A Split-Plot Experimentation Strategy for Making Causal Inferences in Advanced Materials: Auxetic Polyurethane Foam Manufacturing and Processing Analysis.

Matthew S Wadsworth, Md Jahan Deloyer, Omer Arda Vanli, Changchun Zeng

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2024. 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

4 authors.

Matthew S WadsworthDepartment of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, High Performance Materials Institute, Florida State University, 2525 Pottsdamer St., Tallahassee, FL 32310, USA.

Md Jahan DeloyerDepartment of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, High Performance Materials Institute, Florida State University, 2525 Pottsdamer St., Tallahassee, FL 32310, USA.

Omer Arda VanliDepartment of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, High Performance Materials Institute, Florida State University, 2525 Pottsdamer St., Tallahassee, FL 32310, USA.

Changchun ZengDepartment of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, High Performance Materials Institute, Florida State University, 2525 Pottsdamer St., Tallahassee, FL 32310, USA.ORCID 0000-0003-0855-3497

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Development of advanced materials is often time consuming and expensive because of the large number of variables involved and experiments needed. An effective experimentation strategy would accelerate development by reducing the required amount of experiments without sacrificing the obtainable information. In this paper, the development of auxetic polyurethane (PU) foams was discussed as a case study. Auxetic materials are materials with a negative Poisson's ratio and have potential in many structural and functional applications. Auxetic PU foams are the most studied auxetic materials, and their manufacturing and properties are affected by many processing and environmental factors. This paper introduces a sophisticated design of experimental methodology to help reduce the experimental effort while effectively screening these factors. This methodology is then applied in an experiment to illustrate its utility and distinct advantages that greatly facilitate material development.

Indexed as

advanced materialsauxetic foamdesign of experimentssplit-plot design

Identifiers

PMID38998362

DOI10.3390/ma17133280

PMCPMC11243020

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.