MétaCan
Menu
Back to cohort
Record W3114403993 · doi:10.1149/ma2020-024826mtgabs

Understanding Anode Capacity Fade in Symmetric Cells

2020· article· en· W3114403993 on OpenAlexaff
Zilai Yan, Yijia Liu, Timothy Hatchard, Ben Scott, Yidan Cao, Simeng Cao, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAnodeFaraday efficiencyMaterials scienceElectrodeCapacity lossElectrolyteGraphiteFadeAlloyDegradation (telecommunications)Chemical engineeringAnalytical Chemistry (journal)Composite materialChemistryTelecommunicationsComputer scienceOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Symmetric cells have been developed to evaluate electrode degradation directly associated with solid electrolyte interphase (SEI) growth. This limits their applications to some certain anodes, such as graphite and Li4Ti5O12.1 In this work, symmetric cells have been applied to understand the degradation of high energy density anodes, such as Si-alloys, which undergoes multiple degradation mechanisms, including mechanical failure, SEI growth, and excess capacity. The excess capacity is related to the reversible capacity changes, caused by the electrode upper endpoint potential shift. A Li inventory model is developed to interpret anode capacity fade in symmetric cells. The Li inventory model attempts to establish a mathematical relationship between multiple anode degradation processes and measured experimental data, such as cycling charge/discharge capacity. This model allows for the measurement of coulombic efficiencies of individual components of blended electrodes (e.g. alloy + graphite). Figure 1 illustrates the main reactions and side reactions during symmetric cell discharge at ith cycle. Except the main reactions in working electrodes about lithiation and delithiation, the side reactions, as mentioned above, have been included. In this work, a Si-alloy, Si80W20,2 was used as an example. One major benefit of symmetric cells is to measure the coulombic efficiency (CE) of electrodes with standard chargers.3 After deconvolution, the total irreversible capacity (IC) per cycle due to surface reactions on Si80W20 can be obtained from graphite and Si-alloy/graphite blended electrodes. The temperature effects on IC can also be obtained, as summarized in Figure 2. Reference J. C. Burns et al., J. Electrochem. Soc., 158, A1417–A1422 (2011). Y. Liu, B. Scott, and M. N. Obrovac, J. Electrochem. Soc. , 166, A1170–A1175 (2019) 10.1149/2.0851906jes. Z. Yan and M. N. Obrovac, J. Electrochem. Soc., 164, A2977–A2986 (2017). Figure 1 (a) An example of potential curves of electrode A, electrode B, and the symmetric cell during symmetric cell discharge. Illustrations of main and side reactions occurred at electrode A and electrode B during symmetric cell discharge at i th cycle are illustrated in (b) and (c), respectively. The capacity for each reaction during a single symmetric cell discharge/charge process are labelled in the following brackets. Li-Eld, Eld, and Elyt stand for lithiated electrode, delithiated electrode, and electrolyte, respectively. Figure 2 A summary of the average IC of graphite (MAG-E, 20 μm average size, Hitachi) and on Si80W20 per cycle normalized with respect to reversible capacity at different temperatures, as derived from symmetric cells with graphite and alloy/graphite blended electrodes. 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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.104
GPT teacher head0.262
Teacher spread0.158 · 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
GenreEmpirical

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207