Ionic liquid reinforced cellulose nanofiber with iron oxide electrocatalyst
Bibliographic record
Abstract
Increased attention has been focused on the preparation of nanoarchitectures for applicability in energy, environmental and biological streams. However, concerns have been raised regarding environmental effects due to the harsh chemicals used in such preparations. To overcome this issue, the present study reports the use of a green solvent, 1-methyl-3-propyl-1H-imidazol-3-ium iodide or [Pmim]I, for the preparation of cellulose nanofiber-iron oxide (CNF-Fe 2 O 3 ) composite. The as-prepared nanofiber composite is characterized with the aid of analytical tools such as X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, field-emission scanning electron microscopy/energy dispersive X-ray spectroscopy (FE-SEM/EDX), and X-ray photoelectron spectroscopy (XPS). As a proof-of-concept demonstration, the resulting nanostructure is employed as an electrocatalyst and is compared with the bare CNFs, non-cellulose NFs and a standard RuO 2 catalyst. The results indicate that the CNF-Fe 2 O 3 composite achieves a standard current density (j) of 10 mA cm −2 at only 260 mV compared to 280, 290, and 370 mV for the standard RuO 2 , the bare CNFs, and non-cellulose nanofibers (NFs), respectively. These differences become more prominent at a high current density of 100 mA cm −2 , where the CNF-Fe 2 O 3 requires only 310 mV, while the CNFs, RuO 2 , and NFs consume 360, 370, and 550 mV, respectively.
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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".