Evidence map›Paper›PMID 41773331›Full record

ArticleACS applied materials & interfaces2026

Block Copolymer-Enabled Low-Temperature Structural Battery Electrolytes Produced Using Polymerization-Induced Phase Separation.

Sayyam Deshpande, Chen Wang, Coby Scrudder, Ramu Banavath, Jodie L Lutkenhaus, Micah J Green

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Sayyam DeshpandeArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.
Chen WangArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.
Coby ScrudderArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.
Ramu BanavathArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.
Jodie L LutkenhausArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.ORCID 0000-0002-2613-6016
Micah J GreenArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.ORCID 0000-0001-5691-0861

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural battery electrolytes (SBEs) require both high ionic conductivity and high mechanical strength and stiffness. However, SBEs produced using the one-pot polymerization-induced phase separation (PIPS) synthesis method suffer from high tortuosity which decreases the effective ionic conductivity. Additionally, conventional liquid electrolytes demonstrate poor performance in low temperatures and are unsuitable for applications in cold climates. Here, we report SBEs generated using PIPS in the presence of an amphiphilic block copolymer (BCP), which modifies the solid-liquid interface that forms during polymerization, resulting in lower tortuosity and improved ionic conductivity. Using a low-temperature liquid electrolyte, the effect of BCP and resin content on the ionic conductivity and mechanical properties is examined. Only 1 wt % of BCP additive is needed to improve the ionic conductivities even at low temperature (2.34 × 10

Indexed as

block copolymerlithium-ion batterieslow temperaturelow tortuosityorganic radical polymerspolymerization induced phase separation (PIPS)structural battery electrolyte

Identifiers

PMID41773331
PMCPMC13006949

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Registered trials

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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.