Solution Based Methods for Fast Determination of Lithium Insertion/Extraction Kinetics
Bibliographic record
Abstract
Arguably, the electric and hybrid electric vehicles are the new frontiers in lithium batteries. As a new market it is distinctly different from mass produced microelectronics, not only in the energy per battery unit but also in the power performance requirements. Especially the required charging rates during deceleration and emergency charging, as well as, the discharge rates during startup from standstill are exciting challenges.[1,2] Consequently new chemistries and new materials morphologies like nanowires, shape controlled nanoparticle and size/shape controlled meso-scale agglomerates are being developed at an impressive rate. [3,4] The standard methodology to characterize the rate performance of these new materials is to fabricate composite electrodes that combine the electroactive materials with a conductive matrix and polymer binder.[5] This porous electrode structure is filled with electrolyte and thus electron and electrolyte transfer paths to the active material is established (Figure:Left). This brings about an important question inherent to the composite electrode, i.e. is the electrochemical performance limited by the active material or by the transport of electrons and/or ions in the electrolyte filled electrode structure? In response to this type of question we developed a series of techniques that allow the study of the redox process kinetics without the use of composite electrodes.[6-8] The key to our methodology is that the electrons are delivered and retrieved not by a solid-solid contact to the active material, but via a solution based redox couple (Figure:Right). As such our techniques are similar to the common potential step electrochemical approach while providing two unique advantages: A) the complete surface of the active particle experiences the same electrochemical potential at all times. B) the flux of lithium ions to and from the particle is unobstructed. In turn, this allows the study of materials like transition metal phosphates and silicates without the conductive coating required in the standard electrochemical analysis. Consequently, and possibly for the first time, the effect of the coating and its fabrication process on the lithium insertion/extraction kinetics can be studied. Another advantage of electron transport via redox species is that nanoparticles can be studied without concerns that they due to their small nature have not been properly incorporated in the electrode matrix. In this presentation we will display the analytical techniques that we have developed over the electron transport via solution based redox species theme. This includes in situ and ex situ detection of the reaction progress, as well as, several different redox systems appropriate for different positive electrode materials. Importantly, we will show that sub-second time resolution is possible using a photometric technique and that this approach yields considerably faster results, compared to the procedure required to produce test batteries. Further the reaction uniformity throughout the sample provides new kinetic insight, which can help to distinguish between possible reaction mechanisms, thus providing better fundamental understanding of both new and existing redox chemistries. References. [1] Baisden, A.C., Emadi, A. (2004) IEEE Trans Veh Tech, 53, 199. [2] Zaghib, K., et al. (2013) Materials, 6, 1028. [3] Bi, Z., et al. (2013) RSC Adv, 3, 19744. [4] Bresser, D., et al. (2012) J Power Sources, 219, 217. [5] Park, M., et al. (2010) J Power Sources, 195, 7904. [6] Trinh, N.D., et al. (2012) J Power Sources, 200, 92. [7] Kuss, C., et al. (2013) Chem Sci, 4, 4223. [8] Lepage, D., et al. (2014) J Power Sources, DOI: 10.1016/j.jpowsour.2013.12.054. Figure: Left: Standard battery. Right: Electron transport via solution based redox species. Arrows indicate transport (white: Electron; gray: Lithium) to the active material particle in the center of the image.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 teacher head, 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".