Associating Mechano‐electrochemical Phenomena to Stochastic Current Events in Micro‐Electrochemical Cells Containing TiNb<sub>2</sub>O<sub>7</sub> Particles
Bibliographic record
Abstract
Abstract While searching for ultra‐safe high‐power anodes for Li‐ion batteries, TiNb2O7 (TNO) emerged as a promising material for higher energy density compared to the current state‐of‐the‐art Li4Ti5O12 (LTO). Here, the electrochemistry of isolated carbon coated particles of both anode materials were for the first time studied using scanning electrochemical cell microscopy (SECCM). Interestingly, stochastic current event observations were made possible because of the small electrochemical cell created by SECCM and were designated as potential‐driven stochastic events (PDSEs). The PDSEs were especially prominent in TNO compared to LTO. Metrics for judging the frequency and intensity of PDSEs were developed to compare the impact of different C‐rates, particle masses and sizes of SECCM landings. The frequency and/or intensity of PDSEs increases with higher C‐rates and larger overpotentials. Possible theories for the PDSEs were explored, including droplet electrowetting/spreading, gas evolution, (de)lithiation mechanisms, mass transfer limitations and inter‐/intra‐ particle cracking. We speculate that the propensity of TNO to undergo PDSEs as compared to LTO is mainly related to the fact that TNO is known to crack extensively during cycling.
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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".