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Record W4308272194 · doi:10.21203/rs.3.rs-2208344/v1

A nasal spray vaccination device based on Laval nozzle and its experimental test

2022· preprint· en· W4308272194 on OpenAlexaboutno aff
Zhong Wang, Zhengyuan Zhang, Qian Wang, Lingliao Zeng, Jian Jin

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsNasal sprayNozzleSpray nozzleVaccinationMaterials scienceSpray dryingSpray characteristicsNasal administrationBiomedical engineeringParticle (ecology)Composite materialMedicineMechanical engineeringChemistryChromatographyEngineeringImmunology

Abstract

fetched live from OpenAlex

Abstract In order to realize the application of the nasal spray vaccination in the prevention and protection of respiratory infectious diseases, a nasal spray vaccination device is designed in this paper. The device uses a Laval nozzle structure to form a high-speed airflow to impinge on the vaccine reagent and form nebulized particles. Through optimization of the Laval nozzle structure and several experiments, a set of parameters which is applicable to actual nasal spray vaccination is obtained. The experimental results show that when the gas source pressure is 2 bar, the spray angle is about 15°, the diameter of the sprayed particles Xv50 is about 16 um, the volume fraction of particles with diameter larger than 10um is about 75%, the spraying rate is close to 300 ul/s. The vaccine activity tests demonstrate that under these conditions, not only the Active pharmaceutical ingredient (API) of the vaccine is guaranteed, but also the requirements of the spray particle diameter and spray rate for nasal spray inoculation are met.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0010.000
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 head0.080
GPT teacher head0.425
Teacher spread0.345 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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