Evidence map›Paper›PMID 41768670›Full record

ArticleACS omega2026

Structure and Flow-Viscosity of Filled-Polymer-Based 3D Printing Ink: Exploration through Coarse-Grained Molecular Dynamics.

Supun S Mohottalalage, John J Karnes, Spencer C Schmidt, Todd H Weisgraber, Andrew P Saab, Amitesh Maiti

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Multifunctional Effects of Peach Palm (Foods (Basel, Switzerland) · 2026
    Article
  2. Article
  3. Article
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.

Supun S MohottalalageLawrence Livermore National Laboratory, Livermore, California 94550, United States.
John J KarnesLawrence Livermore National Laboratory, Livermore, California 94550, United States.ORCID https://orcid.org/0000-0002-2917-8406
Spencer C SchmidtLawrence Livermore National Laboratory, Livermore, California 94550, United States.
Todd H WeisgraberLawrence Livermore National Laboratory, Livermore, California 94550, United States.
Andrew P SaabLawrence Livermore National Laboratory, Livermore, California 94550, United States.
Amitesh MaitiLawrence Livermore National Laboratory, Livermore, California 94550, United States.ORCID https://orcid.org/0000-0003-0831-0700

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The addition of nanofiller particles to a polymer matrix has long been known to enhance or modify the composite's mechanical and rheological properties. However, quantitatively capturing such changes with molecular level simulations remains computationally challenging. Toward that goal, we performed coarse-grained molecular dynamics of a nanocomposite system at a fixed (25 vol %) filler loading under nonspecific, weak polymer-filler interactions representative of a broad class of technologically important materials. We report several interesting results, including: (1) the equilibrium chain-configuration remains Gaussian-like as in an unfilled melt; (2) smaller filler particles display a stronger tendency to cluster; (3) larger fillers act as plasticizers by reducing the entanglement density and accelerating the chain mobility; and (4) fillers enhance the tensile response modulus, with the effect being stronger for larger particles. We also simulate cluster breakup, yielding, and elongational flow under an applied time-linear tensile strain and study the flow viscosity as a function of filler-size and chain-length.

Identifiers

PMID41768670
PMCPMC12947174

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.