Nucleation and Growth Mechanisms of Micro/Nano Structural Manganese‐Trimesic Acid Coordinations for Aqueous Zinc‐Ion Batteries
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Nucleation and growth of metal–organic frameworks (MOFs) are critical for controlling their morphology, size, and performance. Guided by the crystal nucleation and growth theory, this study systematically explored the effects of the sequential addition of ligand trimesic acid (BTC) and manganese ions (Mn 2+ ), ligand‐to‐metal ion ratio, solvent composition, and surfactants on the nucleation and growth of MnBTC. The regulatory mechanisms of the crystal morphology and internal structure were deeply revealed. Moreover, the established machine learning (ML) model can accurately predict the concentrations of ─COO − and Mn 2+ , providing important guidance for the controlled synthesis of MOFs in the future. In practical, the electrochemical performance of MnBTC with different morphologies and sizes was evaluated for aqueous zinc‐ion batteries. The reaction mechanism of MnBTC during the charge–discharge process was investigated through a series of in situ and ex situ characterizations, and MnBTC demonstrated excellent energy‐storage performance. This study opens a new window for the precise synthesis of MOFs, which show strongly controlled micro/nano structure and coordination environment based on the crystal nucleation and growth theory with the assistance of ML.
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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 | 0.000 |
| 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 it