Regulating Electron Transfer in Vanadium‐Based Metal–Organic Frameworks via the Synergy of Linker Engineering and Machine Learning for Efficient and Reversible Aqueous Zinc Ion Batteries
Bibliographic record
Abstract
Abstract Precise regulation of ligands in metal–organic frameworks (MOFs) to modulate the local electronic structure and charge distribution has become an effective strategy for optimizing their electrochemical performance. However, utilizing ligand‐functionalized MOFs to activate their potential in aqueous zinc‐ion batteries remains a challenge. Herein, eight ligand‐functionalized X‐MIL‐47 (X represents the functional groups) samples are prepared using a one‐pot solvothermal method. The polar substituents on the ligand regulated the electronic structure of the MOFs through inductive and conjugative effects, altering the electron density of the metal center and thereby facilitating the optimization of the Zn 2+ insertion/extraction kinetics. The coordination environment of X‐MIL‐47 is analyzed using X‐ray absorption fine structure spectroscopy, and the Zn 2+ storage mechanism is thoroughly investigated through both in situ/ex situ spectroscopic techniques. The experimental results are consistent with DFT calculations, indicating that the introduction of polar substituents induces charge redistribution within the MOFs, thereby enhancing the reversibility of the redox reaction. Furthermore, a machine learning model based on the orthogonal expansion method and experimental data is developed to predict electrode material performance under varying conditions. This study provides new insights into the design of functional MOFs for energy storage applications.
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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) | 0.000 | 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 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".