Development of Ferromagnetic Materials Containing Co2P, Fe2P Phases from Organometallic Dendrimers Precursors
Bibliographic record
Abstract
The development of synthesis methods to access advanced materials, such as magnetic materials that combine multimetallic phosphide phases, remains a worthy research challenge. The most widely used strategies for the synthesis of magnetic transition metal phosphides (TMPs) are organometallic approaches. In this study, Fe-containing homometallic dendrimers and Fe/Co-containing heterometallic dendrimers were used to synthesize magnetic materials containing multimetallic phosphide phases. The crystalline nature of the nearly aggregated particles was indicated for both designed magnetic samples. In contrast to heterometallic samples, homometallic samples showed dendritic effects on their magnetic properties. Specifically, saturation magnetization (Ms) and coercivity (Hc) decrease as dendritic generation increases. Incorporating cobalt into the homometallic dendrimers to prepare the heterometallic dendrimers markedly increases the magnetic properties of the magnetic materials from 60 to 75 emu/g. Ferromagnetism in homometallic and heterometallic particles shows different responses to temperature changes. For example, heterometallic samples were less sensitive to temperature changes due to the presence of Co2P in contrast to the homometallic ones, which show an abrupt change in their slopes at a temperature close to 209 K, which appears to be related to the Fe2P ratios. This study presents dendrimers as a new type of precursor for the assembly of magnetic materials containing a mixture of iron- and cobalt-phosphides phases with tunable magnetism, and provides an opportunity to understand magnetism in such materials.
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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".