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Record W4284958100 · doi:10.3390/app12146920

Design and Analysis: Servo-Tube-Powered Liquid Jet Injector for Drug Delivery Applications

2022· article· en· W4284958100 on OpenAlexaff
Rocco Portaro, Hoi Dick Ng

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsConcordia University
Fundersnot available
KeywordsInjectorPlungerAccelerationActuatorMechanical engineeringMaterials scienceEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The current state of commercially available needle-free liquid jet injectors for drug delivery offers no way of controlling the output pressure of the device in real time, as the driving mechanism for these injectors provides a fixed delivery pressure profile. In order to improve the delivery efficiency as well as the precision of the targeted tissue depth, it is necessary to develop a power source that can accurately control the plunger velocity. The duration of a liquid jet injection can vary from 10 to 100 ms, and it generate acceleration greater than 2 g (where g is the gravity); thus, a platform for real-time control must exhibit a response time greater than 1 kHz and good accuracy. Improving the pioneering work by Taberner and others whereby a Lorentz force actuator based upon a voice coil is designed, this study presents a prototype injector system with greater controllability based on the use of a fully closed-loop control system and a classical three-phase linear motor consisting of three fixed coils and multiple permanent magnets. Apart from being capable of generating jets with a required stagnation pressure of 15–16 MPa for skin penetration and liquid injection, as well as reproducing typical injection dynamics using commercially available injectors, the novelty of this proposed platform is that it is proven to be capable of shaping the real-time jet injection pressure profile, including pulsed injection, so that it can later be tailored for more efficient drug delivery.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.401
Teacher spread0.305 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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