Hybrid Reduced Graphene Oxide with Special Magnetoresistance for Wireless Magnetic Field Sensor
Bibliographic record
Abstract
Abstract Very few materials show large magnetoresistance (MR) under a low magnetic field at room temperature, which causes the barrier to the development of magnetic field sensors for detecting low-level electromagnetic radiation in real- time. Here, a hybrid reduced graphene oxide (rGO)-based magnetic field sensor is produced by in situ deposition of FeCo nanoparticles (NPs) on reduced graphene oxide (rGO). Special quantum magnetoresistance (MR) of the hybrid rGO is observed, which unveils that Abrikosov’s quantum model for layered materials can occur in hybrid rGO; meanwhile, the MR value can be tunable by adjusting the particle density of FeCo NPs on rGO nanosheets. Very high MR value up to 21.02 ± 5.74% at 10 kOe at room temperature is achieved, and the average increasing rate of resistance per kOe is up to 0.9282 Ω kOe−1. In this paper, we demonstrate that the hybrid rGO-based magnetic field sensor can be embedded in a wireless system for real-time detection of low-level electromagnetic radiation caused by a working mobile phone. We believe that the two-dimensional nanomaterials with controllable MR can be integrated with a wireless system for the future connected society.
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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) | 0.000 | 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".