Electronic and Structural Properties of Core-Shell Amino-Silica Nanoparticles: DFT And SCC-DFTB Calculation
Bibliographic record
Abstract
Introduction: Silica nanoparticles (SNP) are extremely promising tools in nanotechnology and nano medicine. In most of applications such as capture and release of bacteriophage viruses the nano-structures of silica are coated by bio-compatible groups such as amine compounds. The presence of amino groups on the surface of the biosensors enables the installation of analyte receptors and antifouling agents such as oligo (ethylene oxide). Therefore, in this study, the electronic and structural properties of Core-Shell amino- Silica Nanoparticles are investigated.Material and Methods: In this investigation, we aim at obtaining the optimized structures and evaluate the geometries of the ground state for (SiO2) n (n=16, 20) nanoclusters. The electronic properties computed by density functional theory with GGA approximation and SCC-DFTB with hybrid Slater-Koster files are investigated and the effect of functionalization on such properties is discussed.Results: Solvolysis of studied structures is examined and it is shown that the highest occupied and lowest unoccupied molecular orbital states shift to obviously higher energy levels, which lead to more stable hydrogenated nanoclusters. The stability of nanoclusters rises by functionalization with amino and methylamine groups. Charge analysis of functionalized systems indicates the reactivity of nanoclusters. The results obtained in this paper are useful for chemical and biochemical applications of silica nanostructures.Conclusion: Results show that the length of amine hydrocarbon chain can control the electronic and magnetic properties of studied silica nanocluster (SNP) with different number of SiO2 unit. Pure ultra-small nanocluster shows the impressive spin splitting around the Fermi level, which is due to the spin splitting of outer silicon atoms. This feature of silica nanoclusters may be notable for applications in electronics.
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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 | 0.000 |
| 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 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".