Design of PMMA-Cotton Composite Textile with Tunable Properties via a Physics-Aware Bidirectional Neural Network Framework.

Rohith Jayaraman Krishnamurthy, Madisyn M Szypula, Abbas S Milani

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 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

3 authors.

Rohith Jayaraman KrishnamurthySchool of Engineering, The University of British Columbia, Kelowna, BC V1V 1V7, Canada.

Madisyn M SzypulaSchool of Engineering, The University of British Columbia, Kelowna, BC V1V 1V7, Canada.

Abbas S MilaniSchool of Engineering, The University of British Columbia, Kelowna, BC V1V 1V7, Canada.ORCID 0000-0003-1173-9989

Funding

Natural Sciences and Engineering Research Council of Canada Discovery Grant

6 · The paper itself

Abstract

We present a vacuum-assisted Polymethyl methacrylate (PMMA) impregnation process for cotton textiles, coupled with a physics-aware bidirectional artificial neural network (ANN) framework, to both predict and tune the natural fiber composite response from a compliant and flexible to a stiff and strong behavior. Cotton fabric samples were impregnated with acetone-borne PMMA baths, ranging from 0 to 5 wt.% polymer concentration. After drying, the PMMA formed conformal fiber coatings and inter-fiber bridges, with optimal load transfer observed at approximately 0.5-1.0 wt.%. Mechanical properties, including the elastic modulus, tensile strength, ductility, and toughness, were measured alongside Differential Scanning Calorimetry (DSC), Glass Transition Temperature (

Indexed as

physics-aware AIphysics-informed machine learningPMMA–cotton compositesprocessing–property relationshipvacuum impregnation

Identifiers

PMID42280040

DOI10.3390/ma19112387

PMCPMC13257449

What Socratic 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 Socratic graph.