Mechanochemical Activation and Synthesis of Nanomaterials for Hydrogen Storage and Conversion in Electrochemical Power Sources
Bibliographic record
Abstract
In this study we discuss a process of mechanical activation employed in place of chemical or thermal activation to improve the mobility and reactivity of hydrogen atoms and ions in nanomaterials for energy applications: rechargeable batteries and hydrogen storage for fuel cell systems. Two materials are discussed. Both are used or intended for use in power sources. One is nickel hydroxide, Ni(OH)2, which converts to oxyhydroxide in the positive Ni electrode of rechargeable metal hydride batteries. The other is a complex hydride, Mg(AIH4)2, intended for use in reversible, solid-state hydrogen storage for fuel cells. The feature shared by these unlikely materials (hydroxide and hydride) is a sheet-like hexagonal crystal structure. The mechanical activation was conducted in high-energy ball mills. We discuss and demonstrate that the mechanical excitation of atoms and ions imparted on these powders stems from the same class of phenomena. These are (i) proliferation of structural defects, in particular stacking faults in a sheet-like structure of hexagonal crystals, and (ii) possible fragmentation of a faulted structure into a mosaic of layered nanocrystals. The hydrogen atoms bonded in such nanocrystals may be inserted and abstracted more easily from OH- hydroxyl group in Ni(OH)2 and AlH4- hydride complex in Mg(AlH4)2 during hydrogen charge and discharge reactions. However, the effects of mechanical excitation imparted on these powders are different. While the Ni(OH)2 powder is greatly activated for cycling in batteries, the Mg(AlH4)2 complex hydride phase is greatly destabilized for use in reversible hydrogen storage. Such a "synchronic" view of the structure-property relationship in respect to materials involved in hydrogen energy storage and conversion is supported in experiments employing X-ray diffraction (XRD), differential scanning calorimetry (DSC) and direct imaging of the structure with a high-resolution transmission-electron microscope (HREM), as well as in property characterization.
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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 | 0.001 | 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 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".