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Record W2766368464 · doi:10.1021/acs.analchem.7b02490

3D-Printed Paper Spray Ionization Cartridge with Integrated Desolvation Feature and Ion Optics

2017· article· en· W2766368464 on OpenAlexaff
Gert IJ. Salentijn, Richard D. Oleschuk, Elisabeth Verpoorte

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsQueen's University
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsMiniaturizationCartridgeChemistryLens (geology)Body orificeNanotechnologyAnalytical Chemistry (journal)OpticsChromatographyMaterials scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

In this work we present the application of 3D-printing for the miniaturization and functionalization of an ion source for (portable) mass spectrometry (MS). Two versions of a 3D-printed cartridge for paper spray ionization (PSI) are demonstrated, assessed, and compared. We first focus on the use of 3D-printing to enable the integration of an embedded electrostatic lens and a manifold for internal sheath gas distribution and delivery. Cartridges with and without a sheath gas manifold and an electrostatic lens are compared with respect to analytical performance and operational flexibility. The sensitivity and limit of detection are improved in the cartridge with an electrostatic lens and sheath gas manifold compared to the cartridge without (15% and over 6.5× smaller, respectively). The use of these focusing elements also improved the average spray stability. Furthermore, the range of potentials required for PSI was lower, and the distance to the MS orifice over which spray could be obtained was larger. Importantly, both setups allowed quantification of a model drug in the ng/mL range with single-stage MS, after correction for spray instability. Finally, we believe that this work is an example of the impact that 3D-printing will have on the future of analytical device fabrication, miniaturization, and functionalization.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.255
Teacher spread0.244 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations33
Published2017
Admission routes1
Has abstractyes

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