Bottom-Up Top-Down Fabrication of Structurally and Functionally Tunable Hierarchical Palladium Materials
Bibliographic record
Abstract
Palladium nanomaterials have shown great promise for use in sensing and energy storage devices, and developing simple and inexpensive top-down and bottom-up methods for creating such materials has attracted much attention. In spite of tremendous progress in recent years, creating hierarchical multiscale materials which are dually optimized at the nanoscale for addressing various functional demands and are amenable for micro/macroscale integration into practical devices, remains a challenge. Here we demonstrate a rapid and simple approach based on kinetically-controlled electrochemical deposition and photolithography for creating programmable hierarchical multiscale palladium structures. Through electrochemical methods, we are able to structurally and functionally program palladium materials based on their deposition kinetics. Structures ranging from 2D thin films into 3D globules decorated with nano-needles are created with a tunable hydrogen storage capacity, Raman signal magnitude, and analytical sensitivity. Furthermore, lithography-based methods are used to pattern these programmable structures into highly organized and periodic configurations.
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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 | 0.001 |
| 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".