Part I: NiMoO4 Nanostructures Synthesized by the Solution Combustion Method: A Parametric Study on the Influence of Synthesis Parameters on the Materials’ Physicochemical, Structural, and Morphological Properties
Bibliographic record
Abstract
The impact of process conditions on the synthesis of NiMoO4 nanostructures using a solution combustion synthesis (SCS) method, in which agar powder and Ni(NO3)2 were utilized as fuel and as the oxidant, respectively, was thoroughly studied. The results show that the calcination temperature had a significant implication on the specific surface area, phase composition, particle size, band gap, and crystallite size. The influence of calcination time on the resulting physicochemical/structural/morphological properties of NiMoO4 nanostructures was found to be a major effect during the first 20 min, beyond which these properties varied to a lesser extent. The increase in the Ni/Mo atomic ratio in the oxide impacted the combustion dynamics of the system, which led to the formation of higher surface area materials, with the prevalence of the β-phase in Ni-rich samples. Likewise, the change in the pH of the precursor solution showed that the combustion reaction is more intense in the high-pH region, entailing major implications on the physicochemical properties and phase composition of the samples. The change in the fuel content showed that the presence of agar is important, as it endows the sample with a fluffy, porous texture and is also vital for the preponderance of the β-phase.
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 | 0.000 |
| 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) | 0.002 | 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".