MétaCan
Menu
Back to cohort
Record W4391662456 · doi:10.1149/ma2023-022189mtgabs

AMIDR: A Complete Pulse Method for Measuring Cathode Solid-State Diffusivity

2023· article· en· W4391662456 on OpenAlexaff
Mitchell Ball, Marc M. E. Cormier, Eniko Zsoldos, Nutthaphon Phattharasupakun, Michel B. Johnson, Michael Metzger, Chongyin Yang, J. R. Dahn, Ines Hamam, Ning Zhang

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsThermal diffusivitySolid-statePulse (music)CathodeMaterials scienceNuclear engineeringThermodynamicsElectrical engineeringPhysicsEngineering physicsEngineering

Abstract

fetched live from OpenAlex

Lithium transport within cathode material is driven by diffusion. While fast cathode diffusion is desired, solid-state diffusivity is one of the most difficult cell material properties to measure. However, it is an important property to understand as cathode capacity is often limited by poor diffusivity. GITT (Galvanostatic Intermittent Titration Technique) is a common diffusivity measurement method. This technique is accomplished by applying a series of constant current pulses to a cell and analyzing the voltage response. While easy to use, this method only measures near the surface of the active material by assuming that it takes the geometry of a semi-infinite plane. This approximation means that GITT is only accurate when measuring the very initial voltage response. However, the voltage response cannot be analyzed too early as this may amplify error produced by CDL (double layer capacitance) and RI (interface resistance) found at the cathode-electrolyte interface which GITT does not account for. EIS (Electrochemical Impedance Spectroscopy), an AC method, is better suited for distinguishing CDL/RI from diffusivity but also struggles to evaluate diffusivity beyond the surface of active material within a reasonable time frame1. In addition, instruments capable of quality EIS are often cost prohibitive compared to DC cell testers. The voltage responses after the first few moments of a pulse are relevant when evaluating cell behaviour in real-world applications and should be considered to produce comprehensive measurements of diffusivity and its performance impact. AMID (Atlung Method for Intercalant Diffusion) is a multi-rate pulse method produced by this lab which uses a series of pulses, both short/fast and long/slow, and an approximation-free analytical model (developed by Sven Atlung) for diffusion in a spherical particle, not a semi-infinite plane2. However, AMID cannot differentiate diffusion impedance from resistance and therefore requires that resistance be considered negligible. In addition, while AMID mathematics are free of approximations, the typical multi-rate pulse protocol does require that series of pulses be approximated as singular pulses3. Lastly, the multi-rate pulse protocol limits the state-of-charge resolution of diffusivity measurements. AMIDR (Atlung Method for Intercalant Diffusion and Resistance) incorporates a GITT-style single pulse protocol with AMID-style mathematics modified to account for resistance as shown in Figure 1b). AMIDR collects pulse data with a pseudo-logarithmic time distribution. This means that the same number of data points is collected in 0.1-1.0 seconds, 1.0-10 seconds, 10-100 seconds and so on. Unlike GITT which measures diffusivity at an early arbitrary point in a pulse, AMIDR considers the entire length of a pulse comprehensively. Fractional capacity is calculated for each datapoint by dividing the actual capacity with the ideal “impedance-free” capacity expected at that voltage as shown in Fig 1c). These fractional capacity values, normalized from 0-1, are fitted to the modified Atlung model. This modified Atlung model is nearly the same as the original Atlung model except an additional term is added for capacity limited by resistance. Unlike AMID, AMIDR measures both diffusivity and resistance simultaneously by using both as fitting parameters. Lastly, while CDL is usually small enough for RI to behave simply like ohmic resistance on relatively short time scales (>0.1 seconds), AMIDR has the option to account for CDL/RI when relevant such as near the bottom of a complete discharge. AMIDR is a new diffusivity measurement method tailored for cathode material in Li-ion batteries. By combining the advantages of various previous diffusivity methods, AMIDR eliminates many of their concerns and sources of error such as sampling of only the material surface, alternate confounding sources of impedance, and expensive equipment as seen in Figure 1a). When paired with proper cell design (i.e. ultra-low single layer cathode loading to remove pore impedance and reference electrodes to remove anode impedance), AMIDR can produce high resolution diffusivity measurements with improved accuracy and applicability to real-world cell performance. References C. Deng and W. Lu, Journal of Power Sources, 473, 228613 (2020). A. Liu et al., J. Electrochem. Soc., 168, 070503 (2021). M. Doyle, J. Newman, and J. Reimers, Journal of Power Sources, 52, 211–216 (1994). Figure 1. (a) Comparison of various Diffusivity measurements. (b) The modified Atlung model for diffusion in a spherical particle. (c) How pulse data is converted to the unitless Atlung model. Figure 1

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.004

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.

Opus teacher head0.051
GPT teacher head0.308
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvancements in Battery MaterialsFrench-language works237,207