In Situ Electrochemical Reconstruction of Terephthalate Metal–Organic Framework Nanosheets for High‐Efficiency Lithium‐Ion Storage
Bibliographic record
Abstract
Abstract The use of metal–organic frameworks (MOFs) as electrode materials in electrochemical energy storage is still limited to two options, except for a few electrochemically stable MOFs that can be directly used as electrodes. Most of the MOFs often serve as templates for preparing inorganic electrodes. This study demonstrates that terephthalate MOF nanosheet electrodes represent an alternative category for effective electrochemical Li + storage through an in situ electrochemical reconstruction mechanism. Upon the initial lithiation/de‐lithiation cycles, the original MOF nanosheet assembly transitions to a distinctive plum pudding‐like structure with massive metal oxide nanocrystals embedded in a porous lithium terephthalate matrix, which can deliver a high capacity of 1582.4 mAh g −1 at a current density of 0.1 A g −1 and maintain a reversible capacity of 502.6 mAh g −1 at 2 A g −1 after 2000 cycles. This study offers a valuable reference for designing MOF electrodes and advancing the applications of MOF materials in electrochemistry.
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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".