Synthesis of Alpha Ferrous Oxalate Dihydrate from Ferrotitaniferous Mineral Sands via Hot Pressurized Aqueous Oxalic Acid: Kinetics and Characterization
Bibliographic record
Abstract
Ferrous oxalate dihydrate is a versatile organic mineral with applications across fields. However, little is known about the feasibility of its synthesis directly from iron-bearing minerals using binary subcritical water (sCW) systems and its associated kinetics. In this study, the sCW+oxalic acid system at either 115 °C or 135 °C was investigated as a reaction medium for ferrous oxalate dihydrate (α-FeC2O4∙2H2O) synthesis, starting from ferrotitaniferous sands. The kinetics of the synthesis reaction were studied, and the physicochemical characterization of the as-synthetized ferrous oxalates was performed. Overall, the sCW synthesis was temperature-dependent, following second-order reaction kinetics according to the proposed precipitation pathway. A high reaction rate constant, significantly high yields (up to 89%), and reduced reaction times (2–8 h) were evident at 135 °C. The as-synthetized product corresponded to the monoclinic α-FeC2O4∙2H2O, showed relatively high specific surface areas (from 31.9 to 33.7 m2∙g−1), and exhibited band gap energies within the visible light range (~2.77 eV). These results suggest that α-FeC2O4∙2H2O can be synthesized using an organic dicarboxylic acid and iron-rich, widely available, low-cost mineral precursors. In addition, the as-prepared α-FeC2O4∙2H2O could be further optimized and tested for catalytic and visible light photocatalytic 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 | 0.000 | 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".