Solution combustion synthesis: the relevant metrics for producing advanced and nanostructured photocatalysts
Bibliographic record
Abstract
green and sustainable paths to accomplish chemical circularity. The design and preparation methods of photocatalysts play a prime role in determining the structural, surface characteristics and optoelectronic properties of the final products. The solution combustion synthesis (SCS) technique is a relatively novel, cost-effective, and efficient method for the bulk production of nanostructured materials. SCS-fabricated metal oxides are of great technological importance in photocatalytic, environmental and energy applications. To date, the SCS route has been employed to produce a large variety of solid materials such as metals, sulfides, carbides, nitrides and single or complex metal oxides. This review intends to provide a holistic perspective of the different steps involved in the chemistry of SCS of advanced photocatalysts, and pursues several SCS metrics that influence their photocatalytic performances to establish a feasible approach to design advanced photocatalysts. The study highlights the fundamentals of SCS and the importance of various combustion parameters in the characteristics of the fabricated photocatalysts. Consequently, this work deals with the design of a concise framework to link the fine adjustment of SCS parameters for the development of efficient metal oxide photocatalysts for energy and environmental applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".