Guanine-based spin valve with spin rectification effect for an artificial memory element
Bibliographic record
Abstract
Non-volatile electronic memory elements are very attractive for applications, not only for information storage but also in logic circuits, sensing devices and neuromorphic computing. Here, a ferroelectric film of guanine nucleobase is used in a resistive memory junction sandwiched between two different ferromagnetic films of Co and CoCr alloys. The magnetic films have an in-plane easy axis of magnetization and different coercive fields whereas the guanine film ensures a very long spin transport length, at 100 K. The non-volatile resistance states of the multiferroic spintronic junction with two-terminals are manipulated by a combined action of small external magnetic and electric fields. Thus, the magnetic field controls the relative orientation of the magnetization of the metallic ferromagnetic electrodes, that leads to different magnetoresistance states. The orientation and the magnitude of the electric field controls the orientation of the polarization of the guanine ferroelectric barrier, that leads to different electroresistance states, respectively. Moreover, we have observed a strong interfacial coupling of the two parameters. Consequently, positive and negative magnetoresistance hysteresis loops corresponding to spin rectification effects and non-hysteretic (erased) resistive states are manipulated with the electric field by switching the orientation of the electrical polarization of the organic ferroelectric.
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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".