Comprehensive Structural, Surface-Chemical and Electrochemical Characterization of Nickel-Based Metallic Foams
Bibliographic record
Abstract
Nickel-based metallic foams are commonly used in electrochemical energy storage devices (rechargeable batteries) as both current collectors and active mass support. These materials attract attention as tunable electrode materials because they are available in a range of chemical compositions, pore structures, pore sizes, and densities. This contribution presents structural, chemical, and electrochemical characterization of Ni-based metallic foams. Several materials and surface science techniques (transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), focused ion beam (FIB), and X-ray photoelectron spectroscopy (XPS)) and electrochemical methods (cyclic voltammetry (CV)) are used to examine the micro-, meso-, and nanoscopic structural characteristics, surface morphology, and surface-chemical composition of these materials. XPS combined with Ar-ion etching is employed to analyze the surface and near-surface chemical composition of the foams. The specific and electrochemically active surface areas (As, Aecsa) are determined using CV. Though the foams exhibit structural robustness typical of bulk materials, they have large As, in the range of 200-600 cm(2) g(-1). In addition, they are dual-porosity materials and possess both macro- and mesopores.
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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 | 0.001 | 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".