Sponge-Like Porous Metal Surfaces from Anodization in Very Concentrated Acids
Bibliographic record
Abstract
High surface area metals are of great importance for applications ranging from catalysts and electrodes to sensors or biomaterials. Many patents and scientific papers are devoted to a range of manufacturing approaches commonly involving multistep processing under harsh conditions, lacking general applicability and bearing the potential for contamination. Here we demonstrate the fabrication of porous metal layers by anodization at moderate voltages in highly concentrated acids. Porous metal layers were produced on copper, silver, iron and nickel using 99% phosphoric and sulfuric acids. The porous layer thickness can be tuned up to over one micrometer. Structures develop in 4 to 30 minutes independent of substrate purity or crystallographic features. The mechanism is believed to involve templated etching due to a near-stagnant bubble layer in a highly viscous electrolyte near the anode. It is therefore not dependent on any particular chemistry, as long as anodic oxygen bubbles are evolved at a sufficient rate. Since the principal processes of electropolishing are still operational, the surfaces remain flat at a larger scale, even though the optical properties (reflectivity, SERS activity) have changed significantly. Our method is reproducible, cheap, clean, fast and versatile, leading to a wider range of applications for porous metal surfaces.
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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) | 0.001 | 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".