Selenium and tellurium nanomaterials
Bibliographic record
Abstract
Abstract Over the last 40 years, the rapid and exponential growth of nanotechnology led to the development of various synthesis methodologies to generate nanomaterials different in size, shape and composition to be applied in various fields. In particular, nanostructures composed of Selenium (Se) or Tellurium (Te) have attracted increasing interest, due to their intermediate nature between metallic and non-metallic elements, being defined as metalloids. Indeed, this key shared feature of Se and Te allows us the use of their compounds in a variety of applications fields, such as for manufacturing photocells, photographic exposure meters, piezoelectric devices, and thermoelectric materials, to name a few. Considering also that the chemical-physical properties of elements result to be much more emphasized when they are assembled at the nanoscale range, huge efforts have been made to develop highly effective synthesis methods to generate Se- or Te-nanomaterials. In this context, the present book chapter will explore the most used chemical and/or physical methods exploited to generate different morphologies of metalloid-nanostructures, focusing also the attention on the major advantages, drawbacks as well as the safety related to these synthetic procedures. Graphical Abstract : Overview of the chemical and physical methods commonly used to produce various Se- and/or Te-based nanomaterials.
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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 | 0.001 | 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 | 0.001 |
| 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 | 0.002 |
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".