Preparation from a revisited wet chemical route of phase-pure, monocrystalline and SHG-efficient BiFeO3 nanoparticles for harmonic bio-imaging
Bibliographic record
Abstract
Abstract We present two new synthetic routes for bismuth ferrite harmonic nanoparticles (BiFeO3 HNPs). Both phase-pure and mixed phase BiFeO3 materials were produced after improvement of the solvent evaporation and sol-gel combustion routes. Metal nitrates with a series of dicarboxylic acids (tartronic, tartaric and mucic) were used to promote crystallization. We found that the longer the carbon backbone with a hydroxyl group attached to each carbon, the lower the annealing temperature. We also demonstrate that nanocrystals more readily formed at a given temperature by adding glycerol but to the detriment of phase purity, whereas addition of NaCl in excess with mucic acid promotes the formation of phase-pure, monocrystalline nanoparticles. This effect was possibly associated with a better dispersion of the primary amorphous precursors and formation of intermediate complexes. The nanoparticles have been characterized by XRD, TEM, ζ-potential, photon correlation spectroscopy, two-photon microscopy and Hyper-Rayleigh Scattering measurements. The improved crystallization leads to BiFeO3 HNPs without defect-induced luminescence and with a very high averaged second harmonic efficiency (220 pm/V), almost triple the efficiency previously reported. This development of simple, scalable synthesis routes which yield phase-pure and, crucially, monocrystalline BiFeO3 HNPs demonstrates a significant advance in engineering the properties of nanocrystals for bio-imaging and diagnostics applications.
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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".