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
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.
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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".