Electroless deposition of Fe-Ni alloys from acidic and alkaline solutions using hypophosphite as a reducing agent
Bibliographic record
Abstract
The deposition of Fe?Ni alloys from acidic and alkaline solutions, using hypophosphite as a reducing agent, is studied in this work. The experimental results confirm the autocatalytic nature of this process. The composition of alloys is practically independent of the temperature deposition. Fe?Ni alloys produced from acidic solutions contained less than 1% Fe. The amount of Fe in Fe?Ni alloys produced from alkaline solutions was estimated to be about 15 %. The deposition of Fe?Ni alloys was significantly faster in the alkaline than in the acidic solutions, due to more pronounced hydrolysis of Fe(II) and Ni(II) ions under the alkaline conditions. The Fe?Ni alloys produced from both acidic and alkaline solutions contain phosphorus and as such have amorphous structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".