Synthesis and Characterization of Smartphone-Readable Luminescent Lanthanum Borates Doped and Co-Doped with Eu and Dy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite notable advancements in the development of borate materials, improving their luminescent efficiency remains an important focus in materials research. The synthesis of lanthanum borates (LaBO3), doped and co-doped with europium (Eu3⁺) and dysprosium (Dy3⁺), by the solid-state method, has demonstrated significant potential to address this challenge due to their unique optical properties. These materials facilitate efficient energy transfer from UV-excited host crystals to trivalent rare-earth activators, resulting in stable and high-intensity luminescence. To better understand their structural and vibrational characteristics, Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy were employed to identify functional groups and molecular vibrations in the synthesized materials. Additionally, X-ray diffraction (XRD) analysis was conducted to determine the crystalline structure and phase composition of the samples. All observed transitions of Eu3⁺ and Dy3⁺ in the excitation and emission spectra were systematically analyzed and identified, providing a comprehensive understanding of their behavior. Although smartphone cameras exhibit non-uniform spectral responses, their integration into this study highlights distinct advantages, including contactless interrogation, effective UV excitation suppression, and real-time spectral analysis. These capabilities enable practical and portable fluorescence sensing solutions for applications in healthcare, environmental monitoring, and food safety. By combining advanced photonic materials with accessible smartphone technology, this work demonstrates a novel approach for developing low-cost, scalable, and innovative sensing platforms that address modern technological demands.
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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 it