NMR Determination of the Relative Binding Affinity of Crown Ethers for Manganese Cations in Aprotic Nonaqueous Lithium Electrolyte Solutions
Bibliographic record
Abstract
Polymeric chelating agents placed in the interelectrode space of a Li-ion battery (LIB) have been suggested as a means of sequestering Mn cations dissolved from positive electrodes of LIBs to prevent their migration to, and deposition onto, negative electrodes and thus mitigate the associated degradation of LIB performance and life. In order to select the most effective chelating agent and optimize its polymeric form, it is desirable to determine the binding affinity of various chelating agents for manganese cations. The present study evaluates the relative binding affinity of crown ethers for manganese cations in a lithium-containing environment through the detection of the 7 Li nucleus chemical shift. Results are presented for the relative binding affinities of 15-crown-5 and 1-aza-15-crown-5 ethers for Mn 2+ and Mn 3+ . Significant differences in relative binding affinity were discovered with particular crown ether–manganese oxidation state combinations. Additionally, a substantial decrease in binding affinity was observed for the polymeric crown ether relative to its molecular form. These results indicate that the NMR titration technique is a useful screening tool, which will inform and assist the development of more effective manganese cations trapping materials. Quantification of the Mn trapping efficiency will permit the screening of trapping groups at the molecular level (before attachment to a polymer), as well optimization of their polymeric forms (type of backbone, morphology, length of linker, amount of cross-linking, etc.). The methodology developed in the present work will therefore accelerate the development of the Mn trapping technology.
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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".