Parametric study on the stabilization of metal oxide nanoparticles in organic solvents: A case study with indium tin oxide (ITO) and heptane
Bibliographic record
Abstract
The tendency of nanoparticles (NPs) to form large aggregates has been a major limitation to their widespread applications where utilizing monodisperse and stable suspension of NPs is essential. The aggregation of NPs becomes more challenging when there is less affinity between the dispersed phase (NPs) and the continuous phase (solvent), such as, dispersion of hydrophilic metal oxide NPs into a nonpolar (organic) solvent. The objective of this study is to systematically investigate the synergistic effects of eight dispersion parameters on the size and stability of indium tin oxide (ITO) NPs in heptane. The matrix of experimentation was designed using an L18 Taguchi method. The analysis of variance (ANOVA) of the experimental results revealed that the most significant factors on the size and stability of NPs were the mass of ITO NPs and the volume of the dispersing agent. Taguchi signal-to-noise (SN) ratio analysis was used to determine the optimal factor levels for the preparation of well-dispersed and stable NP suspensions. Confirmation tests were carried out at the suggested levels of the ANOVA predictive model, and highly stable ITO NPs in heptane with the size distribution of 43.0–68.3 nm were obtained. The results of the present parametric study can be used for a broad range of applications where effective stabilization of metal oxide NPs in organic solvents is desired.
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.
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 | 0.001 |
| 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".