Understanding the Self-Discharge Redox Shuttle Mechanism of Dimethyl Terephthalate in Lithium-Ion Batteries
Bibliographic record
Abstract
Dimethyl terephthalate (DMT) is a redox shuttle molecule that leads to unwanted self-discharge of lithium-ion cells. It can be created in situ as a breakdown product of polyethylene terephthalate (PET), which is a surprisingly common polymer for the adhesive tapes found in commercial cells. This study investigates the shuttling mechanism and electrochemical stability of DMT, as well as its impact on the performance of LFP/graphite pouch cells with LiFSI and LiPF6 conducting salts. Cyclic voltammetry shows that DMT has a redox potential of 1.5 V vs Li+/Li and is redox active in the full voltage range of LFP/graphite cells. Ultra-high precision coulometry and open-circuit storage experiments show that DMT lowers the coulombic efficiency, increases the charge endpoint capacity slippage, and dramatically accelerates the reversible self-discharge of LFP/graphite pouch cells. Gas chromatography-mass spectrometry shows that DMT is stable over weeks in cells with LiPF6, but only for several days in cells with LiFSI. A well-insulating solid-electrolyte interphase layer derived from vinylene carbonate can prevent DMT from shuttling. However, VC can be consumed, and passivation layers can deteriorate in aged cells, so the best way to prevent DMT-induced self-discharge of lithium-ion batteries is to eliminate PET components.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".