [2] T. Fujita, P. Guan, K. McKenna, X. Lang, A. Hirata, L. Zhang, T. Tokunaga, S. Arai, Y. Yamamoto, N. Tanaka, Y. Ishikawa, N. Asao, Y. Yamamoto, J. Erlebacher and M. Chen, "Atomic origins of the high catalytic activity of nanoporous gold," Nature Materials, vol. 11, pp. 775-780, 2012. [3] T.T Tsong, “Field ion formation,” Surf. Sci., vol. 70, pp. 211-233, 1978. Figure 1
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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 | 0.001 | 0.001 |
| 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".