Lessons learned from dinuclear lanthanide nano-magnets
Bibliographic record
Abstract
The quest for higher density information storage has led to the investigation of Single-Molecule Magnets (SMMs) as potential molecules to be applied in materials such as hard discs. In order for this to occur, one must first design metal complexes which can retain magnetic information at temperatures where these applications become possible. This can only be achieved through answering and understanding fundamental questions regarding the observed physical properties of SMMs. While mononuclear lanthanide complexes have shown promise in obtaining high energy barriers for the reversal of the magnetisation they are limited to Single-Ion Magnet behaviour intrinsic to one metal centre with a limited number of unpaired electrons. As a way of increasing the effective anisotropic barrier, systems with higher nuclearity have been sought to increase the spin ground state of the molecule. Dinuclear complexes are presented as key compounds in studying and understanding the nature of magnetic interactions between metal ions. This tutorial review will span a number of dinuclear 4f complexes which have been critical in our understanding of the way in which lanthanide centres in a complex interact magnetically. It will examine key bridging moieties from the more common oxygen-based groups to newly discovered radical-based bridges and draw conclusions regarding the most effective superexchange pathways allowing the most efficient intracomplex interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".