Manganese ferrite-polyaniline hybrid materials: Electrical and magnetic properties
Bibliographic record
Abstract
Magnetic MnFe2O4 nanopowders were synthesized by an original solvothermal method in the absence and in the presence of tetra-n-butylammonium bromide (TBAB) and Tween 80 (TW) as surfactants. Manganese ferrite/polyaniline (PANI) hybrid materials were synthesized by in situ polymerization of aniline on the surface of MnFe2O4 using ammonium persulfate as oxidant. The purpose of the study was to investigate the influence of the two surfactants on the properties of the MnFe2O4 powders and of their composites with PANI. The specific surface area, the cumulative surface area of pores and the cumulative volume of pores are influenced by the nature of surfactant in case of MnFe2O4 powders and are higher by comparison to those of the MnFe2O4/PANI hybrid materials. The values of saturation magnetization in case of MnFe2O4 powders are higher than those of the hybrid materials and are not influenced by the surfactant nature. These features revealed that MnFe2O4 powders can be efficiently used as adsorbents for the purification of wastewaters. The values of the electrical conductivity of the composites exhibit a significant increase in comparison to the MnFe2O4 powders and depend on the surfactant nature. The highest value of electrical conductivity was achieved by the composite obtained using Tween 80 as surfactant (σDC = 54.5·10−5S m−1) which was close to that of PANI (σDC = 61.2·10−5 S m−1). The fact that the magnetic and electric properties of the synthesized MnFe2O4/PANI composites can be changed by design, demonstrate the high potential of these materials to be used in magneto-electric applications.
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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".