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Record W4414440553 · doi:10.1016/j.ohx.2025.e00702

Open source inert gas glove box

2025· article· en· W4414440553 on OpenAlexafffund
Mottaghi Maryam, M. Pearce Joshua

Bibliographic record

VenueHardwareX · 2025
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGloveboxInert gasInertOpen sourceEnclosureVolume (thermodynamics)Open source hardware

Abstract

fetched live from OpenAlex

A glove box is a controlled environment used for a wide range of scientific experiments. While glove boxes provide significant advantages, their high economic costs ranging from over $1,000 to over $15,000 limits their accessibility in under-resourced labs. There are lower-cost DIY designs available on the internet, but they have not been well characterized nor validated. To overcome these limitations, in this study, an open source glove box design is developed for scientific applications using readily available components and digital distributed manufacturing using open-source RepRap-class 3D printers. The ability of the glove box to hold an inert atmosphere is quantified using an oxygen analyzer. The open source glove box can be customized to the dimensions of the user and the volume of the experiment. The design also enables the use of customizable transfer chambers that can be adjusted based on the scientific application. The open source glove box is built from a low-cost enclosure while preventing contamination. The highly portable device can reduce oxygen down to 19 ppm using an inert gas. The economic savings of the validated device compared to proprietary systems is over 95 %.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0340.019

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.257
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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