Dynamic Phase Transformations of Prussian Blue Analogue Crystals in Hydrotherms
Bibliographic record
Abstract
Prussian blue analogue (PBA)/metal-organic frameworks (MOFs) are multifunctional precursors for the synthesis of metal/metal compounds, carbon, and their derived composites (P/MDCs) in chemical, medical, energy, and other applications. P/MDCs combine the advantages of both the high specific surface area of PBA/MOF and the electronic conductivity of metal compound/carbon. Although the calcination under different atmospheres has been extensively studied, the transformation mechanism of PBA/MOF under hydrothermal conditions remains unclear. The qualitative preparation of P/MDCs in hydrothermal conditions remains a challenge. Here, we select PBA to construct a machine-learning model and measure its hydrothermal phase diagram. The architecture-activity relationship of substances among nine parameters was analyzed for the hydrothermal phase transformation of PBA. Excitingly, we established a universal qualitative model to accurately fabricate 31 PBA derivates. Additionally, we performed three-dimensional reconstructed transmission electron microscopy, X-ray absorption fine structure spectroscopy, ultraviolet photoelectron spectroscopy, in situ X-ray powder diffraction, and theoretical calculation to analyze the advantages of hydrothermal derivatives in the oxygen evolution reaction and clarify their reaction mechanisms. We uncover the unified principles of the hydrothermal phase transformation of PBA, and we expect to guide the design for a wide range of composites.
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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) | 0.001 | 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".