Nickel hydroxides and related materials: a review of their structures, synthesis and properties
Bibliographic record
Abstract
This review article summarizes the last few decades of research on nickel hydroxide, an important material in physics and chemistry, that has many applications in engineering including, significantly, batteries. First, the structures of the two known polymorphs, denoted as α -Ni(OH) 2 and β -Ni(OH) 2 , are described. The various types of disorder, which are frequently present in nickel hydroxide materials, are discussed including hydration, stacking fault disorder, mechanical stresses and the incorporation of ionic impurities. Several related materials are discussed, including intercalated α -derivatives and basic nickel salts. Next, a number of methods to prepare, or synthesize, nickel hydroxides are summarized, including chemical precipitation, electrochemical precipitation, sol–gel synthesis, chemical ageing, hydrothermal and solvothermal synthesis, electrochemical oxidation, microwave-assisted synthesis, and sonochemical methods. Finally, the known physical properties of the nickel hydroxides are reviewed, including their magnetic, vibrational, optical, electrical and mechanical properties. The last section in this paper is intended to serve as a summary of both the potentially useful properties of these materials and the methods for the identification and characterization of ‘unknown’ nickel hydroxide-based samples.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".