Hydrothermal Synthesis of Ni<sub>3</sub>TeO<sub>6</sub> and Cu<sub>3</sub>TeO<sub>6</sub> Nanostructures for Magnetic and Photoconductivity Applications
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
High Resolution Image Download MS PowerPoint Slide Despite great attention toward transition metal tellurates especially M 3 TeO 6 (M = transition metal) in magnetoelectric applications, control on single phasic morphology-oriented growth of these tellurates at the nanoscale is still missing. Herein, a hydrothermal synthesis is performed to synthesize single-phased nanocrystals of two metal tellurates, i.e., Ni 3 TeO 6 (NTO with average particle size ∼37 nm) and Cu 3 TeO 6 (CTO ∼ 140 nm), using NaOH as an additive. This method favors the synthesis of pure NTO and CTO nanoparticles without the incorporation of Na at pH = 7 in MTO crystal structures such as Na 2 M 2 TeO 6, as it happens in conventional synthesis approaches such as solid-state reaction and/or coprecipitation. Systematic characterization techniques utilizing in-house and synchrotron-based characterization methods for the morphological, structural, electronic, magnetic, and photoconductivity properties of nanomaterials showed the absence of Na in individual particulate single-phase MTO nanocrystals. Prepared MTO nanocrystals also exhibit slightly higher antiferromagnetic interactions (e.g., T N -NTO = 57 K and T N -CTO = 68 K) compared to previously reported MTO single crystals. Interestingly, NTO and CTO show not only a semiconducting nature but also photoconductivity. The proposed design scheme opens the door to any metal tellurates for controllable synthesis toward different applications. Moreover, the photoconductivity results of MTO nanomaterials prepared serve as a preliminary proof of concept for potential application as photodetectors.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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