DoE-It-Yourself (DoEIY): An Open-Access Web Application for Democratizing Experimental Design in Chemical and Materials Research.

Niamh Mac Fhionnlaoich, Ye Yang, Runzhang Qi, Federico Galvanin, Stefan Guldin

Abstract read

In one paragraph

Article in Small methods, 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

5 authors.

Niamh Mac FhionnlaoichUniversity College London, Department of Chemical Engineering, London, UK.ORCID https://orcid.org/0000-0001-8459-9994

Ye YangUniversity College London, Department of Chemical Engineering, London, UK.ORCID https://orcid.org/0000-0001-9344-9349

Runzhang QiUniversity College London, Department of Chemical Engineering, London, UK.ORCID https://orcid.org/0000-0002-7805-6501

Federico GalvaninUniversity College London, Department of Chemical Engineering, London, UK.ORCID https://orcid.org/0000-0002-3296-1581

Stefan GuldinUniversity College London, Department of Chemical Engineering, London, UK.ORCID https://orcid.org/0000-0002-4413-5527

Funding

Engineering and Physical Sciences Research Council EP/ M507970/1Engineering and Physical Sciences Research Council Proteins4Singapore

6 · The paper itself

Abstract

Achieving predictable and reproducible outcomes is a central challenge across synthetic workflows. Design of Experiments (DoE) offers a structured, multivariate framework for exploring complex parameter spaces, yet its wider adoption has been limited by statistical complexity, licensing costs, and steep learning curves. To address these barriers, we introduce DoEIY.app, an open-access web application that streamlines the experimental design process. The software supports guided design generation, data entry and analysis, and interactive model exploration through an intuitive interface. We illustrate its use in two case studies on gold nanoparticle synthesis, focusing on minimizing size dispersity and controlling mean particle diameter. These examples demonstrate how DoEIY.app enables efficient, reproducible process optimization and highlight its potential to democratize DoE implementation across a broad range of scientific domains.

Indexed as

data‐driven experimentationdesign of experimentsDoEexperimental design softwareopen‐access tools

Identifiers

PMID41566829

DOI10.1002/smtd.202501779

PMCPMC12929928

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.