Size- and Shape-Controlled Synthesis of Monodisperse Metal Oxide and Mixed Oxide Nanocrystals
Bibliographic record
Abstract
A nanocrystal or nanoparticle (not fully crystalline) is defined as a particle with size in range of 1 to 100 nm (102 to 107 atoms) from zero (0D) to three dimensions (3D), which exhibits the unique physiochemical properties due to the quantum size effect that cannot be anticipated from bulk counterparts. Strictly speaking, the name of “nanocrystal” is only used for crystalline nanoparticle, and is however a more general term which can refer to both crystalline and non-crystalline nanoparticles. Accordingly, their particle size is intermediate between the size of molecule and bulk solid (Rao, Muller and Cheetham 2005, Sorensen 2009). Nanocrystals can be formed in a variety of shapes including dot, sphere, cube, rod, triangle, hexagon and many others. In this size range, they possess an immense surface area per unit volume, a very large percentage of atoms in the surface. As a result, their unexpected properties can be obtained as compared to those of both individual atoms/molecules and bulk counterpart of the same chemical composition. Sizeand shape-dependent properties of the nanocrystals can be tuned by changing the dimension and designing the shape (Rao et al. 2005). Due to the materials at the nanoscale, low coordination number, surfaced edge and corner atoms are usually chemically reactive, catalytically active and polarisable surface, contributing to their high chemical potential. For example, the high surface area is of particular importance regarding heterogeneous catalytic reactions, because of the increase of interaction of reactive molecules and active sites on the catalyst surface (Abbet and Heiz 2005). Furthermore, the particle size not only affects their surface area, but also arise new properties, due to the quantum-size effect (e.g., electron confinement and surface effect) (Kroes et al. 2002, Kamat et al. 2010). Considerable efforts have recently been devoted to the preparation of metal oxide and mixed oxide nanomaterials due to both their unique properties and their technological applications (Seshadri 2005, Burda et al. 2005, Mao et al. 2007, Yin and Alivisatos 2005). Metal oxides including the transition metals and rare earths, display a wide variety of complex structures and interesting electronic and magnetic properties associated with the changes in electronic structure and bonding and in the presence of ordered defect complexes or extended defects. The nanostructured mixed oxides can greatly generate new synergetic properties and improve the overall application performance, that is not available from single metal oxide species, due to the appropriate combination of individual oxide
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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) | 0.000 | 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 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".