Comprehensive Structural, Surface-Chemical and Electrochemical Characterization of Nickel-Based Metallic Foams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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 it