Vector Magnetometer Based On Localized Scattering Between Optical Fiber Spectral Combs and Magnetic Nanoparticles
Bibliographic record
Abstract
The apparent increase in magnetic field measurement has led to a growing demand for new sensing technologies. However, a long-lasting challenge unaddressed is how to achieve ultrahigh sensitive magnetic field measurement in both amplitude and orientation. Here, we demonstrate a compact-in-size and simple-to-implement fiber-optic magnetometer for highly sensitive vector magnetic field measurement. The sensor is based on magnetic nanoparticles coated tilted fiber Bragg grating inscribed in a commercial single mode fiber. The sensing mechanism stems from the backward coupled cladding mode resonances with narrow bandwidth (Q factor > 10<sup>4</sup>), which is highly sensitive to slight surface refractive index perturbation. The switching of the magnetic field direction on the surface of fiber induces redistribution of coated magnetic nanoparticles with spatial anisotropy density and scattering by formation of magnetic chains hereafter the effective index is changed, which in turn modulates the cladding mode resonance with wavelength shift and amplitude attenuation. Through calculating the peak-to-peak intensity of target cladding modes of TFBG, the external magnetic field was precisely measured with an intensity sensitivity of 0.39 dB/mT and a direction sensitivity of 0.038 dB/rad. The inherent core mode can be used as a reference to get rid of temperature crosstalk and light source disturbance. The proposed sensor is compact in size and easy to be used in hard-to-reach place, showing good potentials for industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".