Engineering SrTiO<sub>3</sub> Nanostructures for Enhanced Photocatalytic Performance: Unveiling the Influence of Titanium Precursors and Synthesis Temperature
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The development of advanced functional materials relies on key properties such as morphology, crystallinity, and electronic structure. In this work, we present the hydrothermal synthesis of SrTiO 3 nanoparticles using amorphous titanium as a precursor and systematically investigate the influence of synthesis temperature (from 20 to 200 °C) on their structural, morphological, and chemical characteristics. Electron microscopy revealed a temperature-driven morphological transition from nanocube-like to spherical-like structures. X-ray diffraction analyses demonstrated improved crystallinity with increasing temperature, although local imperfections persisted, contributing to structural disorder. UV–vis spectroscopy showed a slight variation in the optical band gap, ranging from 3.36 to 3.28 eV across the samples. Notably, the sample synthesized at 60 °C exhibited significantly enhanced photocatalytic activity for H 2 production, reaching approximately 43 μmol h –1 . This enhancement was attributed to a synergistic interplay among the surface area, crystallinity, and composition. A dissolution–precipitation mechanism is proposed to explain the in situ formation of SrTiO 3, guided by the solubility and surface reactivity of the titanium precursor. These findings provide valuable insights into the design and optimization of SrTiO 3 -based materials for photocatalytic and related applications, where fine-tuning structural and surface properties is essential to maximize performance.
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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 | 0.001 |
| 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".