Evidence map›Paper›PMID 36873702›Full record

ReviewJACS Au2023

Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations.

Chao Fang, Aashutosh Mistry, Venkat Srinivasan, Nitash P Balsara, Rui Wang

Open access · goldAbstract readReview
In one paragraph

Review in JACS Au, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 35 citations in OpenAlex.

  1. Lessons Learned on Obtaining Reliable Conductivity Estimates From Molecular Dynamics Simulations.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Chao FangMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.ORCID https://orcid.org/0000-0002-6207-0819
Aashutosh MistryJoint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois60439, United States.ORCID https://orcid.org/0000-0002-4359-4975
Venkat SrinivasanJoint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois60439, United States.ORCID https://orcid.org/0000-0002-1248-5952
Nitash P BalsaraMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.ORCID https://orcid.org/0000-0002-0106-5565
Rui WangMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.ORCID https://orcid.org/0000-0002-4058-9521
Argonne National Laboratory · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations and anions. The transference number, a parameter introduced over a century ago, sheds light on the relative rates of cation and anion transport. This parameter is, not surprisingly, affected by cation-cation, anion-anion, and cation-anion correlations. In addition, it is affected by correlations between the ions and neutral solvent molecules. Computer simulations have the potential to provide insights into the nature of these correlations. We review the dominant theoretical approaches used to predict the transference number from simulations by using a model univalent lithium electrolyte. In electrolytes of low concentration, one can obtain a quantitative model by assuming that the solution is made up of discrete ion-containing clusters-neutral ion pairs, negatively and positively charged triplets, neutral quadruplets, and so on. These clusters can be identified in simulations using simple algorithms, provided their lifetimes are sufficiently long. In concentrated electrolytes, more clusters are short-lived and more rigorous approaches that account for all correlations are necessary to quantify transference. Elucidating the molecular origin of the transference number in this limit remains an unmet challenge.

Identifiers

PMID36873702
PMCPMC9975840
OpenAlexW4318954421

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.