Effect of hydrogen on nickel extraction from low-grade ultramafic nickel sulfide concentrate
Bibliographic record
Abstract
Low-grade ultramafic nickel sulfide ores represent an important but underutilized resource for nickel production. However, their high MgO content poses significant challenges to conventional smelting processes, limiting their economic and environmental viability. The authors proposed a solid-state nickel extraction method, employing metallic iron as a nickel extractant under inert or H 2 –Ar atmospheres, to avoid high-temperature smelting and enable direct extraction of nickel as ferronickel. Although hydrogen was present in the atmosphere, the authors observed the loss of iron into iron-magnesium oxides and silicates. The aim of the current work is to understand the mechanism of iron oxidation and the effect of atmosphere on it. It was found that hydrogen helps create a low oxygen potential environment around the sample (logP O2 = −22 to −17 at 750–920 °C), but oxygen-buffering minerals in the ultramafic concentrate create local oxidizing conditions, leading to the oxidation of iron. Kinetic analysis reveals that hydrogen reduction of iron oxide in the complex compounds with MgO and sulfides is hindered by high activation energy barriers (E a = 240 kJ/mol). These findings highlight critical factors affecting the efficiency of hydrogen-based reductio in complex mineral matrices. • Hydrogen reduction enables direct ferronickel production from ultramafic ores. • Iron oxidation mechanism is studied under inert and H 2 –Ar atmosphere. • Oxygen-buffering minerals create local oxidizing conditions. • High activation energy limits H 2 reduction of iron oxide.
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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 | 0.001 |
| 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) | 0.002 | 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".