Synthesis and Characterization of Smartphone-Readable Luminescent Lanthanum Borates Doped and Co-Doped with Eu and Dy
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".