Ce<sup>4+</sup> as a facile and versatile surface modification reagent for templated synthesis in electrical applications
Bibliographic record
Abstract
Surface modification for templated synthesis is crucial to achieving three-dimensional (3D) architectured materials for catalysis, photonics, energy storage, etc. However, the existing facile and versatile modification methods (e.g. with dopamine and catechol) generate modification layers that are unstable in harsh environments. These methods are thus unsuitable for electrical applications. Here we report that Ce4+ can act as an effective surface modification reagent for a broad range of substrates (chitinous butterfly wings, carbon paper, nickel foam, and polyethylene terephthalate planks) with various structural features owing to its strong oxidizing ability and Lewis acid nature. The modification yields discrete CeO2 seed layers on substrate surfaces in ca. 0.25-2 h, important for the subsequent conformal growth of CeO2 nanoparticles, Ni(OH)2 nanowires, FeOOH nanosheets, and WO3 nanosheets into 3D architectured materials. The conformally synthesized FeOOH on nickel foam (NF) yields an overpotential of 241 mV at 10 mA cm-1 for an oxygen evolution reaction. This value is comparable to a typical catalyst Ni(Fe)OOH-NF for which the Ni/Fe ratio must be well-optimized. This facile and versatile strategy might have broad applications in the conformal fabrication and application of 3D architectured materials, especially when applied in electrical applications of architectured materials (e.g. Li-ion battery).
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".